Andreea Bobu named 2021 Apple Scholar in AI/ML
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.
Machine Learning Research at Apple: Announcing the 2021 Apple Scholars in AI/ML
Wenshuo Guo wins 2021 Google PhD Fellowship
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.
Wenshuo Guo's home page
Michael Jordan explains why today's AI systems aren't actually intelligent
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.
IEEE Spectrum: Stop Calling Everything AI, Machine-Learning Pioneer Says
Rediet Abebe tackles inequality through algorithms
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.
Quanta Magazine: A Computer Scientist Who Tackles Inequality Through Algorithms
Joe Hellerstein named Datanami 2021 Person to Watch
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.
Meet 2021 Datanami Person to Watch Joe Hellerstein
Nir Yosef creates algorithm to integrate single-cell data from multiple sources
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.
Berkeley Engineering: TotalVI: A transformative algorithm
Data Limits Could Vanish With New Optical Antennas and "Rings of Light"
EECS Prof. Boubacar Kanté and his team have found a new way to harness properties of light waves that can radically increase the amount of data they carry. They demonstrated the emission of discrete twisting laser beams from antennas made up of concentric rings roughly equal to the diameter of a human hair, small enough to be placed on computer chips. The new work, reported in a paper published Thursday, February 25, 2021, in the journal Nature Physics, throws wide open the amount of information that can be multiplexed, or simultaneously transmitted, by a coherent light source. "It's the first time that lasers producing twisted light have been directly multiplexed," said Kanté. "We've been experiencing an explosion of data in our world, and the communication channels we have now will soon be insufficient for what we need. The technology we are reporting overcomes current data capacity limits through a characteristic of light called the orbital angular momentum. It is a game-changer with applications in biological imaging, quantum cryptography, high-capacity communications, and sensors."
SciTech Daily: Data Limits Could Vanish With New Optical Antennas and "Rings of Light"
EECS celebrates International Women's History Month
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.
Welcome to EECS Women's History Month!
EECS Graduate Spotlight: Xinyun Chen
Rediet Abebe to participate in NSF/CEME Decentralization 2021
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).
University of Pittsburgh, CASSI: NSF/CEME Decentralization 2021: Mechanism Design for Vulnerable Populations
Register for Decentralization 2021 (April 15-17)
New method of harnessing light waves radically increases amount of data transmitted
EECS Associate Prof. Boubacar Kanté and his research team have found a new way to harness properties of light waves that can radically increase the amount of data they carry. They demonstrated the emission of discrete twisting laser beams from antennas made up of concentric rings roughly equal to the diameter of a human hair, small enough to be placed on computer chips. Described in a paper published in Nature Physics, this new technology overcomes current data capacity limits through a characteristic of light called orbital angular momentum (OAM). Potential applications include biological imaging, quantum cryptography, high-capacity communications and sensors. "Having a larger quantum number is like having more letters to use in the alphabet," said Kanté. "We're allowing light to expand its vocabulary. In our study, we demonstrated this capability at telecommunication wavelengths, but in principle, it can be adapted to other frequency bands. Even though we created three lasers, multiplying the data rate by three, there is no limit to the possible number of beams and data capacity."
Berkeley Engineering: Light unbound: Data limits could vanish with new optical antennas
Nature Physics: Photonic quantum Hall effect and multiplexed light sources of large orbital angular momenta