Campus Shutdown Notice

In light of the ongoing coronavirus (COVID-19) situation, we have decided to close our administrative offices starting Monday, March 16, 2020 until further notice.  Cory and Soda Hall are closed.  Classes are being held remotely.  All events in Cory and Soda Halls will either be cancelled or held remotely, and staff will be working remotely during this time.

Rediet Abebe co-chairing ACM Conference on Equity and Access in Algorithms, Mechanisms, & Optimization

CS Assistant Prof. Rediet Abebe is co-chairing the inaugural ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ’21) in October 2021.  This conference will highlight work where techniques from algorithms, optimization, and mechanism design, along with insights from other disciplines, can help improve equity and access to opportunity for historically disadvantaged and underserved communities.  Launched by the Mechanism Design for Social Good (MD4SG) initiative, it will feature keynote talks and panels, and contributed presentations of research papers, surveys, problem pitches, datasets, and software demonstrations.   The submission deadline is June 3, 2021.

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.

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.

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.

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.

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).

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.”

Alvin Cheung and Jonathan Ragan-Kelley win 2020 Intel Outstanding Researcher Award

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 wins 2021 IEEE RAS Early Career Award

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.

Scott Aaronson, Manuel Blum, Shafi Goldwasser and Stuart Russell among Top 20 Influential Computer Scientists

CS alumnus Scott Aaronson (Ph.D. '04, advisor: Umesh Vazirani) ranked #4, Prof. Emeritus Manuel Blum ranked #11, alumna and Prof. Shafi Goldwasser (M.S. '81/Ph.D. '84, advisor: Manuel Blum) ranked #12, and Prof. Stuart Russell ranked #14 on Academic Influence's list of the Top Influential Computer Scientists from 2010 to 2020.  Scholars are ranked using a methodology that includes the number of citations, as well as their web presence,  to determine their impact and influence over society in the past 10 years: "Some have had revolutionary ideas, some may have climbed by popularity, but all are academicians primarily working in computer science."  Aaronson, now at the University of Texas, Austin, is one of the world's leading experts in quantum computing; Blum, now at Carnegie Mellon, works on the theoretical underpinnings of programming and algorithms, notably computational complexity theory, cryptography, and program verification; Goldwasser is an expert in computational complexity theory, cryptography, and number theory; and Russell, the author of the most popular textbook on Artificial Intelligence, is an expert in machine learning and reasoning, and a major proponent of provably beneficial AI.