Prof. Raluca Ada Popa

Raluca Ada Popa wins 2021 ACM Grace Murray Hopper Award

EECS Associate Prof. Raluca Ada Popa is the recipient of the Association for Computing Machinery (ACM) Grace Murray Hopper Award.  This award recognizes an outstanding young computer professional who has made a single recent major technical or service contribution to the field of computer science before the age of 35.  Popa was recognized for her work in the area of design of secure distributed systems, specifically systems that "protect confidentiality against attackers with full access to servers while maintaining full functionality."  Her approach focuses on protecting the confidentiality of data stored on remote servers by providing confidentiality guarantees for areas where servers need to store encrypted data, thus allowing data to be processed without decrypting.  Although computing on encrypted data is still only theoretical, Popa's solution involves building systems for a broad set of applications with common traits, and then utilizing encryption schemes on just these traits so that they can perform most computations on encrypted data.  Some of her systems have been adopted into or inspired systems such as SEEED of SAP AG, Microsoft SQL Server’s Always Encrypted Service, and others.  The award comes with a prize of $35,000.

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.

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.

‘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.

Scott Shenker National Academy of Sciences

Scott Shenker wins 2022 Fiat Lux Faculty Award

CS Prof. Emeritus and Prof. in the Graduate School Scott Shenker has won the 2022 UC Berkeley Fiat Lux Faculty Award.  This achievement award, which is co-presented by the UC Berkeley Foundation and the Cal Alumni Association, recognizes a "faculty member whose extraordinary contributions go above and beyond the call of duty to advance the university’s philanthropic mission and transform its research, teaching, and programs."  Shenker, who is the Research Director of Extensible Internet at the International Computer Science Institute (ICSI), is known for his research contributions in the areas of energy-efficient processor scheduling, resource sharing, and software-defined networking.  He is a leader in the software-defined networking (SDN) technology movement and a co-founder of the open-source non-profit Open Networking Foundation (ONF), which sets standards and promotes SDN in anticipation of problems that arise when cloud computing blurs distinctions between computers and networks.  Shenker is also known for his philanthropic support of the university, including a donation of $25M toward the construction the new Division of Computing, Data Science, and Society (CDSS) building last June.  The award will be presented at the Berkeley charter Gala on May 12th.

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.

Marti Hearst is named iSchool's new head of school

CS Prof. and alumna Marti Hearst (B.A. '85/M.S. '89/Ph.D. '94, advisor: Robert Wilensky) has been named the new head of school for UC Berkeley's School of Information (iSchool).   Hearst, who was the iSchool's first assistant professor in 1997, is taking over the position from CS Prof. Hany Farid.  She will manage the day-to-day operations of the unit, which is an affiliate of the Division of Computing, Data Science, and Society (CDSS),  and communicate its vision on and off campus.  Hearst is known for her work automating sentiment analysis and word sense disambiguation. She invented an algorithm known as “Hearst Patterns," which is used in commercial text mining operations, and developed a now commonly-used automatic text segmentation approach called TextTiling.   She will serve as head of school through June 30, 2023.