By Kerri Lu

MIT departments, labs, and centers participated in the #ShutDownSTEM, #ShutDownAcademia, and #Strike4BlackLives movements

#ShutDownSTEM, #ShutDownAcademia, and #Strike4BlackLives urged non-black researchers to strike as a form of protesting systemic anti-black racism in academia. The strike was intended for researchers not directly involved in COVID-19 research, according to the Shut Down STEM website.

The strike took place in the wake of the May 25 killing of George Floyd by a white police officer and the ensuing protests across the U.S.

Michael Sipser, dean of the School of Science, wrote in an email to The Tech that individual departments, labs, and centers organized their own activities for #ShutDownSTEM. 

According to the discussion schedule attached to Sipser’s email, the biology and chemistry departments held discussions on topics such as “white privilege, fragility, and guilt”; “personal implicit bias”; “non-Black LGBTQIA+ allyship to the Black community”; “how pseudoscience can perpetuate racism”; “anti-blackness in the Cambridge/Boston community”; “anti-blackness in STEM”; and confronting racist family members.

The math department and the earth, atmospheric, and planetary sciences department separately held department-wide readings and discussions on anti-black racism in STEM. 

The brain and cognitive sciences (BCS) department also participated in #ShutDownSTEM and held a town hall June 8. BCS leadership wrote in an email to the BCS community June 9 that the department plans to expand its Diversity Committee and raise funds to support “anti-racist initiatives” suggested by black graduate students, “build relationships with Historically Black Colleges and other minority serving institutions,” and expand local outreach efforts.

The physics department held no meetings but encouraged researchers to “spend a day on conversation, reflection, and education on racism against African Americans,” Sipser wrote, adding that the Kavli Institute for Astrophysics and Space Research hosted a town hall. 

Physics professors Tracy Slatyer and Daniel Harlow helped organize the Strike For Black Lives through Particles for Justice, a worldwide group of physics faculty and postdocs formed in 2018 in response to a misogynistic talk given at CERN.

Harlow wrote in an email to The Tech that the strike, led by physicists Brian Nord and Chanda Prescod-Weinstein, was developed in collaboration with the organizers of #ShutDownSTEM. Harlow wrote that he hopes the strike will mark “the beginning of a new period in academia,” inspiring researchers “to confront our own complicity in systemic racism and to think productively about how we can improve in the future.”

Slatyer wrote in an email to The Tech that as of June 9, over 4,000 people had signed the pledge to strike. Slatyer chaired a committee in the MIT Center for Theoretical Physics to “organize education and action-planning events” for the strike.

Slatyer acknowledged the “real risk that non-Black academics might congratulate themselves on dedicating a day to the strike, and then slip back into complacency and inaction.” However, Slatyer wrote that many non-black academics will be “exposed to ideas and information they haven't previously encountered,” and the strike will “reinforce” previous efforts by black academics to challenge racism in STEM academia.

“It's also important for both Black and non-Black students to see senior people and leading institutions in our field taking the issue of systemic anti-Blackness seriously enough to dedicate real time to working against it,” Slatyer wrote.

Anantha Chandrakasan, dean of the School of Engineering, wrote in an email to The Tech that engineering departments, labs, and centers paused research and organized “programming, discussion, and resources around eradicating anti-Black racism in academia and STEM – and beyond.” 

The nuclear science and engineering, mechanical engineering, chemical engineering, civil and environmental engineering, and EECS departments held department-wide discussion forums, Chandakasan wrote. The Institute for Medical Engineering and Science and Harvard-MIT Program in Health Sciences and Technology maintained an open Zoom channel for discussion all day, with a community forum in the afternoon.

Community members were also invited to share their perspectives with departmental leadership. For instance, EECS department head Asu Ozdaglar PhD ’03 wrote in an email to the EECS community that participation in the EECS “Listen-In” would help EECS leadership “support structural change” to foster a “more welcoming” and “inclusive” community. Over 330 participants attended the EECS Listen-In.

Melissa Nobles, dean of the School of Humanities, Arts, and Social Sciences (SHASS), wrote in a statement emailed to The Tech that some HASS departments are “posting statements of solidarity on department webpages and social media, while others are meeting to discuss class curriculums and action.” SHASS will also publish a webpage with readings on “systemic racism, civil rights, and social justice,” Nobles wrote.

Nobles wrote that SHASS participated in #ShutDownAcademia to “stop business as usual and plan action on issues of racial inequality.”

The Sloan School of Management also participated in #ShutDownAcademia. An email from Sloan leadership encouraged the Sloan community to use the strike day to watch the Black Business Students Association’s June 1 call to action, watch the June 2 MIT Community Vigil, or continue educating themselves on “on allyship, antiracism, and privilege/white fragility.”

“We encourage managers to consider canceling meetings” to “allow for reflective time for themselves and staff,” the email wrote.

According to the email, Sloan will also participate in the Virtual Day of Absence June 19 by holding discussions on allyship and dismantling systemic racism. The Virtual Day of Absence website writes that participants will protest anti-black racism by being “absent from electronic communication platforms,” including email, on Juneteenth, a holiday celebrating the emancipation of enslaved African Americans.

Orignal article published in The Tech on June 11, 2020

Please visit web.mit.edu/covid19/ for the latest MIT updates.

Note: This EECS page, created in tandem with MIT's coronavirus response, is updated as new information becomes available. Most recent update: June 17, 2020.

Note: Following MIT’s recommendation, EECS staff will be working remotely until further notice. Please consult the EECS staff directory to reach us by email or telephone.

Dear EECS Community:

EECS leadership is tracking the coronavirus disease (COVID-19) outbreak carefully with frequent discussions. We are in close contact with Institute leadership and our medical professionals, and we are monitoring the decisions made by our peer universities. Our goal is to protect the health and well-being of our community without unduly disrupting the education, research, and work of our students, faculty, and staff. 

Given the constantly evolving situation, we will be providing regular updates on our policies and guidelines on this website.

CAMPUS ACCESS:

MIT Phase 1 research ramp-up started on Monday, June 15, at 8a.m. Please see message dated June 14, 2020.  Please follow guidelines mentioned in the "FAQ on returning to work on campus"   *NEW*

MIT implemented a Limited Access Plan (LAP) for all MIT Cambridge campus buildings effective Wednesday, March 25, at 8 a.m. These changes to building access are consistent with Governor Charlie Baker’s stay-at-home advisory issued March 23. Please see messages dated March 22, March 23 and March 24 in covid19.mit.edu.

EDUCATION UPDATES:

• Classes: All EECS classes are canceled for Friday, March 13, 2020, and for the week of March 16. We are preparing to run online-only after MIT spring break ends on March 30. You can find emails about this from our education officers to EECS Instructors here.

• Payments: We will continue to support all EECS department TA appointments throughout the Spring 2020 term. We are also encouraging our instructors to continue to work with LAs and graders to support the courses.

• Pre-Registration, Summer Grading, Summer UROP, Surveys of Students and Subjects, & Scenarios for the Fall: Please go here for updates  

MEETINGS AND EVENTS:

• Faculty lunches: We will now host faculty meetings via Zoom. Faculty members have received an email from the department leadership with Zoom-related information. 

• All meetings and events: All MIT business should be done remotely, unless there is no other alternative. Public health guidelines emphasize that minimizing person-to-person interactions is critical to limiting the spread of the virus. To be clear, any MIT meeting — research, academic, or administrative — that can be done remotely must be done remotely.

FACULTY SEARCH:

• Interviews: Effective Wednesday, March 11, we are moving to virtualized faculty candidate interviews for all upcoming searches through the rest of the season. Our goals with this change are to eliminate the pressure for candidates to travel, and to eliminate in-person meetings and seminars.

• Visit Schedules: We will keep the candidate visit schedules as-is, but move one-on-one meetings to Skype, and over the next day or two develop a procedure for video-casting the seminars. Dinners will be canceled and will be replaced with an informal discussion session. Junior faculty lunches and graduate student lunches will be turned into Skype lunch-time meetings. 

Sincerely,

EECS Leadership Team

MIT UPDATES:

• MIT COVID-19 Updates

• MIT Medical COVID-19 Information and FAQs

• MIT Medical Pandemic Response

• Event Cancellations  (list updated regularly)

MIT INFORMATION FOR GRADUATE STUDENTS:

• MIT Vice-Chancellor's March 13 Letter to Graduate Students, Advisors, and Supervisors

• FAQ for Graduate Students

INFORMATION FOR UNDERGRADUATES:

• Summer Student Options & Travel 

• MIT Chancellor's March 12 Letter on Class Cancellation and Undergrad Move-Out

• FAQ for Undergraduates

• FAQs for Student Access to Campus Facilities and UROP Update

• Travel and Storage Support Options for March 2020

• For additional undergraduate housing, housing exceptions, and other Institute-wide info, please visit the main MIT COVID-19 website.

INFORMATION FOR MIT EMPLOYEES:

• MIT HR March 12 Letter on Working from Home

• MIT Medical and MIT BCBS Update

• MIT Staff Emergency Hardship fund

INFORMATION ON ACADEMICS:

• Faculty Governance: Emergency Academic Regulations in Effect

INFORMATION ON RESEARCH:

• Phase 1 research ramp-up go-live information  *NEW*
• Announcement on Scaling Back on Campus Research

MIT PRESIDENT'S ANNOUNCEMENTS:

• MIT President's June 17 Letter: "Initial decisions about fall at MIT"   *NEW*
• MIT President's May 5 Letter: "Planning for Summer and fall" 

• MIT President's April 13 Letter: "MIT's financial picture and next steps"

• MIT President's March 15 Letter: "Thank You"

• MIT President's March 10 Letter on Coronavirus

• MIT President's March 5 Letter on Coronavirus

CONTACT INFORMATION:
Following are email addresses for COVID related questions:

• Educational continuity: ed-continuity@mit.edu

• Events exception requests/concerns: COVIDevents@mit.edu

• HR support for MIT employees: covid-hr@mit.edu

• Student support: covid19-support@mit.edu

• Travel cancellations and expensing: travelsupport@mit.edu

• Travel/events/visitor policies: em-staff@mit.edu

• Travel exception requests/concerns (provost/chancellor): COVIDtravel@mit.edu

OTHER INFORMATION:

• U.S. Centers for Disease Control and Prevention (CDC)

• Massachusetts Department of Public Health

EECS CANCELLATIONS

The following spring 2020 EECS events have been canceled due to the COVID-19 outbreak:

• SuperUROP Showcase poster session (originally scheduled for April 23).

• Masterworks thesis-research poster session (originally scheduled for April 23).

• SuperUROP Closing Reception (originally scheduled for May 7).

Send updates to eecs-communications@mit.edu.

COURTESY PHOTO

By Alice Dragoon

Jim Bales, PhD ’91, is a firm believer that electronics can be fun. So while most introductory electronics courses focus on analysis, Bales, the associate director of the Edgerton Center, designed the elective seminar 6.070 (Electronics Project Lab) to give students a chance to dive right into making circuits—and develop an intuition for what they do.

When he learned that the second half of the semester would be remote, Bales knew he had to improvise to keep his students engaged. While parts like resistors and integrated circuits were cheap, he couldn’t send each of the 11 students in the class home with the $800 worth of equipment they’d normally be using to test their circuits in the lab. He hunted online and found a testing device less than three inches square that plugs into a computer’s USB port—and whose wires can be attached to a circuit in various combinations, making it act as an oscilloscope, function generator, power supply, or voltmeter. Professor Karl Berggren, the Course 6 undergraduate laboratory officer, saw the potential for using the devices in future classes and agreed to fund the purchase (at about $180 each) if Bales would write up a report on how it worked to use the devices for the class.

Jim Bales turned his kitchen into an assembly line, enlisting his wife and daughter to help create 13 electronics kits, each containing 216 resistors, 54 capacitors, 12 ICs, 20 transistors, 19 diodes, and 5 miscellaneous parts.

COURTESY PHOTO

Bales then put in a flurry of orders for resistors, capacitors, ICs, transistors, diodes, and other miscellaneous parts required for building circuits, along with plastic bins to hold them. He and his wife and teenage daughter spent the weekend of March 14 and 15 sorting and packing all 4,238 components into kits that he FedExed to the students, asking them to return the USB test device at the end of the semester. (The rest of the kit is theirs to keep.)

“With the kit, I was able to build circuits with alternating currents, experiment with components, and get the same learnings as I did in the classroom,” says Kevin Yu, a master’s student in the Integrated Design & Management program, who received his kit at his apartment in Central Square. “It is almost like having a piece of the lab at home.”

Orignial article published in Technology Review on June 15, 2020

Muriel Médard, Cecil H. Green Professor, received an honorary doctorate from the Technological University of Munich (TUM) and was elected to the National Academy of Engineering. Photo: Uli Benz/TUM.

EECS Staff

EECS professors are frequently recognized for excellence in teaching, research, service, and other areas. Following is an ongoing list of awards, prizes, medals, fellowships, memberships, grants, and other honors received by EECS faculty beginning in September 2019.

Hal Abelson, Class of 1922 Professor, received an Honorary Doctorate in Education from the Education University of Hong Kong in November 2019.

Mohammad Alizadeh, Associate Professor of EECS, received VMware's early career Systems Research Award in December 2019.

Saman Amarasinghe, Professor of Computer Science and Engineering, was named as a Fellow of the Association for Computing Machinery in December 2019.

Dimitri Antoniadis, Ray and Maria Stata Professor of Electrical Engineering Emeritus, was inducted into the American Academy of Arts and Sciences in October 2019.

Hari Balakrishnan, Fujitsu Professor of Computer Science, was named an IEEE Fellow in December 2019.

Sangeeta Bhatia, John J. and Dorothy Wilson Professor of Biochemistry and professor of EECS, was elected as a member of the National Academy of Medicine in October 2019.

Guy Bresler, Associate Professor of EECS, received a National Science Foundation (NSF) CAREER Award for his research project titled "Reducibility among high-dimensional statistics problems: information preserving mappings, algorithms, and complexity.”

Michael Carbin, Assistant Professor of EECS, received a Sloan Research Fellowship in computer science in Febuary 2020 and Frank E. Perkins Award for Excellence in Graduate Advising from MIT in May 2020,

Vincent W. S. Chan, Joan and Irwin Jacobs Professor of EECS, will serve as president of the IEEE Communication Society for 2020-2021.

Anantha Chandrakasan, Dean of the School of the Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science, was inducted into to the American Academy of Arts and Sciences in October 2019.

Adam Chlipala, Associate Professor of Computer Science, was named as a Distinguished Member by the Association for Computing Machinery (ACM), in recognition of "outstanding scientific contributions to computing," in October 2019.

Jesus del Alamo, Donner Professor of Science, was named as a recipient of University Researcher Award from the Semiconductor Industry Association and the Semiconductor Research Corporation in October 2019. He also received IPRM award 2020"for key technical contributions to InGaAs-based transistors for high frequency and CMOS applications, and pioneering studies on GaN transistor reliability"in May 2020.

Joel Emer, Professor of the Practice, was elected to the National Academy of Engineering in February 2020 "for quantitative analysis of computer architecture and its application to architectural innovation in commercial microprocessors."

William Freeman, Thomas and Gerd Perkins Professor of EECS, (Post-Tenure), was named a fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in December 2019, with a ceremony scheduled for February 2020. He also received a Distinguished Researcher Award from the IEEE Computer Society's Technical Committee on Pattern Analysis and Machine Intelligence (PAMI) and was among the researchers honored with the 2020 Breakthrough Prize in Fundamental Physics, presented by the Breakthrough Prize Foundation in recognition of the Event Horizon Telescope Collaboration, both in in October 2019.

Robert Gallager, EECS Professor Emeritus, was named as a Japan Prize Laureate for “pioneering contributions to information and coding theory" in February 2020. Due to the coronavirus outbreak, the Japan Prize Foundation postponed the award ceremony in Tokyo until April 2021.

Ruonan Han, Associate Professor of EECS, was named as a Distinguished Lecturer for 2020-2022 by the IEEE Microwave Theory & Technique Society in September 2019.

Song Han, Robert J. Shillman (1974) Career Development Assistant Professor of EECS, received a National Science Foundation (NSF) CAREER Awardfor his research project titled "Efficient Algorithms and Hardware for Accelerated Machine Learning" in February 2020.

Leslie Kolodziejski, Professor of EECS, received Frank E. Perkins Award for Excellence in Graduate Advising from MIT in May 2020.

Charles Leiserson, Edwin Sibley Webster Professor of Electrical Engineering and Professor of Computer Scence and Engineering, received the Test of Time Award for 1999 from the IEEE Symposium on Foundations of Computer Science (FOCS) for the paper "Cache-Oblivious Algorithms" in November 2019. His former students Matteo Frigo, Harald Prokop, and Sridhar Ramachandran were also recognized.

Jae S. Lim, Professor of Electrical Engineering, was named a Ho-Am Foundation Laureate in April 2020. The foundation recognized Professor Lim as "a world-renowned expert in the field of digital signal processing who developed digital video conversion technology that became an international digital TV standard."

Nancy Lynch, NEC Professor of Software Science and Engineering, received a Doctor honoris causa (an honorary doctorate) from Sorbonne University in September 2019. She also gave a keynote speech at the International Conference on Distributed Computer Systems (ICDCS) in July 2019 and a Distinguished Lecture at the Arizona State University Ira A. Fulton Schools of Engineering in October 2019.

Muriel Médard Cecil H. Green Professor of EECS, received an Honorary Doctorate from the Technische Universität München (the Technological University of Munich) in January 2020. She was also elected to the National Academy of Engineering in February 2020 "for contributions to the theory and practice of network coding."

Stephanie Mueller, Assistant Professor of EECS, received a Sloan Research Fellowship in computer science in February 2020 and a Microsoft Research Faculty Fellowship, a two-year appointment that recognizes "innovative, promising early-career professors...who are exploring breakthrough, high-impact research," in April 2020. She also received the 2019 Best Paper Award and the 2019 Best Talk Award from the ACM Symposium on User Interface Software and Technology (UIST).

Yury Polyanskiy, Associate Professor of EECS, received the IEEE Information Theory Society’s 2020 James L. Massey Award. The award recognizes outstanding achievement in research and teaching by young scholars in the Information Theory community in June 2020.

Ronitt Rubinfeld, Edwin Sibley Webster Professor, was elected to the American Academy of Arts and Sciences for 2020.

Arvind Satyanarayan, NBX Career Development Assistant Professor of EECS, received a National Science Foundation (NSF) CAREER Award for his research project titled "Effective Interaction Design for Data Visualization.”

Jeffrey H. Shapiro, Julius A. Stratton Professor of Electrical Engineering (Post-Tenure), was announced as a winner of the IEEE Signal Processing Society Best Paper Award for the paper titled "Photon-Efficient Computational 3-D and Reflectivity Imaging With Single-Photon Detectors" in December 2019, with the award to be presented in May 2020. His former students Dongeek Shin and Ahmed Kirmani and former EECS faculty member Vivek Goyal (now at Boston University) were also recognized.

Collin Stultz, Professor in Biomedical Engineering, was named a Fellow of the American Institute for Medical and Biological Engineering (AIMBE) in March 2019.

Vivienne Sze, Associate Professor of EECS, was named as the recipient of the inaugural ACM-W Rising Star Award in January 2020. The award, which "recognizes a woman whose early-career research has had a significant impact on the computing discipline," will be presented in June 2020.

Jacob White, Cecil H. Green Professor of Electrical Engineering and Computer Science, was named as a 2020 Margaret MacVicar Faculty Fellow.The 10-year fellowships are MIT's highest honor in undergraduate teaching.

Updated June 29, 2020. Send additions or changes to eecs-awards@mit.edu. For a list of awards from the 2018-2019 academic year, please see this earlier roundup.

The startup macro-eyes is bringing new techniques in machine learning and artificial intelligence to global health problems like vaccine delivery and patient scheduling with its Connected Health AI Network (CHAIN).

Photo courtesy of macro-eyes

Zach Winn | MIT News Office

More children are being vaccinated around the world today than ever before, and the prevalence of many vaccine-preventable diseases has dropped over the last decade. Despite these encouraging signs, however, the availability of essential vaccines has stagnated globally in recent years, according the World Health Organization.

One problem, particularly in low-resource settings, is the difficulty of predicting how many children will show up for vaccinations at each health clinic. This leads to vaccine shortages, leaving children without critical immunizations, or to surpluses that can’t be used.

The startup macro-eyes is seeking to solve that problem with a vaccine forecasting tool that leverages a unique combination of real-time data sources, including new insights from front-line health workers. The company says the tool, named the Connected Health AI Network (CHAIN), was able to reduce vaccine wastage by 96 percent across three regions of Tanzania. Now it is working to scale that success across Tanzania and Mozambique.

“Health care is complex, and to be invited to the table, you need to deal with missing data,” says macro-eyes Chief Executive Officer Benjamin Fels, who co-founded the company with Suvrit Sra, the Esther and Harold E. Edgerton Career Development Associate Professor at MIT. “If your system needs age, gender, and weight to make predictions, but for one population you don’t have weight or age, you can’t just say, ‘This system doesn’t work.’ Our feeling is it has to be able to work in any setting.”

The company’s approach to prediction is already the basis for another product, the patient scheduling platform Sibyl, which has analyzed over 6 million hospital appointments and reduced wait times by more than 75 percent at one of the largest heart hospitals in the U.S. Sibyl’s predictions work as part of CHAIN’s broader forecasts.

Both products represent steps toward macro-eyes’ larger goal of transforming health care through artificial intelligence. And by getting their solutions to work in the regions with the least amount of data, they’re also advancing the field of AI.

“The state of the art in machine learning will result from confronting fundamental challenges in the most difficult environments in the world,” Fels says. “Engage where the problems are hardest, and AI too will benefit: [It will become] smarter, faster, cheaper, and more resilient.”

macro-eyes is working to scale their model’s predictive success across all of Tanzania and Mozambique. This photo shows frontline health workers in Mozambique. “macro-eyes produced mirrors for the health facilities to remind frontline health workers that they are the experts/'Especialistas,'” Fels says.

Photo courtesy of macro-eyes

Defining an approach

Sra and Fels first met about 10 years ago when Fels was working as an algorithmic trader for a hedge fund and Sra was a visiting faculty member at the University of California at Berkeley. The pair’s experience crunching numbers in different industries alerted them to a shortcoming in health care.

“A question that became an obsession to me was, ‘Why were financial markets almost entirely determined by machines — by algorithms — and health care the world over is probably the least algorithmic part of anybody’s life?’” Fels recalls. “Why is health care not more data-driven?”

Around 2013, the co-founders began building machine-learning algorithms that measured similarities between patients to better inform treatment plans at Stanford School of Medicine and another large academic medical center in New York. It was during that early work that the founders laid the foundation of the company’s approach.

“There are themes we established at Stanford that remain today,” Fels says. “One is [building systems with] humans in the loop: We’re not just learning from the data, we’re also learning from the experts. The other is multidimensionality. We’re not just looking at one type of data; we’re looking at 10 or 15 types, [including] images, time series, information about medication, dosage, financial information, how much it costs the patient or hospital.”

Around the time the founders began working with Stanford, Sra joined MIT’s Laboratory for Information and Decision Systems (LIDS) as a principal research scientist. He would go on to become a faculty member in the Department of Electrical Engineering and Computer Science and MIT’s Institute for Data, Systems, and Society (IDSS). The mission of IDSS, to advance fields including data science and to use those advances to improve society, aligned well with Sra’s mission at macro-eyes.

“Because of that focus [on impact] within IDSS, I find it my focus to try to do AI for social good,’ Sra says. “The true judgment of success is how many people did we help? How could we improve access to care for people, wherever they may be?”

In 2017, macro-eyes received a small grant from the Bill and Melinda Gates Foundation to explore the possibility of using data from front-line health workers to build a predictive supply chain for vaccines. It was the beginning of a relationship with the Gates Foundation that has steadily expanded as the company has reached new milestones, from building accurate vaccine utilization models in Tanzania and Mozambique to integrating with supply chains to make vaccine supplies more proactive. To help with the latter mission, Prashant Yadav recently joined the board of directors; Yadav worked as a professor of supply chain management with the MIT-Zaragoza International Logistics Program for seven years and is now a senior fellow at the Center for Global Development, a nonprofit thinktank.

In conjunction with their work on CHAIN, the company has deployed another product, Sibyl, which uses machine learning to determine when patients are most likely to show up for appointments, to help front-desk workers at health clinics build schedules. Fels says the system has allowed hospitals to improve the efficiency of their operations so much they’ve reduced the average time patients wait to see a doctor from 55 days to 13 days.

As a part of CHAIN, Sibyl similarly uses a range of data points to optimize schedules, allowing it to accurately predict behavior in environments where other machine learning models might struggle.

The founders are also exploring ways to apply that approach to help direct Covid-19 patients to health clinics with sufficient capacity. That work is being developed with Sierra Leone Chief Innovation Officer David Sengeh SM ’12 PhD ’16.

Pushing frontiers

Building solutions for some of the most underdeveloped health care systems in the world might seem like a difficult way for a young company to establish itself, but the approach is an extension of macro-eyes’ founding mission of building health care solutions that can benefit people around the world equally.

“As an organization, we can never assume data will be waiting for us,” Fels says. “We’ve learned that we need to think strategically and be thoughtful about how to access or generate the data we need to fulfill our mandate: Make the delivery of health care predictive, everywhere.”

The approach is also a good way to explore innovations in mathematical fields the founders have spent their careers working in.

“Necessity is absolutely the mother of invention,” Sra says. “This is innovation driven by need.”

And going forward, the company’s work in difficult environments should only make scaling easier.

“We think every day about how to make our technology more rapidly deployable, more generalizable, more highly scalable,” Sra says. “How do we get to the immense power of bringing true machine learning to the world’s most important problems without first spending decades and billions of dollars in building digital infrastructure? How do we leap into the future?”

Original article published on the MIT News website on June 25, 2020.

Professor Yury Polyanskiy

It is our great pleasure to share that LIDS professor Yury Polyanskiy received the IEEE Information Theory Society’s 2020 James L. Massey Award. The award recognizes outstanding achievement in research and teaching by young scholars in the Information Theory community.

Yury is an Associate Professor of Electrical Engineering and Computer Science. His research currently focuses on basic questions in information theory (especially in connection with statistics and machine learning), error-correcting codes, wireless communication and fault-tolerant and defect-tolerant circuits.

Read more about Yury and his work HERE.

Read more about the Massey Award HERE.

Original article published on the Laboratory for Information and Deciision Systems Website on June 29, 2020.

This graphic depicts a stylized rendering of the quantum photonic chip and its assembly process. The bottom half of the image shows a functioning quantum micro-chiplet (QMC), which emits single-photon pulses that are routed and manipulated on a photonic integrated circuit (PIC). The top half of the image shows how this chip is made: Diamond QMCs are fabricated separately and then transferred into the PIC.

Credit: Noel H Wan

Original article appeared in the MIT news on July 8, 2020.

A new challenge launched by MIT App Inventor — a web-based, visual-programming environment that allows children to develop applications for smartphones and tablets — encourages kids and adults to build mobile technologies that could be used to help stem the spread of Covid-19, aid local communities, and provide moral support to people around the world. This image includes four screenshots from apps submitted to the site that were made by participants.

Image from MIT App Inventor website and edited by MIT News.

Abby Abazorius | MIT News Office

When schools around the world closed their doors due to the coronavirus pandemic, the team behind MIT App Inventor — a web-based, visual-programming environment that allows children to develop applications for smartphones and tablets — began thinking about how they could not only help keep children engaged and learning, but also empower them to create new tools to address the pandemic.

In April, the App Inventor team launched a new challenge that encourages children and adults around the world to build mobile technologies that could be used to help stem the spread of Covid-19, aid local communities, and provide moral support to people around the world.

“Many people, including kids, are locked down at home with little to do and with a sense of loss of control over their lives,” says Selim Tezel, a curriculum developer for MIT App Inventor. “We wanted to empower them to take action, be involved in a creative process, and do something good for their fellow citizens.”

Since the Coronavirus App Inventor Challenge launched this spring, there have been submissions from inventors ranging in age from 9 to 72 years and from coders around the globe, including New Zealand, the Democratic Republic of Congo, Italy, China, India, and Spain. While the App Inventor platform has historically been used in classrooms as an educational tool, Tezel and Hal Abelson, the Class of 1922 Professor in the Department of Electrical Engineering in Computer Science, explain that they have seen increased individual engagement with the platform during the pandemic, particularly on a global scale.

“The nice thing about App Inventor is that you’re learning about coding, but it also gives you something that you can actually do and a chance to contribute,” says Abelson. “It provides kids with an opportunity to say, ‘I’m not just learning, I’m doing a project, and it’s not only a project for me, it’s a project that can actually help other people.’ I think that can be very powerful.”

Winners are announced on a monthly basis and honor apps for creativity, design, and overall inventiveness. Challenge participants have addressed a wide variety of issues associated with the pandemic, from health and hygiene to mental health and education. For example, April’s Young Inventors of the Month, Bethany Chow and Ice Chow from Hong Kong, developed an app aimed at motivating users to stay healthy. Their app features a game that encourages players to adapt healthy habits by collecting points that they can use to defeat virtual viruses, as well as an optional location tracker function that can alert users if they have frequented a location that has a Covid-19 outbreak.

Akshaj Singhal, a 11-year-old from India, was selected as the June Inventor of the Month in the Young Inventors category, which includes children 12 years old and younger, for his app called Covid-19 Warrior. The app offers a host of features aimed at spreading awareness of Covid-19, including a game and quiz to test a user’s knowledge of the virus, as well as local daily Covid-19 news updates and information on how to make your own mask.

The challenge has attracted participants with varying levels of technical expertise, allowing aspiring coders a chance to hone and improve their skills. Prayanshi Garg, a 12-year-old from India, created her first app for the challenge, an educational quiz aimed at increasing awareness of Covid-19. Vansh Reshamwala, a 10-year-old from India, created an app that features a recording of his voice sharing information about ways to help prevent the spread of Covid-19 and thanking heroes for their efforts during the pandemic.

Participants have also been able to come together virtually to develop apps during a time when social interactions and team activities are limited. For example, three high school students from Singapore developed Maskeraid, an app that connects users in need of assistance with volunteers who are able to help with a variety of services.

“The ultimate goal is to engage our very creative App Inventor community of all ages and empower them during this time,” says Tezel. “We also see this time as an incredible opportunity to help people vastly improve their coding skills.  When one is confronted by a tangible challenge, one's skills and versatility can grow to meet the challenge.”

The App Inventor team plans to continue hosting the challenge for so long as the pandemic is having a worldwide impact. Later this month, the App Inventor team will be hosting a virtual hackathon or worldwide “appathon,” an event that will encourage participants to create apps aimed at improving the global good.

“Our global App Inventor community never ceases to amaze us,” says Tezel. “We are delighted by how inventors of all ages have been rising to the challenge of the coronavirus, empowering themselves by putting their coding skills to good use for the well-being of their communities.”

Original article published on the MIT News website on July 9, 2020

Charlene (Nohara) Kabcenell ’79 is president of the MIT Alumni Association. "The Covid-19 crisis has reminded us all of the importance of relationships and staying connected, both to MIT and to each other," she says. "[W]e are such a force when we band together."

Photo: Rebecca Rodriguez

Nicole Estvanik Taylor | MIT Alumni Association

On July 1, Charlene C. (Nohara) Kabcenell ’79 began her one-year term as president of the MIT Alumni Association. She joins President-Select Annalisa L. Weigel ’94, ’95, SM ’00, PhD ’02 and a slate of new directors for 2020-21.

A Hawaii native, Kabcenell has lived in California with her husband, Derry Kabcenell ’75, since she graduated from MIT with degree in electrical engineering and computer science. She began her career at Xerox and retired from Oracle as a vice president of software development. A life member of the MIT Corporation, she has served on various committees for both the Corporation and the Alumni Association. 

Q: What aspect of volunteering for MIT do you find most rewarding?

A: I feel good about being able to connect other alumni back to MIT. When I retired from my career in the late ’90s, my husband and I hadn’t been to campus since our school days. Volunteering for MIT was an opportunity to find out what was going on there. It hit us then — we’d been missing out on so much. And naturally I couldn’t contain myself from telling alumni back home, “Did you know about this great project?” Often, it was something an alum might not know that MIT was involved in, such as addressing global poverty. 

Q: What will you focus on during your year as president?

A: My predecessors did a terrific job refreshing and strengthening the association. My role is to keep the momentum going. The Covid-19 crisis has reminded us all of the importance of relationships and staying connected, both to MIT and to each other. I plan to focus on building and improving that connectivity, whether in person or virtually as we must do during social distancing, because we are such a force when we band together. The way we used to support each other as students, in getting through classes and whatnot — we can do the same now that we’re out in the world.

I also want to take advantage of our shared value of making a better world. “Mind, hand, and heart” has always been part of our MIT DNA, and working together to improve the world around us is a natural fit for our alumni community. Our collective response to the challenges of Covid-19 is evidence of that, and the association can do even more to inspire and facilitate those kinds of efforts.

In many ways, this ties to the work the MITAA Board of Directors has been doing with staff and volunteers to implement key tenets of the strategic plan we created in 2018, with special focus on activating our alumni network in service to the world — as well as to our local communities.

Q: What did the MIT community mean to you as a student?

A: I don’t know who the nice people in the admissions office were who let me in, but I really feel like I was a long shot. I attended a public high school not known for academic excellence, where I was a nerd who just didn’t fit in. I was also a first-generation student. I arrived here knowing that I was not as well prepared as my fellow students. 

Once I arrived at MIT, it was this big revelation: “There are other people like me! We speak the same language.” There were so many people here who shared those common values of love of learning and wanting to discover things and solve problems. In that sense, I think of MIT as my second home, certainly my intellectual home.

Q: Do you have a favorite spot on campus?

A: The first time I walked up the steps to Lobby 7, I thought, “Wow! I can see the wear from generations of feet!” Later I found out that some of the original stones have been replaced, so perhaps it’s not quite generations of wear. But I still get that little thrill, remembering the first time going to class up those steps and saying, “I’m really here.”

This article originally appeared in the July/August 2020 issue of MIT Technology Review.

by Sarah Brown | MIT Sloan School of Management

Big data is a promising investment for firms, but embracing data can also bring confusion and potential minefields — everything from where companies should be spending money to how they should be staffing their data teams.

MIT adjunct professor Michael Stonebraker, a computer scientist, database research pioneer, and Turing award winner, said he sees several things companies should do to build their data enterprises — and just as importantly, mistakes companies should cease or avoid.

In a talk last fall as part of the 2019 MIT Citi Conference, Stonebraker borrowed a page from David Letterman to offer 10 big data blunders he’s seen in the last decade or so. His (sometimes opinionated!) advice comes from discussions with tech and data executives during more than decades in the field as well as his work with several data startups.  

Here’s Stonebraker’s list, including a bonus tip.

Blunder #1: Not moving everything to the cloud

Companies should be moving their data out of the building and into a public cloud, or purchase a private cloud, Stonebraker said. Why? Firms like Amazon offer cloud storage at a fraction of the cost and with better infrastructure, often with tighter security and staff that specialize in cloud management for a living. 

“They're deploying servers by the millions; you're deploying them by the tens of thousands,” Stonebraker said. “They're just way further up the cost curve and are offering huge economies of scale.”

Clouds also offer elasticity — with a cloud, your company can use a thousand servers to run end-of-the-month numbers, and a scaled-back amount for everyday tasks.

Blunder #2: Not planning for artificial intelligence and machine learning to be disruptive

Machine learning is already remaking a variety of industries, Stonebraker said, and it is going to replace some workers. “The odds that it is not disruptive in financial services is zero,” he said.

In light of this, companies should avoid being disrupted and instead be the disruptor. This means paying for AI and machine learning expertise, which is in short supply. “There’s going to be an arms race,” he said of the competition to hire talent. “Get going on it as quick as you can.”

Blunder #3: Not solving your real data science problem

Leaders often feel like they are on top of data science, and things like algorithm development, because they’ve hired data scientists. But data scientists typically spend most of their time analyzing and cleaning data and integrating it with other sources, Stonebraker said

For example, a machine learning expert at iRobot told Stonebraker that she spent 90% of her time working on data discovery, integration, and cleaning. Of the 10% left of her time, she said she spent 90% of that fixing data cleaning errors — which left about 1% of her time to the job she was hired for, Stonebraker said. 

These tasks are important — “without clean data, or clean enough data, your data science is worthless,” he said.

But it’s also important to realize how data scientists are actually spending their time. “They are in the data integration business, and so you might as well admit that that’s what your data scientists do,” he said. The best way to address this, he said, is to have a clear strategy for dealing with data cleaning and integration, and to have a chief data officer on staff.

Blunder #4: Believing that traditional data integration techniques will solve issue #3.

Many err in the belief that traditional solutions will help address data cleaning and integration, Stonebraker said, specifically ETL (extract, transform, load) and master data management processes. The ETL process requires intensive human effort, Stonebraker said, and takes a lot of time and gets too expensive if you have more than 20 data sources. These processes also require a global data model at the outset, while today’s enterprises are agile and evolve quickly. The technology is brittle and not going to scale, he said.

Once you’ve run ETL, you need to match records to find out which ones go together, often using rule systems, which also don’t scale.

As an example, Stonebraker pointed to GE, which wanted to classify 20 million spending transactions. Their staff initially wrote about 500 rules, which took care of classifying only about 10% of their transactions. GE partnered with Tamr, an enterprise data mastering company co-founded by Stonebraker. Tamr built a machine learning model to classify the rest of the 18 million records.

“Machine learning is going to take over in this space,” Stonebraker said. “It’s okay to use rules to generate training data. Don’t try to use it for big problems.”

Blunder #5: Believing data warehouses will solve all your problems

Data warehouses can solve some big data problems — but not all of them. Warehouses don’t work for things like text, images, and video, Stonebraker said. Instead, use data warehouses for what they’re good for: customer-facing, structured data from a few data sources.

Many companies have bought into traditional data warehouse technology that costs up to seven figures a year, Stonebraker said. “Get rid of the high-price spread and just remember, always, that your warehouse is going to move to the cloud,” he said.

Blunder #6: Believing that Hadoop/Spark will solve all your problems.

Many companies have invested in Hadoop, the open-source software collection from Apache, or Spark, the company’s analytics engine for big data processing. They shouldn’t be the answer for everything or everyone, Stonebraker said.

“In my opinion you should be looking at best-of-breed technologies, not the lowest common denominator,” Stonebraker said. This is especially true for high-level functions, or a company’s “secret sauce,” the special elements that are the key to success. “Spark and Hadoop are useless on data integration,” Stonebraker asserted, which is where data scientists spend a lot of time.  

What do you do with your Hadoop/Spark cluster? Companies can repurpose it for a data lake or for data integration, or even throw it away — the lifetime for most hardware is about three years.

Blunder #7: Believing that data lakes will solve all your problems

Conventional wisdom assumes that if a company loads all its data into a data lake — a centralized repository for all data — they’ll be able to correlate all their data sets. But they often end up with data swamps, not data lakes.

Independently constructed data sets are never “plug-compatible,” Stonebraker said. Things like semantics, units, and time granularity might not match: one data set might call something salary, the other wages; one might use Euros, the other dollars; one might have gross salary before taxes, the other are net. Duplicates have to be removed, and spellings might vary from one set to another.

For example, Stonebraker said, human resources databases need to account for employees working in two different locations. If two records are simply added together, staff will be overcounted by the number of duplicates. “The net result is your analytics will be garbage, and your machine learning models will fail. Garbage in, garbage out,” he said

Companies need to clean their lake data with a data curation system that will solve these problems. “This problem has been around since I’ve been an adult and it’s getting easier by applying machine learning and modern techniques,” Stonebraker said (see Blunder #4), but it's still not easy and companies should put their best staff on the problem. “Don’t use your homebrew system,” he said of in-house technology, which is often outdated. Usually the best data curation systems come from startups, he said.

Blunder #8: Outsourcing your new stuff to big data analytics services firms like Palantir, IBM, and Mu Sigma

Typical enterprises spend about 95% of the IT budget on running legacy code, like maintenance, and often have their best people keeping the lights on. “Shiny new stuff gets outsourced, often because there is no appropriate talent internally, or because your best people are stuck keeping your accounts receivable system up,” Stonebraker said. This is a bad outcome — maintenance is boring, he said, and so creative people quit, and companies lose talent that could be working on new things.

New tools shouldn’t be outsourced, Stonebraker said. Other things should, like maintenance, — and while you’re at it, don’t run your own email system, he said.

Blunder #9: Succumbing to the “Innovators Dilemma”

In his classic book “The Innovators Dilemma,” Harvard Business School professor Clayton Christiansen said successful companies know when to abandon legacy systems, even if it means drastic changes or potentially losing customers.

“You have to make all kinds of bets on the future, and a bunch of them are going to require you to give up at least some piece of your current business model and reinvent yourself,” Stonebraker said. “You simply have to be willing to do that in any high tech field.

Blunder #10: Not paying up for a few rocket scientists

To address all of the above issues, companies need to invest in some highly skilled employees, Stonebraker said. Human resources won’t like what you’re paying, and “they’re not going to wear suits,” Stonebraker said, but don’t drive them away. “They will be your guiding lights.”

(Bonus) Blunder #11: Working for a company that is not trying to do something about the “sins of the past.” If you work for a company that's falling into any of the above blunders, then you should be fixing it — or looking for a new job, Stonebraker said.

https://youtu.be/4SK8jdBhGN

Original article published on the MIT Sloan School of Managment website on July 14, 2020

MIT Driverless and TU Delft pose with their race car, DUT19, at Formula Student Germany 2019. The joint team placed third overall.

Photo: Delft Driverless

Zain Humayun | Edgerton Center

On a cloudy day last August, Emily Zhang held her breath at the Hockenheimring racetrack in Germany’s Rhine Valley. Less than two weeks after the circuit hosted the German Grand Prix, it was the stage for Formula Student Germany, one of the world’s most competitive automobile design competitions for students.

Zhang and her teammates from MIT Driverless — working under the auspices of the Edgerton Center — watched anxiously as their race car rolled over to the starting line. A sleek, gleaming blue striped with white, the automobile named DUT18 wasn’t an ordinary race car. First, it was built by engineers at TU Delft; then, programmers at MIT wrote algorithms that would replace its human driver. Together, the two schools were competing for the first time in the competition’s category for autonomous — or self-driving — vehicles.

For Zhang, the competition was the test of a year’s work. A Course 6 (electrical engineering and computer science) senior and member of the Driverless computer vision team, Zhang helped write the race car’s software. Her team’s code would enable the car to attempt the competition’s various challenges, such as steering itself through a circuit it had never seen before. Two cameras mounted on the car would point at cones lining the track, then use geometry to calculate — and maintain — the car’s distance from them. For Zhang, the open-ended nature of the challenge made it an exciting change from her usual classwork. “It’s been really cool and very fun to be working on such an interesting and complex problem,” Zhang says. “And the team is great. Everyone is very passionate about working together on this problem.”

In Germany, Zhang and the other Driverless team members had one goal: getting their car to work. They certainly weren’t expecting to achieve a third-place podium finish. The success, says Driverless business team member and MBA student Krystal Liu, inspired them to aim even higher. “This year, we were planning to do it better,” she says. “This year, we were trying to go for first.”

Liu and the rest of the team would soon encounter a major challenge. After the success in Germany, the team had begun improving their software for the following year’s competition. They were about to start testing it in the Netherlands when the coronavirus pandemic struck.

“When Covid hit, we were unsure of what would happen,” Liu recalls. “Everything changed.” The team soon heard that the 2020 Formula Student Championship had been cancelled — in addition to all the other summer competitions. A while later, the team learned about a new competition being held later this year: The Indy Autonomous Challenge, or IAC, is a $1.5 million prize competition that will be held at the famed Indianapolis Motor Speedway. “It’s like the autonomous student version of the Indy 500,” Liu says.

At Formula Student Germany 2019, MIT Driverless and TU Driverless competed together against teams from all over the world.

Photo: Delft Driverless

The competition is different from the Formula Student Championship in a few ways. First, teams aren’t required to build their own cars, just the autonomous driving software — they use it to maneuver professional race cars. “It’s super high-speeds, up to 120 miles per hour,” Liu says. “So way faster than we were doing before — and still autonomous!” And for Zhang, the competition will pose the Driverless team an engineering problem they didn’t have in Germany. “We’re going to be driving with other cars,” she says. “So that’s going to bring out a lot of really cool problems.”

But even though the Driverless team is ready to take on the challenge of the IAC, participating in the competition requires funding that is difficult to come by without sponsors. Spearheading that effort is Dan Reilly, a dual-degree MBA and mechanical engineering master’s student in the Leaders for Global Operations (LGO) program. Reilly says he joined Driverless to support the team’s sponsorship strategies, and “to make sure the engineers don’t have to worry about this stuff, that they can just do the great work they do.” Over the next few months, he says, he hopes to convince companies to join hands with Driverless.

Every year, almost 40,000 Americans lose their lives in vehicular crashes. More than 90 percent of these are caused by human error, which means that autonomous vehicles could potentially save thousands of lives. Competitions like the Indy Autonomous Challenge aim to play their part by increasing public awareness of the benefits of driverless cars, as well as offering teams like MIT Driverless an opportunity to tackle the most important problems holding driverless technology back.

Liu says her time at Driverless has convinced her that autonomous technology will change the world. “I used to be uncertain about whether it would,” she says. “Now, I’m both certain that it will happen, and confident that it’s the right thing to do.”

Original article published on the MIT News website on July 14, 2020

Charlene (Nohara) Kabcenell ’79 is president of the MIT Alumni Association. "The Covid-19 crisis has reminded us all of the importance of relationships and staying connected, both to MIT and to each other," she says. "[W]e are such a force when we band together."

Photo: Rebecca Rodriguez

Nicole Estvanik Taylor | MIT Alumni Association

On July 1, Charlene C. (Nohara) Kabcenell ’79 began her one-year term as president of the MIT Alumni Association. She joins President-Select Annalisa L. Weigel ’94, ’95, SM ’00, PhD ’02 and a slate of new directors for 2020-21.

A Hawaii native, Kabcenell has lived in California with her husband, Derry Kabcenell ’75, since she graduated from MIT with degree in electrical engineering and computer science. She began her career at Xerox and retired from Oracle as a vice president of software development. A life member of the MIT Corporation, she has served on various committees for both the Corporation and the Alumni Association. 

Q: What aspect of volunteering for MIT do you find most rewarding?

A: I feel good about being able to connect other alumni back to MIT. When I retired from my career in the late ’90s, my husband and I hadn’t been to campus since our school days. Volunteering for MIT was an opportunity to find out what was going on there. It hit us then — we’d been missing out on so much. And naturally I couldn’t contain myself from telling alumni back home, “Did you know about this great project?” Often, it was something an alum might not know that MIT was involved in, such as addressing global poverty. 

Q: What will you focus on during your year as president?

A: My predecessors did a terrific job refreshing and strengthening the association. My role is to keep the momentum going. The Covid-19 crisis has reminded us all of the importance of relationships and staying connected, both to MIT and to each other. I plan to focus on building and improving that connectivity, whether in person or virtually as we must do during social distancing, because we are such a force when we band together. The way we used to support each other as students, in getting through classes and whatnot — we can do the same now that we’re out in the world.

I also want to take advantage of our shared value of making a better world. “Mind, hand, and heart” has always been part of our MIT DNA, and working together to improve the world around us is a natural fit for our alumni community. Our collective response to the challenges of Covid-19 is evidence of that, and the association can do even more to inspire and facilitate those kinds of efforts.

In many ways, this ties to the work the MITAA Board of Directors has been doing with staff and volunteers to implement key tenets of the strategic plan we created in 2018, with special focus on activating our alumni network in service to the world — as well as to our local communities.

Q: What did the MIT community mean to you as a student?

A: I don’t know who the nice people in the admissions office were who let me in, but I really feel like I was a long shot. I attended a public high school not known for academic excellence, where I was a nerd who just didn’t fit in. I was also a first-generation student. I arrived here knowing that I was not as well prepared as my fellow students. 

Once I arrived at MIT, it was this big revelation: “There are other people like me! We speak the same language.” There were so many people here who shared those common values of love of learning and wanting to discover things and solve problems. In that sense, I think of MIT as my second home, certainly my intellectual home.

Q: Do you have a favorite spot on campus?

A: The first time I walked up the steps to Lobby 7, I thought, “Wow! I can see the wear from generations of feet!” Later I found out that some of the original stones have been replaced, so perhaps it’s not quite generations of wear. But I still get that little thrill, remembering the first time going to class up those steps and saying, “I’m really here.”

This article originally appeared in the July/August 2020 issue of MIT Technology Review.

Out of a total of 200 research proposals, 26 projects were selected and awarded $5.4 million to continue AI research to mitigate the impact of Covid-19 in the areas of medicine, urban planning, and public policy.

School of Engineering | MIT Schwarzman College of Computing

Artificial intelligence has the power to help put an end to the Covid-19 pandemic. Not only can techniques of machine learning and natural language processing be used to track and report Covid-19 infection rates, but other AI techniques can also be used to make smarter decisions about everything from when states should reopen to how vaccines are designed. Now, MIT researchers working on seven groundbreaking projects on Covid-19 will be funded to more rapidly develop and apply novel AI techniques to improve medical response and slow the pandemic spread.

Earlier this year, the C3.ai Digital Transformation Institute (C3.ai DTI) formed, with the goal of attracting the world’s leading scientists to join in a coordinated and innovative effort to advance the digital transformation of businesses, governments, and society. The consortium is dedicated to accelerating advances in research and combining machine learning, artificial intelligence, internet of things, ethics, and public policy — for enhancing societal outcomes. MIT, under the auspices of the School of Engineering, joined the C3.ai DTI consortium, along with C3.ai, Microsoft Corporation, the University of Illinois at Urbana-Champaign, the University of California at Berkeley, Princeton University, the University of Chicago, Carnegie Mellon University, and, most recently, Stanford University.

The initial call for project proposals aimed to embrace the challenge of abating the spread of Covid-19 and advance the knowledge, science, and technologies for mitigating the impact of pandemics using AI. Out of a total of 200 research proposals, 26 projects were selected and awarded $5.4 million to continue AI research to mitigate the impact of Covid-19 in the areas of medicine, urban planning, and public policy.

The first round of grant recipients was recently announced, and among them are five projects led by MIT researchers from across the Institute: Saurabh Amin, associate professor of civil and environmental engineering; Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management; Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of the MIT Institute for Data, Systems, and Society; David Gifford, professor of biological engineering and of electrical engineering and computer science; and Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, head of the Department of Electrical Engineering and Computer Science, and deputy dean of academics for MIT Schwarzman College of Computing.

“We are proud to be a part of this consortium, and to collaborate with peers across higher education, industry, and health care to collectively combat the current pandemic, and to mitigate risk associated with future pandemics,” says Anantha P. Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “We are so honored to have the opportunity to accelerate critical Covid-19 research through resources and expertise provided by the C3.ai DTI.”

Additionally, three MIT researchers will collaborate with principal investigators from other institutions on projects blending health and machine learning. Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science, join Ziv Bar-Joseph from Carnegie Mellon University for a project using machine learning to seek treatment for Covid-19. Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science, joins Sendhil Mullainathan of the University of Chicago for a project using machine learning to support emergency triage of pulmonary collapse due to Covid-19 on the basis of X-rays.

Bertsimas’s project develops automated, interpretable, and scalable decision-making systems based on machine learning and artificial intelligence to support clinical practices and public policies as they respond to the Covid-19 pandemic. When it comes to reopening the economy while containing the spread of the pandemic, Ozdaglar’s research provides quantitative analyses of targeted interventions for different groups that will guide policies calibrated to different risk levels and interaction patterns. Amin is investigating the design of actionable information and effective intervention strategies to support safe mobilization of economic activity and reopening of mobility services in urban systems. Dahleh’s research innovatively uses machine learning to determine how to safeguard schools and universities against the outbreak. Gifford was awarded funding for his project that uses machine learning to develop more informed vaccine designs with improved population coverage, and to develop models of Covid-19 disease severity using individual genotypes.

“The enthusiastic support of the distinguished MIT research community is making a huge contribution to the rapid start and significant progress of the C3.ai Digital Transformation Institute,” says Thomas Siebel, chair and CEO of C3.ai. “It is a privilege to be working with such an accomplished team.”

The following projects are the MIT recipients of the inaugural C3.ai DTI Awards: 

"Pandemic Resilient Urban Mobility: Learning Spatiotemporal Models for Testing, Contact Tracing, and Reopening Decisions"— Saurabh Amin, associate professor of civil and environmental engineering; and Patrick Jaillet, the Dugald C. Jackson Professor of Electrical Engineering and Computer Science

"Effective Cocktail Treatments for SARS-CoV-2 Based on Modeling Lung Single Cell Response Data"— Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science (Principal investigator: Ziv Bar-Joseph of Carnegie Mellon University)

"Toward Analytics-Based Clinical and Policy Decision Support to Respond to the Covid-19 Pandemic"— Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management and associate dean for business analytics; and Alexandre Jacquillat, assistant professor of operations research and statistics

"Reinforcement Learning to Safeguard Schools and Universities Against the Covid-19 Outbreak"— Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of MIT Institute for Data, Systems, and Society; and Peko Hosoi, the Neil and Jane Pappalardo Professor of Mechanical Engineering and associate dean of engineering

"Machine Learning-Based Vaccine Design and HLA Based Risk Prediction for Viral Infections"— David Gifford, professor of biological engineering and of electrical engineering and computer science

"Machine Learning Support for Emergency Triage of Pulmonary Collapse in Covid-19"— Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science (Principal investigator: Sendhil Mullainathan of the University of Chicago)

"Targeted Interventions in Networked and Multi-Risk SIR Models: How to Unlock the Economy During a Pandemic"— Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, department head of electrical engineering and computer science, and deputy dean of academics for MIT Schwarzman College of Computing; and Daron Acemoglu, Institute Professor

Original article published on the MIT News website on July 17, 2020

Frédo Durand (left) and Sam Madden are the recipients of the first two named professorships in the Schwarzman College of Computing.

MIT Schwarzman College of Computing

The MIT Stephen A. Schwarzman College of Computing announced its first two named professorships, beginning July 1, to Frédo Durand and Samuel Madden in the Department of Electrical Engineering and Computer Science (EECS). These named positions recognize the outstanding achievements and future potential of their academic careers.

“I’m thrilled to acknowledge Frédo and Sam for their outstanding contributions in research and education. These named professorships recognize them for their extraordinary achievements,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing.

Frédo Durand, a professor of computer science and engineering in EECS, has been named the inaugural Amar Bose Professor of Computing. The professorship, named after Amar Bose, former longtime member of the MIT faculty and the founder of Bose Corporation, is granted in recognition of the recipient’s excellence in teaching, research, and mentorship in the field of computing. A member of the Computer Science and Artificial Intelligence Laboratory, Durand’s research interests span most aspects of picture generation and creation, including rendering and computational photography. His recent focus includes video magnification for revealing the invisible, differentiable rendering, and compilers for productive high-performance imaging.

He received an inaugural Eurographics Young Researcher Award in 2004; an NSF CAREER Award in 2005; an inaugural Microsoft Research New Faculty Fellowship in 2005; a Sloan Foundation Fellowship in 2006; a Spira Award for distinguished teaching in 2007; and the ACM SIGGRAPH Computer Graphics Achievement Award in 2016.

Samuel Madden has been named the inaugural College of Computing Distinguished Professor of Computing. A professor of electrical engineering and computer science in EECS, Madden is being honored as an outstanding faculty member who is recognized as a leader and innovator. His research is in the area of database systems, focusing on database analytics and query processing, ranging from clouds to sensors to modern high-performance server architectures. He co-directs the Data Systems for AI Lab initiative and the Data Systems Group, investigating issues related to systems and algorithms for data focusing on applying new methodologies for processing data, including applying machine learning methods to data systems and engineering data systems for applying machine learning at scale. 

Madden was named one of MIT Technology Review's "35 Innovators Under 35" in 2005, and received an NSF CAREER Award in 2004 and a Sloan Foundation Fellowship in 2007. He has also received several best paper awards in VLDB 2004 and 2007 and in MobiCom 2006. In addition, he was recognized with a "test of time" award in SIGMOD 2013 for his work on acquisitional query processing and a 10-year best paper award in VLDB 2015 for his work on the C-Store system.

Original article published on the MIT News website on July 20, 2020

MIT engineers have designed nanoparticle sensors that can diagnose lung diseases. If a disease-associated protein is present in the lungs, the protein cleaves a gaseous molecule from the nanoparticle, and this gas can be detected in the patient’s breath.

Credit: Cygny Malvar

Anne Trafton | MIT News Office

Using specialized nanoparticles, MIT engineers have developed a way to monitor pneumonia or other lung diseases by analyzing the breath exhaled by the patient.

In a study of mice, the researchers showed that they could use this system to monitor bacterial pneumonia, as well as a genetic disorder of the lungs called alpha-1 antitrypsin deficiency.

“We envision that this technology would allow you to inhale a sensor and then breathe out a volatile gas in about 10 minutes that reports on the status of your lungs and whether the medicines you are taking are working,” says Sangeeta Bhatia, the John and Dorothy Wilson Professor of Health Sciences and Technology and Electrical Engineering and Computer Science at MIT.

More safety testing would be needed before this approach could be used in humans, but in the mouse study, no signs of toxicity in the lungs were observed.

Bhatia, who is also a member of MIT’s Koch Institute for Integrative Cancer Research and the Institute for Medical Engineering and Science, is the senior author of the paper, which appears today in Nature Nanotechnology. The first author of the paper is MIT senior postdoc Leslie Chan. Other authors are MIT graduate student Melodi Anahtar, MIT Lincoln Laboratory technical staff member Ta-Hsuan Ong, MIT technical assistant Kelsey Hern, and Lincoln Laboratory associate group leader Roderick Kunz.

Monitoring the breath

For several years, Bhatia’s lab has been working on nanoparticle sensors that can be used as “synthetic biomarkers.” These markers are peptides that are not naturally produced by the body but are released from nanoparticles when they encounter proteins called proteases.

The peptides coating the nanoparticles can be customized so that they are cleaved by different proteases that are linked to a variety of diseases. If a peptide is cleaved from the nanoparticle by proteases in the patient’s body, it is later excreted in the urine, where it can be detected with a strip of paper similar to a pregnancy test. Bhatia has developed this type of urine test for pneumonia, ovarian cancer, lung cancer, and other diseases. 

More recently, she turned her attention to developing biomarkers that could be detected in the breath rather than the urine. This would allow test results to be obtained more rapidly, and it also avoids the potential difficulty of having to acquire a urine sample from patients who might be dehydrated, Bhatia says.

She and her team realized that by chemically modifying the peptides attached to the synthetic nanoparticles, they could enable the particles to release gases called hydrofluoroamines that could be exhaled in the breath. The researchers attached volatile molecules to the end of the peptides in such a way that when proteases cleave the peptides, they are released into the air as a gas.

Working with Kunz and Ong at Lincoln Laboratory, Bhatia and her team devised a method for detecting the gas from the breath using mass spectrometry. The researchers then tested the sensors in mouse models of two diseases — bacterial pneumonia caused by Pseudomonas aeruginosa, and alpha-1 antitrypsin deficiency. During both of these diseases, activated immune cells produce a protease called neutrophil elastase, which causes inflammation.

For both of these diseases, the researchers showed that they could detect neutrophil elastase activity within about 10 minutes. In these studies, the researchers used nanoparticles that were injected intratracheally, but they are also working on a version that could be inhaled with a device similar to the inhalers used to treat asthma.

Smart detection

The researchers also demonstrated that they could use their sensors to monitor the effectiveness of drug treatment for both pneumonia and alpha-1 antitrypsin deficiency. Bhatia’s lab is now working on designing new devices for detecting the exhaled sensors that could make them easier to use, potentially even allowing patients to use them at home.

“Right now we’re using mass spectrometry as a detector, but in the next generation we’ve been thinking about whether we can make a smart mirror, where you breathe on the mirror, or make something that would work like a car breathalyzer,” Bhatia says.

Her lab is also working on sensors that could detect more than one type of protease at a time. Such sensors could be designed to reveal the presence of proteases associated with specific pathogens, including perhaps the SARS-CoV-2 virus.

The research was funded by a Global Health Innovation Partnership grant from the Bill and Melinda Gates Foundation; Massachusetts General Hospital; the Ragon Institute of MGH, MIT, and Harvard; Janssen Research and Development; and the Kathy and Curt Marble Cancer Research Fund.

Original article published on the MIT News website on July 20, 2020

MIT UNDERGRADUATES ARE USING COMPUTING to tackle critical research questions, delving into fields as diverse as robotics, health care, and transportation through the Advanced Undergraduate Research Opportunities Program (SuperUROP).

A yearlong research experience supported by coursework, SuperUROP is an expanded version of MIT’s Undergraduate Research Opportunities Program. SuperUROP was launched by the Department of Electrical Engineering and Computer Science and quickly expanded across the School of Engineering. Thanks to philanthropic support, it also now brings together projects in the School of Humanities, Arts, and Social Sciences with computer science.

“SuperUROP has evolved into a microcosm that showcases the research taking place around the Institute,” says Ted Equi ’81, SM ’84, former SuperUROP industrial sponsor liaison. Equi, who is now the MIT Leaders for Global Operations director of academics, research, and career engagement, noted that 102 students enrolled in the advanced program this year. Here are project examples from this year’s class.

Space Exploration

Jaeyoung Jung ’21, Texas Instruments Undergraduate Research and Innovation Scholar

Advisors: Tomás A. Palacios, professor, Department of Electrical Engineering and Computer Science (EECS), with PhD student Nadim Chowdhury
SM ’18

Silicon-based electronics have transformed life on Earth, but they are ill-suited to the demands of space. This project’s goal is to equip computers for a trip to Venus, where surface temperatures can reach 471ºC (880ºF).

Semiconductors made with gallium nitride (GaN) rather than silicon can function at temperatures as high as 1,000ºC. However, GaN circuits typically consume too much power to be used practically in microprocessors. Nadim Chowdhury, working with Tomás A. Palacios, has developed a new transistor that addresses this issue, work that could prove critical in deploying electronics in harsh environments.

An electrical engineering major, Jaeyoung Jung is working with Chowdhury and Palacios on the next step: designing the world’s first energy-efficient GaN microprocessor. “This work will allow for sophisticated computing systems in spacecraft,” says Jung, who uses industry- standard software for semiconductor device analysis and circuit design.

“If everything goes well, we expect Jaeyoung to start fabricating the GaN microprocessor at the new MIT.nano cleanroom facility in early 2020,” says Palacios. The team hopes the microprocessor will be used to control a rover on a future NASA trip to Venus. “The SuperUROP program has allowed us to try a moon shot kind of project—in fact, a Venus-shot—and have an amazing MIT undergraduate student at the center of it.”

Robotics

Ashay Athalye ’20, Angle Undergraduate Research and Innovation Scholar

Advisors: Alberto Rodriguez, associate professor, Department of Mechanical Engineering (MechE), with MechE PhD student Maria Bauza SM ’18

Like humans, robots need to perceive and understand their environment to manage tasks. Unlike humans, robots still can’t easily and reliably track moving objects. Ashay Athalye’s project combines visual and sensory data to assist a robotic arm in better estimating object location.

Currently, many robots employ deep-learning algorithms such as Deep Object Pose Estimation (DOPE), which uses images to estimate object position. However, DOPE doesn’t consider information about where the object was previously or how it might be moving. Athalye, who is majoring in EECS with minors in mechanical engineering and economics, is endeavoring to incorporate such information by applying probabilistic filtering to the output of such algorithms, a method that has shown promise in preliminary testing.

“His work builds from state-of-the-art techniques based on deep learning to estimate the pose of objects under occlusions and fuse them with classic techniques for filtering that aim at providing smoother and temporally coherent object tracking,” says advisor Alberto Rodriguez. “The particular approach involves adding a probabilistic interpretation to outputs of a deep neural network, which then can be used as measures of confidence to do robust object tracking.”

Next, Athalye plans to apply similar filtering techniques to tactile data drawn from robot sensors. His goal is to effectively combine tactile and visual information to help robots with manipulation tasks.

“This project, which involves inference, machine learning, and control theory, has been a perfect way to apply what I’ve learned in my classes,” Athalye says.

Transportation

Avital Vainberg ’21, Undergraduate Research and Innovation Scholar

Advisors: Joseph Ferreira Jr. ’67, EE ’70, SM ’70, PhD ’71, professor, Department of Urban Studies and Planning (DUSP) with DUSP PhD student Rounaq Basu MCP ’19, SM ’19

Location-tagged data are widely available but often underutilized by urban planners and policy makers. Avital Vainberg is working to put such data to better use by developing visualizations that are accessible to nontechnical audiences.

Vainberg, who is majoring in urban science and planning with computer science, and minoring in theater arts, is using travel survey data from Singapore to develop an interactive dashboard. “The goal is to inform planners and policy makers of where, when, and why people are traveling in order to encourage better decisions regarding land use, zoning, and transportation infrastructure.”

Advisor Joseph Ferreira Jr. says, “Avital is focusing on a data-processing pipeline that isolates and parallelizes the computing-intensive image generation steps so that the activity patterns of subgroups can be visualized and compared on the fly from an interactive dashboard.”

For example, Vainberg has created an animation that maps the activity patterns of Singapore’s residents, revealing commuting habits and other trends. Her dashboard will enable users to filter the data by such criteria as demographics and time of day.

Ferreira says that these visualizations can reveal patterns that could otherwise be hard to detect, such as activity clusters that might signal a need for additional transportation.

“This project has simultaneously sharpened my coding and data science skills, pushed me to think critically of the world around me, and encouraged me to take on impactful projects,” Vainberg says.

Computer Systems

Amir Farhat ’20, Hewlett Foundation Undergraduate Research and Innovation Scholar

Project Title: Understanding the Fundamentals of Reconfigurable Data Center Networks
Advisor: Manya Ghobadi, TIBCO Career Development Assistant Professor, Department of Electrical Engineering and Computer Science; Computer Science and Artificial Intelligence Laboratory

The explosion of data in every field from health care to business has spurred growing demand for big data analytics. This has led to increased use of big data server farms, where up to a million servers work together to tackle complex computations and run large applications such as web search engines.

A computer science and engineering major, Amir Farhat is endeavoring to make large-scale data centers more efficient by changing the physical topology of a wired data center network to increase its throughput. The goal is to develop a “smart” data center.

“By engineering the network to be reconfigurable to adapt to demand, we hope to increase the application performance in large- scale data centers,” he says.

In traditional data center networks, operators decide in advance how much capacity to provide. Farhat is developing a simulation framework to experiment with alternative data center architectures and scheduling algorithms in the hopes of designing a reconfigurable data center.

“It might seem impossible to change the topology of a network without physically changing the cables,” says advisor Manya Ghobadi. But she says optical networking, which encodes information in light waves, opens the door to new design options. Since optical waves can be redirected using mirrors, they are capable of quick changes: no rewiring required.

“This is a realm where the network is no longer a static entity but a dynamic structure of interconnections that may change depending on the workload,” Ghobadi says, noting that Farhat is helping to lay the groundwork for the future. “This work promises to revolutionize the way networks are designed in practice, defined in textbooks, and taught in classrooms.”

Original article posted on the MIT Spectrum website in the Spring 2020 edition

Photo: Sarah Bastille

MACHINE-LEARNING SYSTEMS USE DATA TO UNDERSTAND PATTERNS and make predictions. When the system is predicting which photos are of cats, you may not care how certain it is about its results. But if it’s predicting the fastest route to the hospital, the amount of uncertainty becomes critically important.

“Imagine the system tells you ‘Route A takes 9 minutes’ and ‘Route B takes 10 minutes.’ Route A sounds better,” says Tamara Broderick, an associate professor in the Department of Electrical Engineering and Computer Science. “But now it turns out that Route A takes 9 minutes plus-or-minus 5, and Route B takes 10 minutes plus-or-minus 1. If you need a life-saving procedure in 12 minutes, suddenly your decision making really changes.”

A high-school outreach program, MIT’s Women’s Technology Program (WTP), first brought Broderick to campus. “My experience at WTP was formative,” she says. Now Broderick studies how machine-learning systems can be made to quantify the “known unknowns” in their predictions, using a mathematical technique called Bayesian inference. “The idea is to learn not just what we know [from the data], but how well we know it,” she explains.

Broderick says, “We’re trying to make science easier for biologists, for chemists, for physicists, so they can focus on their really cool problems and just get the data analysis out of the way.”

The catch is that traditional algorithms for “Bayesian machine learning” take a very long time to work on complex data sets like those in biology, physics, or social science. “It’s not just that we’re getting more data points, it’s that we’re asking more questions of those data points,” says Broderick, who is a principal investigator at MIT’s Computer Science and Artificial Intelligence Laboratory and affiliated with MIT’s Institute for Data, Systems, and Society. “If I have gene-expression levels for a thousand genes, that’s a thousand-dimensional [machine-learning] problem. But if I try to look at interactions between just one gene with another, that’s now a million-dimensional problem. The computational and statistical challenges go through the roof.”

These challenges impose a bottleneck on using Bayesian machine learning for many applications where quantifying uncertainty is essential. Some complex data analyses might take an infeasible amount of time to run—months or more. And in so-called “high-dimensional” data sets, such as ones with millions of gene interactions, it can be difficult to find the signal among the noise. “It’s harder to find out what’s really associated with what, when you have that many variables,” Broderick says.

In other words, Bayesian machine learning has a scaling problem. Broderick’s research devises mathematical work-arounds that reduce computational and statistical complexity “so that our methods run fast, but with theoretical guarantees on accuracy.” Her recent work includes techniques with colorful names—“kernel interaction trick,” “infinitesimal jackknife”—that evoke a sense of technical wizardry crossed with down-to-earth pragmatism. Indeed, Broderick says she sees scalable Bayesian machine learning as “a service profession” aimed at amplifying the discovery efforts of her fellow scientists.

One such effort came to Broderick’s attention from an economist colleague studying how microcredit—small, low-interest loans made to entrepreneurs in developing economies—affects household incomes. “She’s interested in finding out whether these small loans actually help people, but it was taking her a really long time to run her experiments with existing Bayesian software,” Broderick says. Broderick’s team has been developing methods for this work that are both accurate and orders of magnitude faster.

In another collaboration, her team is using Bayesian machine learning to quantify the uncertainty in different kinds of genomics experiments, work that opens the door to a wealth of new, interesting science, Broderick says. This will help biologists use the data they already have to make informed decisions on how to allocate their research funds to best support future work. Think of it as the science-focused version of predicting the fastest route to a hospital with the least uncertainty.

“Even when we’re writing a purely theoretical paper, I’d like to think that the theory is very much inspired by problems that arise in people’s applications,” Broderick says. “We’re trying to make science easier for biologists, for chemists, for physicists, so they can focus on their really cool problems and just get the data analysis out of the way.”

Original article posted on the MIT Spectrum website in the Spring 2020 edition

2020 EECS Celebrates:  Honoring the department's outstanding contributors

Due to the COVID-19 pandemic, the 2020 EECS Celebrates event and reception could not take place on campus this year.  Instead, this slide show virtually celebrates the department’s annual awards to faculty, students, and staff.

 

 

 

 

 

SPECIAL EECS AWARD

Seth J. Teller Award for Excellence, Inclusion, and Diversity

Named for the late EECS professor, this award honors members of the MIT community who embody those three values through work, research, or educational innovation. The 2020 winners were Professor Leslie Kolodziejski and EECS PhD students Candace Ross and José Cambronero Sanchez. All were honored for their activities to improve diversity and inclusion in the department.

Kolodziejski is the lead of the EECS Committee on Diversity, Equity and Inclusion (CDEI) and also focuses on graduate student diversity as the EECS Graduate Officer. 

Ross and Cambronero Sanchez were recognized for their many volunteer activities for the EECS Graduate Office, especially their leadership in the EECS Graduate Student Visit Days Events.  For the past three years they have planned, organized, publicized, and led the discussion for the "Student Diversity Panel" portion of the visit days.  Due to their efforts, attendance in this event has grown each year.  Visiting admitted students describe how informative and helpful the authentic discussion was during the panel, and report choosing MIT because of our openness about diversity.   

 

TEACHING AWARDS

Burgess (1952) & Elizabeth Jamieson Prizes for Excellence in Teaching (EECS Award)

• Patrick Jaillet, Co-Director, MIT Operations Research Center; Dugald C. Jackson Professor of EECS, recognized for his teaching in 6.036 Introduction to Machine Learning.

• Pablo Parrilo, Joseph F. & Nancy P. Keithley Professor, recognized for his teaching in 6.215 (6.255J) Optimization Methods and 6.036 Introduction to Machine Learning.

 

EECS Outstanding Educator Award (EECS Award)

• Kimberle Koile, Lecturer, was recognized for her teaching in 6.UAT Oral Communication and 6.034 (6.844) Artificial Intelligence.

 

Jerome H. Saltzer Award for Excellence in Teaching (EECS Award)

• Peter Hagelstein,Associate Professor of EE, was recognized for his teaching in 6.011 Introduction to Communications, Control, and Signal Processing and 6.728 Applied Quantum and Statistical Physics.

 

Kolokotrones Education Award (EECS Award)

• Arvind Satyanarayan, NBX Career Development Assistant Professor of EECS, was recognized for creating the department's first course on data visualization, 6.984 Interactive Data Visualization, and also for curricular contributions to 6.170 Software Studio

 

Louis D. Smullin (’39) Award for Excellence in Teaching (EECS Award)

• Daniel Sanchez Martin, Associate Professor of EECS was recognized for his effort in redesigning 6.004 Computation Structures, EECS introductory subject in Logic Design and Computer Architecture. He used Bluespec, a modern hardware description language and integrated it with an open source logic synthesis tool to provide feedback regarding circuit area and timing.

 

Ruth and Joel Spira Awards for Excellence in Teaching (SoE Award)

• Tamara Broderick, Associate Professor of EECS, recognized for her outstanding teaching of 6.882 Advanced Topics in Artificial Intelligence, 6.036 Introduction to Machine Learning, and 6.435 Bayesian Modeling and Inference. She also made important contributions to 6.041 Introduction to Probability, incorporating new problem sets about real situations.

• Julian Shun, Douglas Ross (1954) Career Development Assistant Professor of Software Technology, was recognized for the breadth of his teaching ability in 6.006 Introduction to Algorithms, for his teaching in 6.172 Performance Engineering of Software Systems, and for creating the new graduate course 6.886 Algorithms Engineering.

 

AWARDS FROM STUDENT ORGANIZATIONS

Frank E. Perkins Award for Excellence in Graduate Advising (Institute Award: Recognizes faculty who demonstrates unbounded compassion and dedication towards students)

• Michael Carbin, Assistant Professor of EECS

• Leslie Kolodziejski, EECS Graduate Officer; EECS CDEI Co-Chair; Professor of EE

 

HKN Best Instructor Award (EECS Award)

• Jason S. Ku, Lecturer, received the 2020 award for his teaching in 6.006 Intro to Algorithms.

 

IEEE/ACM Best Advisor Award (EECS Award)

• Silvina Hanono Wachman, Principal Lecturer, received the 2020 award for her guidance and support of students -- for being someone they could come to with any problems in their lives (not just academics), especially during the pandemic.

 

SPECIAL RECOGNITIONS

Department Head Special Recognition Award (EECS Award)

• Irene Huang, EECS Administrative Officer, was recognized for her excellent and important work for the department.

• Chris Terman, Senior Lecturer Emeritus, was recognized for his work in the EECS undergraduate office, especially for new tools he created that enable staff to work more effectively (and from home after the pandemic struck).  His solutions changed the way students reach out to staff and improved understanding of the students’ degree progress for both students and advisors.

 

Junior Bose Award (SoE Award: Recognizes an outstanding contributor to education from among the faculty members who are being proposed for promotion to associate professor without tenure.)

• Justin Solomon, Associate Professor of EECS, for his teaching in 6.006 Intro to Algorithms and 6.837 Computer Graphics.

 

Margaret MacVicar Faculty Fellow (MIT Program: Honors MIT’s best teachers, recognizing faculty who have made exemplary and sustained contributions to the teaching and education of undergraduates at MIT.)

• Jacob White, Cecil H. Green Professor of EE and chair of the MIT Committee on Curricula was named a 2020 MacVicar Faculty Fellow.

 

Richard J. Caloggero Award (EECS Award: Recognizes members of the EECS department who have shown loyalty, dedication, and effectiveness beyond normal expectations.)

• Luca Daniel, Professor of EECS, was recognized for his dedicated work in numerous EECS committees, especially faculty search.

 

STUDENT TEACHING AWARDS  (EECS Awards)

Carlton E. Tucker Teaching Award

• Srini Raghuraman

 

Frederick C. Hennie III Teaching Awards

• Evan Denmark

• Mesert Kebed

• Jennifer Madiedo

• Valerie Richmond

• Zhongxia Yan

 

Harold Hazen Teaching Award

• Dylan Mathis McKay

 

Undergraduate Teaching Assistant (UTA) Awards

• Srijon Mukherjee

• Tony Wang

 

STUDENT UROP AWARDS  (EECS Awards)

Anna Pogosyants UROP Award

• Zoë Marschner
  Project: Hexahedral Mesh Repair via Sum-of-Squares Relaxation
  Supervisor: Justin Solomon

 

Jeremy Gerstle Undergraduate Research Opportunity Program (UROP) in AI Award

• Steven Liu
  Project: Diverse Image Generation via Self-Conditioned GANs
  Supervisor: Antonio Torralba

 

Licklider UROP Award

• Carlos Castillo Lozada
  Project: Sprayable User Interfaces: Prototyping Large-Scale Interactive Surfaces with Sensors and Displays
  Supervisor: Stefanie Mueller

 

Morais (1986) and Rosenblum (1986) UROP Award

• Brandon Motes
  Project: Automated Optical Measurements to Predict Performance of Optoelectronic Devices
  Supervisor: Vladimir Bulovic

 

Robert M. Fano UROP Award

• Dhruv Rohatgi (2 Projects)
  Project1: Truncated Linear Regression in High Dimensions
  Project 2: Near-Optimal Bounds for Online Caching with Machine Learned Advice
  Supervisors: Costis Daskalakis, Piotr Indyk

 

SuperUROP Awards (2019-2020)

• Fatima Gunter-Rahman
  Project: Using Single-Cell Transcriptomics to Understand Cognitive Resilience in Alzheimer’s Disease
  Supervisor: Li-Huei Tsai

• Lior Hirschfeld
  Project: Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
  Supervisor: Regina Barzilay

• Yaateh Richardson
  Project: Learned Bloom Filters
  Supervisor: Tim Kraska

 

STUDENT CLASS AWARDS  (EECS Awards)

David A. Chanen Writing Award (for Group Writing in 6.033)

• Eric Hong, Magdalena Price, and Alok Puranik
  Title:  ZoomNet: Updating NASA’s ExtraNet

 

George C. Newton Undergraduate Laboratory Award (6.111 Group Project)

• Ishaan Govindarajan and Eric Pence
  Project: The DiGuitar

 

Northern Telecom/BNR Undergraduate Laboratory Award (6.111 Group Project)

• Jeremy McCulloch, Adam Potter, and Sreya Vangara
  Project: Futuristic Pepper’s Ghost Approximation

• Kendall Garner and Claire Traweek
  Project: digitEyez

 

J. Francis Reintjes Excellence in 6-A Industrial Practice Award

• Matthew Hutchinson
  Title:  Applying High Performance Computing to Early Fusion Video Action Recognition
  Supervisor: Charles Leiserson
  Company: MIT Lincoln Lab

• Lokhin Cheng
  Title: Digital Control for Adaptive Efficiency in Switching Regulators
  Supervisor: David Perreault
  Company: Analog Devices

 

STUDENT THESIS AWARDS  (EECS Awards)

Charles & Jennifer Johnson Artificial Intelligence and Decision-Making MEng Thesis Awards

1^st Place

• David Mayo
  Title: Understanding Object Recognition Performance at Scale in Machines and Humans
  Supervisor: Boris Katz

2^nd Place

• Nilai Sarda
  Title:  On Anomaly Detection in Particle Accelerators
  Supervisor: Justin Solomon

 

Charles & Jennifer Johnson Computer Science MEng Thesis Awards

1^st Place

• Ryan Senanayake
  Title: A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra
  Supervisor: Saman Amarasinghe

2^nd Place

• Severyn Kozak
  Title: Chasing Zero Variability in Software Performance
  Supervisors: Charles Leiserson, Tao B. Schardl

 

David Adler Electrical Engineering MEng Thesis Awards

1^st Place

• Theia Henderson
  Title: A Continuous Approach to Information-Theoretic Exploration with Range Sensors
  Supervisors: Vivienne Sze, Sertac Karaman

2^nd Place

• Chandler Squires
  Title: Causal Structure Discovery from Incomplete Data
  Supervisor: Caroline Uhler

 

Ernst A. Guillemin SM Thesis Award in Artificial Intelligence and Decision Making

• Jingzhao Zhang
  Title: Dynamical Systems View of Acceleration in First-Order Optimization
  Supervisor: Suvrit Sra

 

Ernst A. Guillemin SM Thesis Award in Electrical Engineering

• Alireza Fallah
  Title: Robust Accelerated Gradient Methods for Machine Learning
  Supervisor: Asu Ozdaglar

  

George M. Sprowls PhD Thesis Award in Artificial Intelligence and Decision Making

• Jiajun Wu
  Title: Computational Perception of Physical Object Properties
  Supervisors: William Freeman, Joshua Tenenbaum

 

George M. Sprowls PhD Thesis Award in Computer Science

• Joshua Alman
  Title: Linear Algebraic Techniques in Algorithms and Complexity
  Supervisors: Ryan Williams, Virginia Williams

• Young Hyun (Albert) Kwon
  Title: Riffle - An Efficient Communication System with Strong Anonymity
  Supervisor: Srini Devadas

• Amy Xian Zhang
  Title: Systems for Collections Human Curation of Online Discussion
  Supervisor: David Karger

 

CONGRATULATIONS TO ALL WINNERS!

 

Send changes/corrections to: eecs-communications@mit.edu

Top row, left to right: Thomas Heldt and William Oliver. Bottom row, left to right: , Vivienne Sze, and Caroline Uhler.

School of Engineering

The School of Engineering has announced that MIT has granted tenure to eight members of its faculty, including four from EECS: Thomas Heldt, William Oliver, Vivianne Sze and Caroline Uhler

“This year’s newly tenured faculty in the School of Engineering are truly inspiring,” says Anantha P. Chandrakasan, dean of the School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “Their dedication to research and teaching drives novel solutions urgently needed to advance their fields.”

This year’s newly tenured associate professors are:

Thomas Heldt, in the Department of Electrical Engineering and Computer Science and the Institute for Medical Engineering and Science, focuses his research on signal processing, mathematical modeling, and model identification to understand the physiology of the injured brain and to support real-time clinical decision-making, monitoring of disease progression, and titration of therapy. His research is conducted in close collaboration with clinicians from Boston-area hospitals — particularly in emergency, neonatal and neurocritical care — where his team is integrally involved in designing and deploying high-fidelity data-acquisition systems and in collecting clinical data. 

William Oliver, in the Department of Electrical Engineering and Computer Science, works with the Quantum Information and Integrated Nanosystems Group at Lincoln Laboratory and the Engineering Quantum Systems Group at MIT, where he provides programmatic and technical leadership for programs related to the development of quantum and classical high-performance computing technologies for quantum information science applications. His interests include the materials growth, fabrication, design, and control of superconducting quantum processors, as well as the development of cryogenic packaging and control electronics involving cryogenic CMOS and single-flux quantum digital logic. He is director of the Center for Quantum Engineering and associate director of the Research Laboratory of Electronics.

Vivienne Sze, in the Department of Electrical Engineering and Computer Science, focuses her research on designing and implementing computing systems that enable energy-efficient machine learning, computer vision, and video compression for a wide range of applications, including autonomous navigation, digital health, and the internet of things. In particular, she is interested in the joint design of algorithms, architectures, circuits, and systems to enable optimal tradeoffs between energy consumption, speed, and quality of results. 

Caroline Uhler, in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society, focuses her research at the intersection of machine learning, statistics, and genomics. In particular, she is interested in obtaining a better understanding of genome regulation by developing machine learning methods that can integrate different data modalities, including interventional data, and bridge the gap from predictive to causal modeling.

Original article published on the MIT News website on July 24, 2020