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.

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.

Steven Conolly awarded 2022 Bakar Prize

EECS and Bioengineering Prof. Steven Conolly has been awarded the 2022 U.C. Berkeley Bakar Prize.  This prize is given annually to former Bakar Program Fellows whose technological innovations promise to deliver solutions to some of the world’s most pressing problems.  Funds are provided to help new technologies transition from an academic setting to industrial applications.  The objective of Conolly's project, titled Rapid in vivo optimization of solid tumor CAR-T cell therapies using advanced magnetic particle imaging (MPI),  is to determine whether a particular CAR-T cell cancer immunotherapy is working in hours rather than months.  CAR-T cells are tagged with safe magnetic nanoparticles before a treatment is administered so that oncologists can view how well they are targeting cancer cells using high resolution imaging technology.

Colin Parris elected to the NAE

EE alumnus Colin Parris (M.S. '87, Ph.D. '94, advisor: Domenico Ferrari) has been elected to the National Academy of Engineering (NAE).  After a career at IBM Systems & Technology and General Electric (GE) Research, Parris is currently Senior Vice President and Chief Technology Officer at GE.  He is known for his life-long commitment to "the development and enhancement of STEM programs across minority communities," and serves as a board member of the Annual Multicultural Business Youth Educational Services Embarkment (Ambyese), which prepares multicultural secondary school students for the challenges of pursuing careers in the corporate sector through self-esteem-building and exposure to successful role models in industry.  While a student Berkeley, Parris helped start the Summer Undergraduate Program in Engineering Research at Berkeley (SUPERB) and was deeply involved with the group Black Graduate Engineering and Science Students (BGESS).  At GE, Parris, whose expertise spans engineering, software, and AI-driven analytics, leads teams that leverage digital technologies in the energy industry and other industrial environments.  He created and leads the Digital Twin Initiative company-wide and is currently working to "accelerate business impact and transformation by combining lean principles with digital solutions."

Kathy Yelick wins 2022 CRA Distinguished Service Award

EECS Prof. Katherine Yelick has won the 2022 CRA Distinguished Service Award.  This award recognizes "a person or organization that has made an outstanding service contribution" with a major impact "to the computing research community" in the areas of government, professional societies, publications, conferences, or leadership.  Yelick has been a professor in the department since 1991,  and was the Associate Laboratory Director for Computing Sciences at Lawrence Berkeley National Laboratory (LBNL).  She is known as the co-inventor of the UPC and Titanium languages and demonstrated their applicability through the use of novel runtime and compilation methods.  She also co-developed techniques for self-tuning numerical libraries.  She is the co-author of two books and more than 100 refereed technical papers on parallel languages, compilers, algorithms, libraries, architecture, and storage.

Avishay Tal named 2022 Sloan Research Fellow in Computer Science

CS Assistant Prof. Avishay Tal has been selected as a 2022 Alfred P. Sloan Research Fellow in Computer Science.   This award recognizes outstanding early-career faculty for their "potential to revolutionize their fields of study."  Tal is a member of the Theory group;  his interests include computational complexity, analysis of boolean functions, circuit and formula lower bounds, query complexity, pseudorandomness, computational learning theory, quantum computing, combinatorics, and connections between algorithms and lower bounds.  He is among 4 winners from UC Berkeley representing the fields of CS, math, physics, and neuroscience.  Winners receive $75K, which may be spent over a two-year term to support their research.