CS Prof. Ken Goldberg is the co-author of a study published in Science Robotics which describes the creation of a new artificial intelligence software that gives robots the speed and skill to grasp and smoothly move objects, making it feasible for them to soon assist humans in warehouse environments.  He and postdoc Jeffrey Ichnowski had previously created a Grasp-Optimized Motion Planner that could compute both how a robot should pick up an object and how it should move to transfer the object from one location to another, but the motions it generated were jerky.  Then they, along with EECS graduate student Yahav Avigal and undergraduate (3rd year MS) student Vishal Satish, integrated a deep learning neural network into the motion planner, cutting the average computation time from 29 seconds to 80 milliseconds, or less than one-tenth of a second.  Goldberg predicts that, with this and other advances in robotic technology, robots could be assisting in warehouse environments in the next few years.

Since computers cannot store unlimited amounts of data, it is important to be able to quickly extract patterns in that data without having to remember it in real time. CS Prof. Jelani Nelson, who is profiled in a Q&A session for Quanta magazine, has been expanding the theoretical possibilities for low-memory streaming algorithms using a technique called sketching, which compresses big data sets into smaller components that can be stored using less memory and analyzed quickly.  He has used this technique to help devise the best possible algorithm for monitoring things like repeat IP addresses accessing a server.  “The design space is just so broad that it’s fun to see what you can come up with,” he said.  Nelson also founded AddisCoder, a free summer program which has taught coding and computer science to over 500 high school students in Addis Ababa, Ethiopia.  "A lot of the students have never been outside of their town, or their region," he said.  "So AddisCoder is the first time they’re seeing kids from all over the country, and then they’re meeting instructors from all over the world.  It’s very eye-opening for them."

EECS grad student and alumnus Jake Tibbetts (B.S. EECS/Global Studies '20) has won the Bulletin of the Atomic Scientists’ 2020 Leonard M. Rieser Award.   Winners of the award have published essays in the Bulletin's Voices of Tomorrow column, and are selected by the Bulletin’s editorial team for recognition as "outstanding emerging science and security experts passionate about advancing peace and security in our time."  Tibbetts received the award for his article “Keeping classified information secret in a world of quantum computing,” published in the Bulletin on February 11, 2020.  “In his piece, Jake Tibbetts accomplished the kind of deep, thoughtful, and well-crafted journalism that is the Bulletin's hallmark," said editor-in-chief John Mecklin. "Quantum computing is a complex field; many articles about it are full of strange exaggerations and tangled prose. Tibbetts' piece, on the other hand, is an exemplar of clarity and precision and genuinely worthy of the Rieser Award.”  Tibbetts is a fellow at the NNSA-supported Nuclear Science and Security Consortium, and has previously worked as a research assistant at the LBNL Center for Global Security Research.  He has made contributions to the Nuclear Policy Working Group and the Project on Nuclear Gaming at Cal, and made the EECS news last year for his involvement in creating the online three-player experimental wargame "SIGNAL," which was named the Best Student Game of 2019 by the Serious Games Showcase and Challenge (SGS&C).  The Rieser Award comes with a $1K prize.

EECS Prof. Ruzen Bajcsy has won the 2021 Institute of Electrical and Electronics Engineers (IEEE) Medal For Innovations In Healthcare Technology.  The award is presented "for exceptional contributions to technologies and applications benefitting healthcare, medicine, and the health sciences."  Bajcsy, who has done seminal research in the areas of human-centered computer control, cognitive science, robotics, computerized radiological/medical image processing and artificial vision, was cited “for pioneering and sustained contributions to healthcare technology fundamental to computer vision, medical imaging, and computational anatomy.” In addition to her significant research contributions, Bajcsy is also known for her leadership in the creation of the University of Pennsylvania's General Robotics and Active Sensory Perception (GRASP) Laboratory, globally regarded as a premiere research center.  She is especially known for her comprehensive outlook in the field, and her cross-disciplinary leadership in successfully bridging the once-diverse areas of robotics, artificial intelligence, engineering and cognitive science.  EECS Prof. Thomas Budinger previously received the Health Care Innovations medal in 2018.

CS Prof. Michael I. Jordan has been awarded the 2021 American Mathematical Society (AMS) Ulf Grenander Prize in Stochastic Theory and Modeling.   The prize, which was established in 2016, recognizes "exceptional theoretical and applied contributions in stochastic theory and modeling." It is awarded for "seminal work, theoretical or applied, in the areas of probabilistic modeling, statistical inference, or related computational algorithms, especially for the analysis of complex or high-dimensional systems." Jordan, who has a split appointment in Statistics, was cited for "foundational contributions to machine learning, especially unsupervised learning, probabilistic computation, and core theory for balancing statistical fidelity with computation."  He is known for his work on recurrent neural networks as a cognitive model in the 1980s, formalizing various methods for approximate interference, and popularizing Bayesian networks and the expectation-maximization algorithm in machine learning.  The prize is awarded every three years, making Jordan the second recipient of the honor.

CS Prof. and alumna Dawn Song (Ph.D. '02, advisor: Doug Tygar) has won the 2020 ACM Special Interest Group on Security, Audit and Control (SIGSAC) Outstanding Innovation Award.  This award recognizes "outstanding and innovative technical contributions to the field of computer and communication security that have had lasting impact in furthering or understanding the theory and/or development of commercial systems."  Song was cited "for contributions to systems and software security, in particular, dynamic taint analysis for vulnerability discovery and malware detection."  She pioneered the BitBlaze Binary Analysis Infrastructure, a unified binary program analysis platform used to provide novel solutions to computer security problems, including automatic vulnerability discovery and defense, in-depth malware analysis, and automatic extraction of security models for analysis and verification.

CS Assistant Prof. Natacha Crooks has won the 2020 ACM Special Interest Group on Operating Systems (SIGOPS) Dennis M. Ritchie dissertation award for her thesis titled "A Client-Centric Approach to Transactional Datastores."  The award, which recognizes creative research in software systems, was bestowed upon a dissertation which a colleague described as "a landmark, with deep and beautiful results in transactions and distributed consistency, and systems that exploit them."  The award committee commented that "Natacha Crooks’ thesis achieves something rare: a new conceptual framework for client-centric consistency and two efficient systems built on those insights. The document for this attractive package is accessible (in part) to undergraduates and the advanced material is very clearly written. With the enduring popularity of consistency as a research topic in distributed systems for the past several decades it is surprising that a breakthrough as large as Natacha’s took as long as it did."  The work was done at the University of Texas, Austin, advised by Lorenzo Alvisi and Simon Peter.

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.