News

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.

3 UC Presidents and Gary S. May

UC Davis Chancellor and EECS alumnus Gary S. May (M.S. '88/Ph.D. '91, advisor: Costas Spanos) took the stage with UC President Michael V. Drake and Presidents Emeriti Janet S. Napolitano and Mark G. Yudof  for the UCD Chancellor's Colloquium on March 8th.  The four discussed the challenges they faced and lessons learned during their tenures in office.  Topics included the impact of the pandemic on campus communities, the importance of public health, and the efficacy of remote learning; the university's federal lawsuit over the Deferred Action for Childhood Arrivals (DACA) program; approaches to managing UC funding cuts, including maintaining access to retirement plans and student aid;  and America's cultural and democratic future, including ways that universities might help shape it.

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.

Alberto Sangiovanni-Vincentelli awarded AGH UST Honorary Doctorate

EECS Prof. Alberto Sangiovanni-Vincentelli will receive an Honorary Doctorate, or Doktor Honoris Causa, from AGH University of Science and Technology in Krakow, Poland on March 18th.  AGH UST includes engineering disciplines, exact sciences, Earth sciences, and social sciences, with an emphasis on current priorities of economy and business, and it regularly ranks first among Polish technical universities in international rankings. Sangiovanni-Vincentelli, an expert in electronic design automation, co-founded both Cadence Design Systems and Synopsys, Inc.  He has also been awarded Honorary Doctorates by the combined EE and CS departments of the University of Aalborg in Denmark (2009) and from KTH in Sweden (2012).

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.