EECS graduate student Andreea Bobu (advisor: Anca Dragan) has been named a 2021 Apple Scholar in AI and Machine Learning (AI/ML).  The scholarship was created by Apple to "celebrate the contributions of students pursuing cutting-edge fundamental and applied machine learning research worldwide."  Bobu's research interests lie at the intersection of machine learning, robotics, and human-robot interaction, with a focus in robot learning with uncertainty. She is particularly interested in the ways in which autonomous systems’ models of the world and of other agents (e.g. humans) can go wrong, and is devising ways to enhance interaction between people and robots.  She earned her BS in Computer Science and Engineering at MIT in 2017, where she worked on probabilistic models for medical image analysis.  She is currently associated with the Berkeley Artificial Intelligence Research (BAIR) lab.

EECS graduate student Wenshuo Guo (advisor: Michael I. Jordan) has won a 2021 Google PhD Fellowship in Algorithms, Optimization and Markets.  This award acknowledges and supports exemplary PhD students in computer science and related fields who are making contributions to their areas of specialty.   Guo studies robustness guarantees in algorithms and machine learning foundations, as well as their impact on society.  She is also interested in the intersection of CS and economics, and is currently focused on mechanism design, causal inference, and statistical questions in reinforcement learning. The award, which will cover full tuition, fees, and a stipend for the 2021-22 school year, will be presented at the Global Fellowship Summit over the summer.
 

CS Prof. Michael I. Jordan is the subject of an IEEE Spectrum article which describes his life, research, and philosophy.  A computer science pioneer, Jordan blended CS, statistics, and applied mathematics, to help transform unsupervised machine learning into a powerful algorithmic tool for solving problems in fields like natural language processing, computational biology, and signal processing.  He explains that machine learning is, in essence, a new field of engineering focused on the interface between people and technology.  The optimal goal of machine learning should not be artificial imitation of human thinking since that is something human beings can already do for themselves.  Instead, AI should be focused on helping humanity solve the problems that it has created.  “While the science-fiction discussions about AI and super intelligence are fun, they are a distraction,” Jordan says. “There’s not been enough focus on the real problem, which is building planetary-scale machine learning–based systems that actually work, deliver value to humans, and do not amplify inequities.

CS Assistant Prof. Rediet Abebe is the subject of a profile in Quanta Magazine which describes how she uses the tools of theoretical computer science to understand pressing social problems -- and try to fix them.   Abebe, who is from Ethiopia, earned a B.A. in mathematics from Harvard, attended a one-year intensive math program at Cambridge, and switched to Computer Science at Cornell where she earned her Ph.D.   She was drawn to CS because it allowed her to apply mathematical thinking to social problems like discrimination, inequity and access to opportunity.  Abebe has co-founded two organizations: Black in AI, a community of Black researchers working in artificial intelligence, and Mechanism Design for Social Good, which brings together researchers from different disciplines to address social problems. The Q&A interview discusses her life and career choices, as well as her research and its applications.

CS Prof. Joseph Hellerstein has been named a Datanami 2021 Person to Watch.  Hellerstein is the chief strategy officer and one of the co-founders  a Trifacta, a company which markets data preparation and interaction technology based on Data Wrangler, a data transformation and discovery tool he developed in the RISELab at Berkeley with some colleagues from Stanford.  He is the subject of a Datanami article in which he discusses the state of data science education, the next wave of data, and the secrets of his success.

CS Associate Prof. Nir Yosef has joined with colleagues in Bioengineering to write an algorithm called totalVI that uses deep learning to integrate gene and protein data about single cells, and which will allow collaborative experiments to be more accurate and efficient.   TotalVI will help to manage, analyze, and distribute gene and protein data about single cells that were gathered from different tissues and donors, and that were processed in different labs, into a single organizational system.  “The combination of CITE-seq (an RNA sequencing technique) and totalVI allows us to estimate, from the same cell, not only its gene expression but also the expression of the cell membrane proteins,” said Yosef.  “Those tell us a lot about the biology of the cells, since working with these proteins is kind of the standard in immunology.”  The new algorithm will enable researchers to integrate single-cell datasets from labs around the world, and will aid the progression of global knowledge bases.

In an effort to facilitate the conversation about diversity and inclusion in the field of EECS, undergraduate students Neha Hudait and Prachi Deo have put together a web page and calendar of events for March 2021 and beyond.  The web page will feature a series of profiles, the first of which is of EECS graduate student Xinyun Chen, who is working with Prof. Dawn Song at the intersection of deep learning, programming languages, and security.  Their events are organized around a different theme every week, and will encompass community building, the tech industry, academia, personal projects, and achievements in tech.  They will also host daily giveaways and social media challenges, and encourage everyone in the community to join in the celebration.

CS Assistant Prof. Rediet Abebe will be moderating a problem solving session at the 2021 NSF/CEME Decentralization Conference.  The theme of this year's conference is "Mechanism Design for Vulnerable Populations." Abebe's session will be designed to help academics understand the challenges facing refugees and practitioners working on refugee issues globally, and to facilitate a dialog between these practitioners and experts in the academic community. Abebe is co-founder and co-organizer of the multi-institutional, interdisciplinary research initiative Mechanism Design for Social Good (MD4SG).  The 2021 conference will be hosted in April by the Center for Analytical Approaches to Social Innovation (CAASI) in the Graduate School of Public and International Affairs (GSPIA) at the University of Pittsburgh.  The conference series is funded by a grant from the National Science Foundation (NSF) in support of Conferences on Econometrics and Mathematical Economics (CEME), and administered through the National Bureau of Economic Research (NBER).

EECS Assistant Profs. Alvin Cheung and Jonathan Ragan-Kelley are among 18 winners of Intel's 2020 Outstanding Research Awards (ORA). These awards recognize exceptional contributions made through Intel university-sponsored research.  Cheung and Ragan-Kelley are developing ARION, a system for compiling programs onto heterogeneous platforms. The team will use verified lifting, which rewrites legacy code into a clean specification, stripping away optimizations that target legacy architectures. This spec, written in a DSL, can then be compiled to new platforms, sometimes with orders of magnitude of speedup in resulting code performance.

Anca Dragan has won the 2021 IEEE Robotics and Automation Society Early Career Award - Academic "For pioneering algorithmic human-robot interaction."  This award is bestowed on current members of IEEE who are in the early stage of their career, and who have made an identifiable contribution or contributions which have had a major impact on the robotics and/or automation fields.  Dragan runs the InterACT lab and is the principal investigator for the Center for Human-Compatible AI.  Her research explores ways to enable robots to work with, around and in support of people, autonomously generating behavior in a way that formally accounts for their interactions with humans.