News

Aviral Kumar, Serena Wang and Eric Wallace win 2022 Apple Scholars in AI/ML PhD fellowships

Three EECS graduate students, Aviral Kumar (advisor: Sergey Levine), Serena Wang (advisors: Rediet Abebe and Michael Jordan), and Eric Wallace (advisors: Dan Klein and Dawn Song) have been named 2022 recipients of the Apple Scholars in AI/ML PhD fellowship.  This fellowship recognizes graduate and postgraduate students in the field of Artificial Intelligence and Machine Learning who are "emerging leaders in computer science and engineering" as demonstrated by their "innovative research, record as thought leaders and collaborators, and commitment to advance their respective fields."  Kumar is working in the area of "Fundamentals of Machine Learning" to develop "reinforcement learning algorithms and tools that enable learning policies by effectively leveraging historical interaction data and understanding and addressing challenges in using RL with deep neural nets." Wang is working in the area of "AI for Ethics and Fairness" to "foster positive long-term societal impact of ML by rethinking ML algorithms and practices, employing tools from robust optimization, constrained optimization, and statistical learning theory."  Wallace is working in the area of "Privacy Preserving Machine Learning," to make "NLP models more secure, private, and robust." Apple Scholars receive support for their research, internship opportunities, and a two-year mentorship with an Apple researcher in their field.

‘Off label’ use of imaging databases could lead to bias in AI algorithms, study finds

A paper with lead author EECS postdoc Efrat Shimron and co-authors EECS graduate student Ke Wang, UT Austin professor Jonathan Tamir (EECS PhD ’18), and EECS Prof. Michael Lustig shows that algorithms trained using "off-label" or misapplied massive, open-source datasets are subject to integrity-compromising biases.  The study, which was published in the Proceedings of the National Academy of Sciences (PNAS), highlight some of the problems that can arise when data published for one task are used to train algorithms for a different one.  For example, medical imaging studies which use preprocessed images may result in skewed findings that cannot be replicated by others working with the raw data.  The researchers coined the term “implicit data crimes” to describe research results that are biased because algorithms are developed using faulty methodology. “It’s an easy mistake to make because data processing pipelines are applied by the data curators before the data is stored online, and these pipelines are not always described. So, it’s not always clear which images are processed, and which are raw,” said Shimron. “That leads to a problematic mix-and-match approach when developing AI algorithms.”

Chandan Singh is 2022 Berkeley Grad Slam Competition semi-finalist

CS graduate student Chandan Singh (advisor: Bin Yu) has made it to the semi-finals of the 2022 Berkeley Grad Slam Competition, a UC showcase for graduate student research presented in three-minute talks for a general audience, likened to short Ted Talks.  In "Unlocking Scientific Secrets by Distilling Neural Networks," Singh hopes to build on recent advances in machine learning to improve the world of healthcare.   His research focuses on how to build trustworthy machine-learning systems by making them more interpretable through partnerships with domain experts (e.g. medical doctors and cell biologists). These collaborations give rise to useful methodology that both build more transparent models as well as improve the trustworthiness of black-box models. He hopes to help bridge the gap between both types of models so that they can be reliably used to improve real-world healthcare.

Lucas Spangher brings musicians together for Ukraine benefit concert

CS graduate student Lucas Spangher (advisor: Costas Spanos) gathered musicians from all over the Bay Area to perform a benefit concert in support of Ukraine on March 13th.  Opera and gospel singers, violists, pianists and harpists, were among the more than one dozen volunteers to participate in the Benefit Concert for Humanitarian Aid for Ukraine at Herbst Hall in San Francisco.  Spangher, who plays the cello, reached out to other local musicians on social media to ask if anyone would be interested in participating in an informal, online musical performance in honor of Ukraine, and it expanded from there. “It turned into this amazing professional operation,” said Spangher, “which I think just speaks to the energy and communal desire to do something. This is more than just a fundraiser. It’s a political statement and a way to honor Ukraine’s amazing contributions to classical music that can’t be erased by a vicious autocrat.”  Spangher is a committed climate change activist whose research focuses on how to make artificial intelligence become more flexible for a transition to green energy.  All proceeds from the performance have been donated to Nova Ukraine.

Berkeley CS students help build a database of police misconduct in California

Students in the Data Science Discovery Program are filling a gap in engineering resources to help journalists more easily sort through large stores of records for their research.  The Discovery Program, which is part of Berkeley's Division of Computing, Data Science, and Society (CDSS), connects  around 200 undergraduates with hands-on, team-based data science research projects at Berkeley, government agencies, community groups, and entrepreneurial ventures.  Students have worked on projects like the SF Chronicle's air quality map, the Wall Street Journal's effort to analyze its source and topic diversity using natural processing language, and the California Reporting Project's police misconduct database. “I don’t know if we’d be able to do this without them,” said KQED data reporter Lisa Pickoff-White. “None of these newsrooms would be able to automate this work on their own.”

2022 Diversity in Tech Symposium: Advancing Climate Resilience - March 10-11th

A number of EECS faculty and students are slated to participate in the 2022 Diversity in Tech Symposium, which will be held virtually on March 10 & 11.  This year's theme is "Advancing Climate Resilience."  EECS Prof. Tsu-Jae King Liu, dean of Berkeley Engineering, will warm up the audience with a fireside chat on the symposium's topic;  EECS Prof. Costas Spanos, director of the CITRIS and Banatao Institute, will welcome participants to the second day of the event;  Adjunct Prof. Sascha von Meier will participate in the UC Berkeley-hosted panel Getting to zero: Trends in the built environment; and senior EECS major Katherine Shu will represent WiCSE in a presentation on the Career Fair.  The symposium is open to the public and anyone interested in climate innovation and action, and the advancement of women and underrepresented communities working in technology fields, is encouraged to attend.

CDSS and Cal Performances present: "Place and Displacement: Bias in Our Algorithms and Society"

The Division of Computing, Data Science, and Society (CDSS) is excited to announce an upcoming event in collaboration with Cal Performances. On October 28, "Place and Displacement: Bias in Our Algorithms and Society" will feature Cal Artist-in-Residence Angélique Kidjo in conversation with CDSS Associate Provost Jennifer Chayes, EECS Assistant Professor Nika Haghtalab and Computer Science PhD Student Devin Guillory (advisor: Trevor Darrell). The group will discuss the intersection of artificial intelligence and art, computing tools' reflection of the biases of the people and data used to train them, and promising interventions that could make algorithms more just.  The event, which is free and open to the public, will be held in person at Zellerbach Hall from 4:00 to 5:30 pm PST on Thursday, October 28. It will also be live-streamed. Registration is required and now open!

Anca Dragan, Raluca Popa, and Thomas Courtade win 2020 EECS Teaching Awards

The 2019-20 EECS Teaching Awards recognize three members of our faculty whose extraordinary performances kept students focused and engaged during a particularly difficult year.  The CS Diane McEntyre Award for Excellence in Teaching was presented to Anca Dragan in the spirit of McEntyre who was know for her "dedication to teaching and her innovative programs for women in mathematics and computer science." Students said Dragan was "passionate, dedicated, inclusive, and enthusiastic," and "literally the most entertaining and helpful professor I’ve ever had." The CS Jim and Donna Gray Faculty Award for excellence in undergraduate teaching went to Raluca Ada Popa. She was commended by students for her passion, clarity, care, and enthusiasm, and was described as an "AMAZING" and entertaining lecturer who "encourages a lot of class discussion and gets us involved, even over zoom."   The EE Award for Outstanding Teaching, which recognizes innovation and excellence in curriculum and teaching methods, publication of quality textbooks, graduate and undergraduate advising, and personal inspiration of students, was presented to Thomas Courtade.  He was described by students as "a brilliant instructor" whose "ability to teach the fundamental core concepts of this content is incredible." He was also said to be "amazing when it comes to interacting with students. It is hard to believe how many people are in the class, because he makes it feel very personal."

Matthew Anderson wins 2021-22 Google-CMD-IT LEAP Fellowship Award

EECS Ph.D. student Matthew Anderson (advisors: Jan Rabaey and Ali Niknejad) has won the Google-CMD-IT LEAP Fellowship Award for 2021-22.  The award recognizes computer science scholars from underrepresented groups who are "positively influencing the direction and perspective of technology."  Anderson, who also won the 2021 Berkeley EECS Eugene L. Lawler Prize, has been a pioneer in the department's anti-racism efforts, including taking a leadership position in the EECS and Division of Computing, Data Science, and Society (CDSS) faculty/staff/student Anti-Racism Committee. His research interests include design of mixed-signal and wireless circuits for bio-sensing, brain machine interfaces, and accelerated neural networks.  This award is part of a joint effort by Google Research, the Computing Alliance of Hispanic-Serving Institutions (CAHSI), and the Center for Minorities and People with Disabilities in Information Technology (CMD-IT) Diversifying LEAdership in the Professoriate (LEAP) Alliance to increase the diversity of doctoral graduates in computing.  Anderson is one of three winners of this year's award. Last year's inaugural award was won by EECS grad student Gabriel Fierro.

Sagnik Bhattacharya and Jay Shenoy named 2022 Siebel Scholars

Graduate students Sagnik Bhattacharya (B.A. CS and Statistics '21) and Jay Shenoy (B.A. CS '21) are recipients of the 2022 Siebel Scholars award.  The Siebel Scholars program annually recognizes "exceptional students from the world’s leading graduate schools of business, computer science, and bioengineering."  Bhattacharya, a 5th Year Masters student and TA for CS 70 (Discrete Math and Probability), is interested in machine learning theory and its applications in data science.  He is currently working with Prof. Jonathan Shewchuk on the theory behind deep linear neural networks.  Shenoy is working on computational imaging with Prof. Ren Ng, as well as problems in autonomous vehicle simulation in the Industrial Cyber-Physical Systems (iCyPhy) group.  Siebel Scholars receive a $35,000 award for their final year of studies. "On average, Siebel Scholars rank in the top five percent of their class, many within the top one percent."