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.

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).

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."

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.