CS graduate student Shruti Agarwal and her thesis advisor Prof. Hany Farid have created a new weapon in the war against "deepfakes," the hyper-realistic AI-generated videos of people appearing to say and do things they never actually said or did.  The new forensic technique, which uses the subtle characteristics of how a person speaks to recognize whether a new video of that individual is real, was presented this week at the Computer Vision and Pattern Recognition conference in Long Beach.  “The basic idea is we can build these soft biometric models of various world leaders, such as 2020 presidential candidates," said Farid, "and then as the videos start to break, for example, we can analyze them and try to determine if we think they are real or not.”

A paper written by recent graduate Elizaveta Tremsina (B.S. '19 CS/Physics/Applied Math) has taken third place in the undergraduate category of the 2019 ACM Student Research Competition (SRC) Grand Finals.  The paper, titled "Your Story Recorded in a Magnet: Micromagnetic Simulations of Spin-Orbit Torque in Multi-layer Structures," was a continuation of the first place poster she presented at the 2018 Richard Tapia Celebration of Diversity in Computing.    "I am extremely thankful to the Berkeley EECS department for the amazing 4.5 years and for the unique chance to participate in cutting-edge research with Dr. Salahuddin's group and also attend the Tapia conference (my first one back in 2016 and last year)," she said. "I hope that more Berkeley undergrads participate in this competition in the future, be it at Tapia or other ACM conferences."  Tremsina was presented with her award at the ACM awards banquet last weekend.

"Improved Subthreshold Swing and Short Channel Effect in FDSOI n-Channel Negative Capacitance Field Effect Transistors," has won the 2018 IEEE Electron Devices Society (EDS) George E. Smith Award.  The paper was co-authored by current postdoc Korok Chatterjee, graduate student Ava J. Tan, former postdocs Daewoong Kwon,  Angada B. Sachid, Ajay K. Yadav and Hong Zhou, EE Profs. Chenming Hu and Sayeef Salahuddin, and LBNL's Roberto dos Reis. The award recognizes the best paper appearing in a fast turn around archival publication of the IEEE Electron Devices Society, targeted to IEEE Electron Device Letters.

EECS PhD student Justin Yim (with advisor EECS Prof. Ron Fearing and ME undergraduate co-author Eric Wang) has won the best student paper award at the 2019 IEEE International Conference on Robotics and Automation (ICRA) (May 20-24, Montreal) for his paper "Drift-free Roll and Pitch Estimation for High-acceleration Hopping."  The robot "Salto" (Saltatorial Agile Locomotion on Terrain Obstacles) was previously restricted to only indoor operation in a room equipped with a motion tracking system. In the newest paper, Salto can now estimate its own position by combining its onboard inertial sensor with a model of its takeoffs and landings. This improved estimate allows
Salto to hop outside with human steering.

A paper co-authored by freshly minted alumnus Tianshi Wang (Ph.D. '19, winner of the 2019 EECS David Sakrison Memorial Prize for "truly outstanding research") and Prof. Jaijeet Roychowdhury has won Best Paper Award at the International Conference on Unconventional Computation and Natural Computation (UCNC) 2019.  The paper, titled "OIM: Oscillator-based Ising Machines for Solving Combinatorial Optimisation Problems" will be presented at the conference in Japan next week.

Salto the robot, first unveiled in 2016 by the Biomimetic Millisystems Lab, is now equipped with a slew of new skills, giving it the ability to bounce in place like a pogo stick and jump through obstacle courses like an agility dog. Salto can even take short jaunts around campus, powered by a radio controller.  Salto creators Justin Yum, Eric Wang and Ronald S. Fearing will describe the robot’s new skills today (Tuesday, May 21) at the 2019 International Conference on Robotics and Automation in Montreal.

Recent graduate Chelsea Finn (Ph.D. '18, advisors: Pieter Abbeel and Sergey Levine), has won the prestigious ACM Doctoral Dissertation Award. This award is presented annually to "the author(s) of the best doctoral dissertation(s) in computer science and engineering."  In her dissertation, "Learning to Learn with Gradients," Finn introduced algorithms for meta-learning that enable deep networks to solve new tasks from small datasets, and demonstrated how her algorithms can be applied in areas including computer vision, reinforcement learning and robotics.  Finn  is currently a research scientist at Google Brain, a post-doc at the Berkeley AI Research Lab (BAIR), and an acting assistant professor at Stanford.  Last year's recipient, Aviad Rubinstein, was also a Berkeley EECS alum.

Graduate student Soham Phade and his advisor, Venkat Anantharam, have won the Best Paper Award at the 9th EAI International Conference on Game Theory for Networks (GameNets 2019).  Their paper, titled "Optimal Resource Allocation over Networks via Lottery-Based Mechanisms," was in the Games for Economy and Resource Allocation category.  Phade's current focus is on "designing market-based mechanisms and algorithms on presumably more accurate models of human behavior from psychology and decision theory, for increasing human welfare and for building more efficient commercial systems that interact with humans."

Two papers by EECS faculty have been named 2018 IEEE Micro Top Picks by the Association for Computing Machinery (ACM) Special Interest Group on Computer Architecture (SIGARCH).  The papers were "A Hardware Accelerator for Tracing Garbage Collection," co-authored by Profs. Krste Asanović and John Kubiatowicz (along with Martin Maas), and "FireSim: FPGA-Accelerated Cycle-Exact Scale-Out System Simulation in the Public Cloud," co-authored by Profs. Borivoje Nikolić, Randy Katz, Jonathan Bachrach, and Krste Asanović (along with Karandikar, Mao, Kim, Biancolin, Amid, Lee, Pemberton, Amaro, Schmidt, Chopra, Huang and Kovacs).  Top Picks represent "the most significant research papers in computer architecture based on novelty and potential for long-term impact."  The papers will be published in IEEE Micro's annual “Top Picks from the Computer Architecture Conferences” issue in May/June 2019.

A research paper by EECS Prof. Sayeef Salahuddin's group that shows direct measurement of Negative Capacitance was highlighted in an article in Nature Electronics titled "Negative capacitance found."   Negative Capacitance is a new state of ferroelectric material that was discovered by Salahuddin in 2008 and promises to significantly improve energy efficiency in electronics.