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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.

Putri Karunia's Typedream allows users to build no-code websites

EECS alumna Putri Karunia (B.S. '19) who co-founded 2022 Forbes 30-Under-30 Enterprise Tech company "Typedream," is the subject of a profile titled "Putri Karunia proves that women not only belong in tech startups, but will actually make them more successful and profitable." Karunia, who was raised in Indonesia, graduated cum laude from Cal in 2019 and joined a team that included fellow EECS student Anthony Christian (B.S. '19) to found start-up Cotter, a passwordless authentication service that allows users to add a one-tap login to websites and apps in less than 15 minutes.  While developing Cotter, they came up with the idea for Typedream, a fast, user-friendly website-building tool that enables Notion (platform) customers to publish attractive websites in just 10 minutes, without prior coding experience. The design offers an intuitive text-editing interface with enriched web3 functionality, like gradients, blur navigation bars, cards, and text or buttons over images. "With a community-driven approach, our users help us prioritize the features we build and define our roadmap for the foreseeable future," said Karunia. "Listening and observing our community also led us to see glimpses of what the web could be like in the next 5-10 years."

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.

Chase Norman selected to participate in the Heidelberg Laureate Forum

CS undergraduate student Chase Norman is among 200 young mathematics and computer science researchers selected from across the globe to attend the 9th Heidelberg Laureate Forum (HLF) in Germany this September. During the week-long conference, participants will share ideas with some of the "most exceptional mathematicians and computer scientists of their generations," namely the recipients of some of the field’s most prestigious awards: the Abel Prize, ACM A.M. Turing Award, ACM Prize in Computing (won this year by Berkeley CS Prof. Pieter Abbeel), Fields Medal, and Nevanlinna Prize. Participants and laureates will interact through a blend of scientific and social activities that are designed to foster a relaxed atmosphere and encourage scientific exchange.  Participants are selected by a panel of international reviewers on the basis of their research experience, social engagement skills, and letter of motivation.”. Norman is a CS and Math double major who was admitted to the EECS Honors Program in the breadth area of Mathematical Logic and Foundations.  He is also the president of the CS honor society Upsilon Pi Epsilon, was course staff for CS 170 and CS 61A, and was a percussionist with UC Jazz and the UCB Symphony Orchestra.

Dave Epstein wins 2022 Paul & Daisy Soros Fellowship

CS graduate student Dave Epstein (advisor: Alexei Efros) has won a 2022 Paul & Daisy Soros Graduate Fellowship for New Americans.  This fellowship recognizes outstanding graduate students who are immigrants and children of immigrants in the United States, and "who are poised to make significant contributions to US society, culture or their academic field."  Epstein is affiliated with the Berkeley AI Research (BAIR) Lab where he is teaching machines to solve visual problems without labels, and enabling a creative understanding of the real world to emerge. He is also interested in language, machine learning, synthesis, and interaction. Paul & Daisy Fellowships come with a $90K award.

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.

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.