Ming Wu and Steven Conolly named Bakar Fellows

EECS Profs. Ming Wu and Steven Conolly been selected for the Bakar Fellows Program, which supports faculty working to apply scientific discoveries to real-world issues in the fields of engineering, computer science, chemistry and biological and physical sciences.  Wu's fellowship support will be used accelerate commercialization of his invention: a high-performing silicon photonic switch for data center networks.  Conolly's laboratory is developing a high-resolution three-dimensional imaging method, Magnetic Particle Imaging, which does not use any radiation and has unprecedented sensitivity.

Nancy Amato is first woman to lead UI computer science department

CS alumna Nancy Amato (M.S. '88, advisor: Manuel Blum) has been chosen to lead the highly ranked University of Illinois Department of Computer Science — the first woman to hold that position.  She will oversee a fast-growing department that has 80 faculty members and more than 2,400 students, plus 700 online, and is ranked fifth in the nation by U.S. News and World Report.  As a professor at Texas A&M, Amato's research focused on motion planning in robotics, parallel algorithms and bio-informatics.  She led an influential group within the Computing Research Association (CRA) to bring more women into the field and runs an undergraduate summer research program that matches students from underrepresented groups with faculty members. She received the CRA Habermann Award in 2014 for her efforts to involve more women and underrepresented minorities in computing research.

Dawn Tilbury: Shaping engineering research

EECS alumna Dawn Tilbury (M.S. '92/Ph.D. '94) is the subject of a Berkeley Engineering profile in honor of  the campus's year-long 150th anniversary celebration.  As head of the National Science Foundation's (NSF) Directorate for Engineering, which provides academic institutions with more than 40% of the federal grants for fundamental engineering research, Tilbury exemplifies the type of leadership nurtured through a Berkeley Engineering education.  “As the primary funder of basic research, NSF is uniquely positioned to bring people together to discover new approaches to renewable energy, reliable transportation, enhanced health and safety, and other national challenges," she said.

RAFAR wins Best Student Paper Award at MARSS 2018

"Bidirectional thin-film repulsive-/attractive-force electrostatic actuators for a crawling milli-robot," written by recent EE alumnus Ethan Schaler (Ph.D. '18), his advisor Prof. Ron Fearing, and two undergraduates from other departments (Loren Jiang in BioE and Caitlyn  Lee in E3S), received the Best Student Paper Award  from the International Conference on Manipulation, Automation, and  Robotics at Small Scales (MARSS) 2018 in Nagoya, Japan in July. The authors demonstrated a new thin-film electrostatic actuator (RAFA)  capable of generating bidirectional repulsive- and attractive-forces:  156 Pa in repulsion and 352 Pa in attraction, when operating at up to  1.2 kV. They used this actuator to power RAFAR, a 132 mg milli-robot  that crawls at 0.32 mm/s with anisotropic friction feet.   Schaler will be joining NASA Jet Propulsion Laboratory (JPL) this summer.

Maxim Rabinovich named 2018 Hertz-Gates Fellow

CS Ph.D. student Maxim Rabinovich (joint advisors: Michael Jordan and Daniel Klein) has received a 2018 Fannie and John Hertz Foundation Hertz-Gates Fellowship in Global Health and Development.  Rabinovich is currently researching machine learning and natural language processing, and is interested in developing artificial intelligence tools that support and extend human reasoning. Recent work in this direction includes projects on minimax theory for multiple testing, code generation from natural language specifications, fine-grained entity typing, and function-specific mixing rates for MCMC.  Rabinovich's work has been supported by the Hertz Foundation since 2015.

Larry Nagel wins IEEE Donald O. Pederson Award in Solid-State Circuits

EECS alumnus Larry Nagel (B.S. '69/M.S. '70/Ph.D. '75) has won the 2019 Institute of Electrical and Electronics Engineers (IEEE) Donald O. Pederson Award in Solid-State Circuits, named for his graduate advisor EECS Prof. Donald O. Pederson.  The award recognizes outstanding contributions to solid-state circuits and has previously been presented to five EECS professors: Paul Gray, Robert Brodersen, Ping Ko, Chenming Hu and Robert Meyer.  Nagel was cited "for the development and demonstration of SPICE as a tool to design and optimize electronic circuits."  His Ph.D. dissertation was on SPICE2 and he founded Omega Enterprises in 1998 to consult on analog circuit design, circuit simulation, and semiconductor device modeling.

Andrea Goldsmith wins IEEE Eric E. Sumner Award

2018 Distinguished Alumna Andrea Goldsmith (B.A. '86/M.S. '91/Ph.D. '94, advisor: Pravin Varaiya) has won the IEEE Eric E. Sumner Award "for contributions to the fundamental understanding and innovation in adaptive and multiple antenna techniques for wireless communication networks."    The Sumner Award is sponsored by Nokia Bell Labs and recognizes outstanding contributions to communications technology. Goldsmith, who is the Stephen Harris Professor of Electrical Engineering in the School of Engineering at Stanford University, is an expert in the design, analysis and fundamental performance limits of wireless systems and networks, and in the application of communication theory and signal processing to neuroscience.

Chelsea Finn is one of MIT TR's 2018 35 Innovators Under 35

CS PhD student Chelsea Finn (advisers: Pieter Abbeel and Sergey Levine) has been named to MIT Technology Review's 2018 list of "35 Innovators Under 35," an honor which recognizes "exceptionally talented young innovators whose work we believe has the greatest potential to transform the world."  Finn is cited in the Pioneers category because "her robots act like toddlers—watching adults, copying them in order to learn."  She works in the Berkeley AI Research Lab (BAIR) developing robots that can learn just by observing and exploring their environment. Her algorithms require much less data than is usually needed to train an AI—so little that robots running her software can learn how to manipulate an object just by watching one video of a human doing it. “In many ways, the capabilities of robotic systems are still in their infancy,” she says. “The goal is to have them gain common sense.”

Alessandro Chiesa named one of MIT TR's 35 Innovators Under 35

CS Assistant Prof. Alessandro Chiesa has been named to the 2018 roster of MIT Technology Review's "35 Innovators Under 35."  The list acknowledges "exceptionally talented young innovators whose work we believe has the greatest potential to transform the world."  Chiesa, who co-founded Zcash, was cited in the Pioneers category for "a cryptocurrency that’s as private as cash."  Zcash employs a cryptographic protocol called a succinct zero-knowledge proof--an efficient way to convince both parties to a transaction that something is true without divulging any other information. It allows people to do transactions online without risking their privacy or exposing themselves to identity theft.  Launched 4 years ago, Zcash now has a market cap of over a billion dollars.

John Schulman named MIT TR Pioneering Innovator Under 35

CS alumnus John Schulman (Ph.D. '16, adviser: Pieter Abbeel) has been named to MIT Technology Review's 2018 list of "35 Innovators Under 35," an honor which recognizes "exceptionally talented young innovators whose work we believe has the greatest potential to transform the world."  Schulman, whose dissertation was on "Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs," is cited in the Pioneer category for "training AI to be smarter and better, one game of Sonic the Hedgehog at a time."   He is the co-founder of OpenAI, where he has created some key algorithms in reinforcement learning: he trains AI agents in the same way you might train a dog, by offering a treat for a correct response--in this case, by racking up a high score in a video game.  These algorithms, once trained, might be applied in the real world, where they can be used to improve robot locomotion.