News

Rediet Abebe named 2022 Carnegie Fellow

CS Assistant Prof. Rediet Abebe has been named to the 2022 class of Andrew Carnegie Fellows.  This fellowship recognizes "scholars and writers in the humanities and social sciences" who are addressing "important and enduring issues confronting our society."  Abebe’s research is in algorithms and artificial intelligence, with a focus on inequality and distributive justice concerns.  Her project, “Algorithms on Trial: Interrogating Evidentiary Statistical Software,” will shed light on the ubiquitous and improper use of software tools as evidence in the U.S. criminal legal system. "The project will use a mix of algorithmic and qualitative techniques to analyze large legal databases, with a focus on admissibility hearings. The results will coalesce in the form of a public platform containing thousands of tools, alongside known issues and resources like ready-to-file affidavits to empower public defenders."  Abebe is a co-founder and co-organizer of both the MD4SG research initiative and the nonprofit organization Black in AI, where she also sits on the board of directors and co-leads the Academic Program.  Carnegie Fellows, who each receive a $200K award, are selected by a panel of jurors based on the originality and potential impact of their proposal as well as their capacity to communicate their findings to a broad audience.

Chase Norman selected to participate in the Heidelberg Laureate Forum

CS undergraduate student Chase Norman is among 200 young mathematics and computer science researchers selected from across the globe to attend the 9th Heidelberg Laureate Forum (HLF) in Germany this September. During the week-long conference, participants will share ideas with some of the "most exceptional mathematicians and computer scientists of their generations," namely the recipients of some of the field’s most prestigious awards: the Abel Prize, ACM A.M. Turing Award, ACM Prize in Computing (won this year by Berkeley CS Prof. Pieter Abbeel), Fields Medal, and Nevanlinna Prize. Participants and laureates will interact through a blend of scientific and social activities that are designed to foster a relaxed atmosphere and encourage scientific exchange.  Participants are selected by a panel of international reviewers on the basis of their research experience, social engagement skills, and letter of motivation.”. Norman is a CS and Math double major who was admitted to the EECS Honors Program in the breadth area of Mathematical Logic and Foundations.  He is also the president of the CS honor society Upsilon Pi Epsilon, was course staff for CS 170 and CS 61A, and was a percussionist with UC Jazz and the UCB Symphony Orchestra.

Dave Epstein wins 2022 Paul & Daisy Soros Fellowship

CS graduate student Dave Epstein (advisor: Alexei Efros) has won a 2022 Paul & Daisy Soros Graduate Fellowship for New Americans.  This fellowship recognizes outstanding graduate students who are immigrants and children of immigrants in the United States, and "who are poised to make significant contributions to US society, culture or their academic field."  Epstein is affiliated with the Berkeley AI Research (BAIR) Lab where he is teaching machines to solve visual problems without labels, and enabling a creative understanding of the real world to emerge. He is also interested in language, machine learning, synthesis, and interaction. Paul & Daisy Fellowships come with a $90K award.

Bin Yu chosen as speaker for 2023 Wald Lectures

EECS Prof. Bin Yu (Statistics M.A. '87/Ph.D. '90) has been chosen by the Institute of Mathematical Statistics (IMS) to present the 2023 Wald Memorial Lectures.  Considered the highest honor bestowed by the IMS, a single Wald Lecturer is selected annually to deliver a series of one, two, three or four one-hour talks on a single topic of unusual interest over multiple days at the IMS Annual Meeting in Probability and Statistics.  This format allows speakers to break down complex subject matter in a way that makes it more accessible to non-specialists.  The honor is named for Abraham Wald, the mathematician who founded the field of statistical sequential analyses.  Yu, who has a joint appointment in the Department of Statistics, is focused on solving high-dimensional data problems through developments of statistics and machine learning methodologies, algorithms, and theory. Her group is engaged in interdisciplinary research with scientists from genomics, neuroscience, and medicine.

Pieter Abbeel wins 2021 ACM Prize in Computing

EECS Prof. Pieter Abbeel is the recipient of the 2021 Association for Computing Machinery (ACM) Prize in Computing.  This award  recognizes an early to mid-career computer scientist whose has made "a fundamental innovative contribution in computing that, through its depth, impact and broad implications, exemplifies the greatest achievements in the discipline."  Abbeel is known for his pioneering approaches to robot learning, including teaching robots through human demonstration (“apprenticeship learning”) and through their own trial and error (“reinforcement learning”).  He has created robots that can perform surgical suturing, detect objects, and plan their trajectories in uncertain situations. More recently, he introduced “few-shot imitation learning,” where a robot is able to learn to perform a task from just one demonstration after having been pre-trained with a large set of demonstrations on related tasks.  He is also credited with the innovation of combining reinforcement learning with deep neural networks to usher in the new field of deep reinforcement learning, which can solve far more complex problems than computer programs developed with reinforcement learning alone.  These contributions have formed the foundation of contemporary robotics and continue to drive the future of the field.  Abbeel is also the Co-Founder, President and Chief Scientist at AI robotics company Covariant. The ACM Prize in Computing  The award carries a prize of $250,000, from an endowment provided by Infosys Ltd.

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.”

Robots, AI and podcasting: a Q&A with Pieter Abbeel

EECS Prof. Pieter Abbeel launched “The Robot Brains Podcast” in the spring of 2021.   In each episode, he is joined by leading experts in AI Robotics from around the world to explore how far humanity has come in its mission to create conscious computers, mindful machines and rational robots.  Abbeel sits down for a Q&A with Berkeley Engineering, in which he discusses his experience with podcasting and how it has shaped his own thinking about communicating AI to a broader audience.

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.