Quartz  explores an algorithm devised by CS Prof. Trevor Darrell, L&S CS undergraduate student Dong Huk Park, CS grad student Lisa Anne Hendricks, and postdoc Marcus Rohrbach, along with researchers in the Max Planck Institute for Informatics,  in an article titled "We don’t understand how AI make most decisions, so now algorithms are explaining themselves." Engineers have developed deep learning systems that ‘work’ without necessarily knowing why they work or being able to show the logic behind a system’s decision.   The algorithm uses a “pointing and justification” system, to point to the data used to make a decision and justify why it was used that way.

EE Prof. Bernhard Boser is profiled in an article in the Cal Aggie titled "Fingerprint recognition on smartphones unsafe and hackable" in which he discusses a new ultrasonic imaging process developed at UC Berkeley and UC Davis to more securely protect personal information than current finger recognition technologies.  This new technology, which combines an ultrasonic sensor in air and an ultrasonic sensor in tissue, captures a fingerprint in 3D to uniquely identify a person.  It images both the ridges and valleys of a fingerprint surface as well as the subsurface structure of the skin,  distinguishing between layers of tissue by analyzing the densities of live and dead skin cells.  "This imaging process can look at the surface of fingerprints and inside the finger,” Boser said. “There are more patterns inside the finger that can’t be put onto glass screen of a phone.”

CS graduate student Jun-Yan Zhu (adviser: Alexei Efros) is the subject of an article in California Magazine titled "Paint by Numbers: Algorithms for the Artistically Challenged."  Zhu and his team apply the tools of machine learning to computer graphics.  For example, in the team's most recent project, they developed software that lets users easily create realistic images from the crudest brushstrokes.  Their research projects have yielded potential applications from improving online searching and e-commerce to art and fashion.

A Computational Imaging research proposal submitted by EE Associate Prof. Laura Waller, EE Associate Prof. Michael Lustig, CS Assistant Prof. Ren Ng, CS Assistant Prof. Jonathan Ragan-Kelley, and CS Associate Prof. Benjamin Rechts has been accepted as part of a set of cross-disciplinary activities planned for development by Berkeley Research.  Berkeley Research ran eight faculty forums on a wide range of topics and received 30 proposals which were reviewed by a faculty panel and discussed with the Deans.  The selected projects "hold great promise for Berkeley to be at the forefront of developing a positive vision for the future."

Prof. Ali Javey and his team's presentation at the 2016 International Electron Devices Meeting (IEDM) is profiled in an EE Times article titled "Sweating Big Human-Body Data Challenge." This year, IEDM papers  explored a number of technologies to make flexible and printable electronics,  and Prof. Javey's team's paper stood out. Unlike conventional wearable devices, the team has zeroed in on the idea of attaching sweat biosensors — like a patch — on the body to collect sweat as it appears, for “real-time perspiration analysis.”

Earlier this year NVIDIA CEO Jen-Hsun Huang delivered a NVIDIA DGX-1 AI supercomputer in a box to the Berkeley AI Research Lab (BAIR). BAIR’s research is at the cutting edge of multi-modal deep learning, human-compatible AI and connecting AI with other scientific disciplines and the humanities. According to Prof. Pieter Abbeel, “More compute power directly translates into more ideas being investigated, tried out, tuned to actually get them to work.”

EECS Prof. Ronald Fearing, EECS PhD student Justin Yim, post doc Dr. Mark Plecnik, and ME PhD student Duncan Haldane have created Salto, the most vertically agile jumping robot.  Salto can repeatedly jump 1 meter vertically at almost two times per second.  Salto is featured in the premier issue of Science Robotics (Dec. 6).

Alumnus Paul Debevec (Ph.D. 1996) is the subject of a Cartoon Brew interview titled "Paul Debevec: A Name You Absolutely Need to Know in CG, VFX, Animation, and VR." Paul's insights into virtual cinematography, image-based lighting (IBL), and the crafting of photoreal virtual humans inspired several films, including The Matrix, Spider-Man 2, and Avatar, along with games and real-time rendered content.   Paul is now an adjunct research professor at the University of Southern California Institute for Creative Technologies (USC ICT) and just began as a senior staff engineer in the GoogleVR Daydream team, working at the intersection of virtual reality and real-time rendering.  The interview explores why his research has had such a major influence on computer graphics, animation, vfx, and vr.

CS postdoctoral fellow Jeff Regier (adviser: Michael Jordan) along with researchers from Julia Computing, Intel,  NERSC, LBNL, and JuliaLabs@MIT have developed a new parallel computing method to dramatically scale up the process of cataloging astronomical objects. This major improvement leverages 8,192 Intel Xeon processors in Berkeley Lab’s Cori supercomputer and Julia, the high-performance, open-source scientific computing language to deliver a 225x increase in the speed of astronomical image analysis.

The code used for this analysis is called Celeste.  “Astronomical surveys are the primary source of data about the Universe beyond our solar system,” said Jeff. “Through Bayesian statistics, Celeste combines what we already know about stars and galaxies from previous surveys and from physics theories, with what can be learned from new data. Its output is a highly accurate catalog of galaxies’ locations, shapes and colors. Such catalogs let astronomers test hypotheses about the origin of the Universe, as well as about the nature of dark matter and dark energy.”

More detail can be found in an article on HPC Wire "Researchers Develop New Parallel Computing Method."

RISC-V, an open-source instruction set architecture created at UC Berkeley is featured in an electronic design article titled “RICS-V (Five) is Alive!” RISC (Reduced Instruction Set Computer) was originally designed in 1982 by students with the direction of Professors David Patterson and Carlo Sequin. Since then, iterations of RISC have been developed. In 2010 Prof. Krste Asanovic, with the help of Prof. Patterson, decided to develop another version of RISC to help both academic and industrial users and RISC-V was published.