students

Alisha Menon wins 2022 Outstanding Graduate Peer Mentor Award

EECS Ph.D. candidate Alisha Menon (M.S. '20, advisor: Jan Rabaey) has won a 2022 Outstanding Graduate Peer Mentor Award.  This award, presented by The Graduate Assembly, honors four Berkeley graduate and professional students annually "who have shown an outstanding commitment to mentoring, advising, and generally supporting either undergraduate students or their fellow graduate students."  Menon's research is in the area of neural engineering, an interdisciplinary field centered on the interface between humans and computers.  Her focus is on digital integrated circuits and systems for biomedical applications, specifically the intersection of hardware-efficient machine learning algorithms, physiological sensor fusion, gesture recognition, and closed-loop neural prosthetic feedback.  Menon won an NSF Graduate Research Fellowship and UC Berkeley Fellowship in 2018.  She is also an accomplished theater actress and Indian Classical dancer.

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.”

Tiny switches give solid-state LiDAR record resolution

A new type of high-resolution LiDAR chip developed by EECS Prof. Ming Wu could lead to a new generation of powerful, low-cost 3D sensors for autonomous cars, drones, robots, and smartphones. The paper, which appeared in the journal Nature, was co-authored by his former graduate students Xiaosheng Zhang (Ph.D. '21) and Johannes Henriksson (Ph.D. '21), current graduate student Jianheng Luo, and postdoc Kyungmok Kwon, in the Berkeley Sensor and Actuator Center (BSAC).  Their new, smaller, more efficient, and less expensive LiDAR design is based on a focal plane switch array (FPSA) with a resolution of 16,384 pixels per 1-centimeter square chip, which dwarfs the 512 pixels or less currently found on FPSA.  The design is scalable to megapixel sizes using the same complementary metal-oxide-semiconductor (CMOS) technology used to produce computer processors.   Additionally, large, slow and inefficient thermo-optic switches are replaced by microelectromechanical system (MEMS) switches, which are traditionally used to route light in communications networks.  If the resolution and range of the new system can be improved, conventional CMOS production technology can be used to produce the new, inexpensive chip-sized LiDAR.

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.

He Yin and Murat Arcak win 2019-20 Brockett-Willems Outstanding Paper Award

EECS Prof. Murat Arcak and his graduate student He Yin have won the second Systems & Control Letters (SCL) Brockett-Willems Outstanding Paper Award. Their paper, "Reachability analysis using dissipation inequalities for uncertain nonlinear systems," published in SCL Volume 142, on August 2020, was deemed the best of 295 papers submitted to the journal in the two-year period between January 2019 through December 2020.  Co-authors include former ME Prof. Andrew Packard, who died in 2019, and Packard's former graduate student, Peter Seiler.  SCL hopes to present the award at the 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS) which will be held in Bayereuth, Germany, in September 2022.

Rose Abramson wins EPE 2021 Young Author Best Paper Award

EECS graduate student Rose A. Abramson (advisor:  Robert Pilawa-Podgurski) has won the European Power Electronics and Drives Association (EPE) 2021 Young Author Best Paper Award.   Her paper, “A High Performance 48-to-8 V Multi-Resonant Switched-Capacitor Converter for Data Center Applications,” co-authored by EECS alumnus Zichao Ye (Ph.D. '20) and Prof. Robert Pilawa-Podgurski, was presented during the EPE 2020 ECCE Europe conference.  Abramson, whose research focuses on power electronics and energy, received her B.S. in 2015 and her M.Eng. in 2016, both from MIT, and worked as a project electronics engineer at both Nucleus Scientific and Lutron Electronics before coming to Berkeley.   EPE Awards honor outstanding achievements in power electronics and more generally in the field of EPE activities.