News

New Sky Computing Lab aims to revolutionize the cloud industry

Sky Computing Lab, the latest 5-year collaborative research lab launched out of Berkeley EECS, aims to build a new backbone for interconnected cloud computing, a milestone that would revolutionize the industry. The lab will leverage distributed systems, programming languages, security, and machine learning to decouple the services that companies want to implement from the choice of a specific cloud, with the goal of transforming the cloud into an undifferentiated commodity, much like the Internet. Google, IBM, Intel, Samsung SDS, and VMware are among the founding sponsors of the lab. The lab's team is comprised of over 60 members, including students, staff, and EECS faculty like Alvin Cheung, Natacha Crooks, Ken Goldberg, Joseph Gonzalez, Joe Hellerstein, Mike Jordan,  Anthony Joseph, Raluca Ada Popa, Koushik Sen, Scott Shenker, and Dawn Song. CS Prof. Ion Stoica, who will lead the lab, says “Sky will knock out current barriers and accelerate the transition to the cloud, which will accelerate the progress across different fields.”

 

EECS faculty applaud graduates’ resilience

EECS Assistant Prof. Nika Haghtalab and CS Assistant Prof. and Associate Prof. in the School of Information, David Bamman, are quoted in a Computing, Data Science, and Society (CDSS) article about the resiliency and determination of the 2022 graduating class, particularly during the pandemic. “This generation of students has persevered, despite these global challenges, to forge a real community with their peers,” said Bamman. They also anticipated the ways the graduates will use their new skills to shape our collective future. “We need graduates who understand the technical methods of data science, their limitations and sources of bias, and the broader context in which information is used to drive policy, inform decision-making, and shape opinion,” Bamman said.  Haghtalab noted that “this is a great time to enter the workforce and contribute to the shaping of data science and computing for the advancement and betterment of the world.”

Angjoo Kanazawa wins Society of Helman Fellows Evergreen Fellowship

CS Assistant Prof. Angjoo Kanazawa has won the Society of Hellman Fellows Evergreen Fellowship.  The Society of Hellman Fellows is an endowed UC program administered by the Vice Provost for the Faculty that provides research funding "to promising assistant professors who show capacity for great distinction in their chosen fields."  Kanazawa's research lies at the intersection of computer vision, computer graphics, and machine learning. She is focused on building a system that can capture, perceive, and understand the complex ways that people and animals interact dynamically with the 3-D world--and can use that information to correctly identify the content of 2-D photos and video portraying scenes from everyday life.

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.

Kam Lau wins Caltech Distinguished Alumni Award

EECS Prof. Emeritus Kam Lau, has won the 2022 California Institute of Technology Distinguished Alumni Award, the highest honor presented by Caltech to its alumni.  He was cited "for extraordinary contributions to society as an engineer, entrepreneur, and artist." Lau is known for his pioneering developments and commercialization of RF over fiber devices, systems and applications, which helped launch the microwave photonics industry.  He received his B.S., M.S., and Ph.D degrees from Caltech in 1978, 1978 and 1981, respectively.  Before coming to Berkeley in 1990, he was founding chief scientist of Ortel Corporation, and a professor at Columbia University.  He subsequently  co-founded LGC Wireless with some of his Berkeley colleagues.  Lau is also an accomplished ink painting artist.  At age 16, his work was accepted into the 1972 Hong Kong Contemporary Art Exhibition, a venue for professional artists, and one of his pieces was acquired by the Hong Kong Museum of Art for its permanent collection.

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.