News

Noam Nisan, Kimberly Keeton, Bruce Hajek and Nickhil Jakatdar named 2022 Berkeley EECS Distinguished Alumni

Congratulations to the winners of the 2022 EECS Distinguished Alumni Awards!  The CS winners are Noam Nisan (academia) and Kimberly Keeton (industry); and the EE winners are Bruce Hajek (academia) and Nickhil Jakatdar (industry). Noam Nisan (Ph.D. 1988, advisor: Richard Karp), currently a CS professor at Hebrew University of Jerusalem, was cited "For fundamental contributions to computational complexity theory and the creation of the field of algorithmic mechanism design;" Kimberly Keeton (M.S. 1994/Ph.D. 1999, adviser: David Patterson), currently a principal engineer at Google, was cited "For leadership in the research and the production of computer data and storage systems, and for mentoring the next generation of computer scientists and engineers;"  Bruce Hajek (Ph.D. 1979, advisor: Eugene Wong), currently an ECE professor at the University of Illinois at Urbana-Champaign, was cited "For his prodigious and fundamental research contributions to stochastic processes, information theory, and communications and computer networks; for his sustained and worldwide influence as a beloved teacher and mentor; and for his major leadership role in electrical and computer engineering;" and Nickhil Jakatdar (Ph.D. 2000, advisor: Costas Spanos), currently the CEO of GenePath Diagnostics, was cited for "For serial entrepreneurship and visionary leadership across several sectors, with profound impact to the microelectronics industry and to the developing world." Their awards will be presented at the 2022 Berkeley EECS Annual Research Symposium (BEARS) on April 25th.

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.

Audrey Sillers wins 2022 BSA Excellence in Management Award

Audrey Sillers, the EECS Director of Student Diversity, has won a Berkeley Staff Assembly (BSA) 2022 Excellence in Management (EIM) Award.  This award "honors exemplary non-academic managers and supervisors who have led their teams and team members to meaningful accomplishments this past year."  The 2022 theme, building and maintaining community, highlights leaders "who demonstrate and encourage flexibility, adaptiveness, supportiveness, compassion, understanding, work-life balance, and well-being." Sillers, who was nominated by her team, will be honored at a live-streamed ceremony on May 3rd. "Audrey exemplifies so many of the UC Berkeley Principles of Community, not in a self-conscious way, but just in the way that she operates in the world as a person," said one of her coworkers.  "Audrey’s passion for diversity and her openness to her staff developing their own capacities to do better work as advisors to a very diverse student population has been inspirational. Having such a supervisor makes a tremendous difference."

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.

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.

Berkeley EECS ranks 1 & 2 in 2023 US News graduate rankings

Berkeley EECS is once again ranked as the #1 Electrical/Electronic/Communications Engineering graduate program in the country for 2023, tied with MIT and  Stanford.  The Berkeley Computer Engineering graduate program ranked #2 (tied with Stanford), as did the Computer Science graduate program (tied with Carnegie Mellon and Stanford).  Berkeley Engineering, as a whole, again ranked #3.

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.