EECS graduate student Gabriel Fierro (B.S. c. 2014, Ph.D. advisor: David Culler) has won an inaugural Google - CMD-IT FLIP Dissertation Fellowship.   He is one of 11 computer science scholars from underrepresented groups who were recognized this year for "positively influencing the direction and perspective of technology."   The award is part of a joint effort by Google Research, the Computing Alliance of Hispanic-Serving Institutions (CAHSI), and the Center for Minorities and People with Disabilities in Information Technology (CMD-IT) Diversifying Future Leadership in the Professoriate (FLIP) Alliance to increase the diversity of doctoral graduates in computing.  After completing his Ph.D., Fierro aspires to "a faculty position in a computer science department where I am able to pursue non-traditional and cross-disciplinary approaches to long-standing problems of sustainability and the built environment."  Fierro is currently working on the Buildings, Energy and Transportation Systems project in conjunction with the RISE Lab.

"Extreme MRI: Large‐scale volumetric dynamic imaging from continuous non‐gated acquisitions,” a paper by EECS alumnus Frank Ong (B.S. '13, Ph.D. '18) and his advisor, Prof. Miki Lustig, has been chosen as October's Reproducible Research pick by the International Society for Magnetic Resonance in Medicine (ISMRM).  The paper, in which the researchers attempt to reconstruct a large-scale dynamic image dataset while pushing reconstruction resolution to the limit, was chosen "because, in addition to sharing their code, the authors also shared a demo of their work in a Google Colab notebook."  Lustig and Ong, now a research engineer at Stanford, participated in a Q&A in which they discussed how they became interested in MRI, what makes Extreme MRI "extreme," the culture and value of open science, and why Lustig's grad school paper on compressed sensing became the most cited paper in MRM.  ISMRM is an international nonprofit association that promotes research development in the field of magnetic resonance in medicine to help facilitate continuing education in the field.

EECS Prof. Emeritus Michael Stonebraker has won the prestigious NEC Computers and Communications (C&C) Prize "For Pioneering Contributions to Relational Database Systems." The prize is awarded "to distinguished persons in recognition of outstanding contributions to research and development and/or pioneering work in the fields of semiconductors, computers, and/or telecommunications and in their integrated technologies."  In the early 1970's, Stonebraker and Prof. Eugene Wong began researching Relational Database Management Systems (RDBMS), which culminated in the creation of the Interactive Graphics and Retrieval System (INGRES), a practical and efficient implementation of the relational model running on Unix-based DEC machines.  It included a number of key ideas still widely used today, including B-trees, primary-copy replication, the query rewrite approach to views and integrity constraints, and the idea of rules/triggers for integrity checking in an RDBMS.  Stonebraker, Wong, and Prof. Larry Rowe, founded a startup called Relational Technology, Inc. (renamed Ingres Corporation), which they sold to Computer Associates in the early 1990's for $311M.  Stonebraker's student, Robert Epstein (Ph.D. '80), founded the startup Sybase, which created the code used as a basis for the Microsoft SQL Server.  Stonebraker also created Postgres in the late 1980's, which made it easier for programmers to modify or add to the optimizer, query language, runtime, and indexing frameworks.  It broadened the commercial database market by improving both database programmability and performance, making it possible to push large portions of a number of applications inside the database, including geographic information systems and time series processing.  Stonebraker retired from Berkeley in 2000 to found more companies and become an adjunct professor at MIT.  His achievements have been recognized with an IEEE John von Neumann Medal in 2005, ACM A.M. Turing Award in 2014, and ACM SIGMOD Systems Award in 2015.

EECS Prof. Katherine Yelick has won The Berkeley Lab Citation, the Lawrence Berkeley National Laboratory (LBNL) Director's Award for Exceptional Achievement which "honors extraordinary achievement(s) in broad categories of science and operations, with special focus on service to the Lab and/or the DOE National Lab Complex."  Yelick was cited for "extraordinary leadership both within the Lab and at the national level, including her significant role in developing DOE strategy in Exascale and Quantum Computing, Big Data, and Artificial Intelligence."  Yelick is the Senior Advisor on Computing at LBNL and the Associate Dean for Research in UC Berkeley's new Division of Computing, Data Science and Society (CDSS).  She was the Associate Laboratory Director for Computing Sciences at LBNL from 2010 through 2019, and led the National Energy Research Scientific Computing Center (NERSC) prior to that.  Her research focuses on high performance computing, programming languages, compilers, parallel algorithms, and automatic performance tuning. She currently leads the ExaBiome project on scalable tools for analyzing microbial data and co-leads the Berkeley Benchmarking and Optimization (Bebop) group.

New EECS Assistant Profs. Sophia Shao and Alp Sipahigil have received Engineering faculty fellowships, which will help fund the first five years of their projects and labs at Berkeley.   The fellowships are sponsored by Berkeley Engineering alumni and friends as part of a $1.25M program that will be shared among five new faculty.  Shao, who began teaching at Berkeley in 2019, studies computer architecture with a special focus on specialized accelerator, heterogeneous architecture and agile VLSI design methodology.  Sipahigil, who will arrive in spring 2021 from Caltech, has been focused on using nanoscale phononic and photonic structures to bring new functionalities to superconducting quantum circuits.

CS Assistant Prof. Alessandro Chiesa has been selected as a 2020 Okawa Foundation Research Grant recipient for his work on the "Foundations of Quantum and Non-Signaling Proofs (Post-Quantum Zero-Knowledge Proofs for Secure Distributed Systems)."  Okawa Research Grants are awarded to Asian and American scholars for studies and analyses in the fields of information and telecommunications.  Winners receive a $10K prize which is usually awarded in an autumn ceremony in San Francisco, but the event has been cancelled this year because of COVID-19.

CS Prof. Michael I. Jordan is featured in This Week in Machine Learning & AI (TWIML AI) Podcast episode #407 titled "What are the Implications of Algorithmic Thinking? with Michael I. Jordan."   He discusses his current exploration into the intersection of economics and AI, and how machine learning systems could be used to create value and empowerment across many industries through “markets.”  The interview also touches on the potential of “interacting learning systems” at scale, the valuation of data, and the commoditization of human knowledge into computational systems.  Jordan's career, and the ways it has been influenced by other fields like philosophy, is also explored.  Jordan received the 2020 IEEE John von Neumann Medal for "outstanding achievements in computer-related science and technology" earlier this year.

EECS alumnus John S. Davis II (Ph.D. '00, advisor: Edward Lee) will be participating in the Black Engineering and Science Alumni Club (BESAC)'s homecoming week panel on "Black in STEM -  in the face of two pandemics."  This virtual moderated panel, which will be held on October 17th,  will discuss the impact that both the CoVID-19 pandemic and the events underlying the Black Lives Matter movement have had on the Black community.   Davis is a senior privacy engineer at Google where he has published work to aid CoVID-19 researchers in datamining symptom search terms in Google while simultaneously protecting user privacy.  He joined Google in 2019 after eight years as a senior information scientist at the Rand Corporation, and seven years as a staff researcher at IBM’s Watson Research Center.  The panel will discuss topics ranging from engineering projects by UC Berkeley alumni and faculty to meet the moment of the CoVID-19 pandemic, efforts to address the disparate effects of CoVID-19 on the Black community, and wide-ranging initiatives to redress the impacts of systemic racism.   Registration is required to receive the Zoom log-in.

The National Science Foundation (NSF) and the Simons Foundation Division of Mathematics and Physical Sciences are partnering to award $10 million to fund research in the Mathematical and Scientific Foundations of Deep Learning, led by CS Prof. Peter Bartlett and EECS Prof. Bin Yu.  Both professors hold joint appointments in the Department of Statistics.  The researchers hope to gain a better theoretical understanding of deep learning, which is part of a broader family of machine learning methods based on artificial neural networks that digest large amounts of raw data inputs and train AI systems with limited human supervision. Most of the research and education activities will be hosted by the Simons Institute for the Theory of Computing, in the form of structured programs of varying themes.  Other participating institutions will include Stanford, MIT, UCI, UCSD, Toyota Tech in Chicago, EPFL in Switzerland, and the Hebrew University in Israel.