faculty

A “blankie” that contains printed MRI coils (Usha Lee McFarling/STAT)

Ana Claudia Arias, Miki Lustig, and Joe Corea's printable, wearable devices

Prof. Ana Claudia Arias, Prof. Miki Lustig, and graduate student Joseph Corea, are featured in a STAT article titled "Electronics ‘like a second skin’ make wearables more practical and MRIs safer for kids."  The team is using printers loaded with a variety of high-tech inks (liquid silver nanoparticles, carbon nanotubes and semiconducting plastics) to make a new generation of medical devices, from wearables to barely noticeable MRI hardware for kids.  They have created light, flexible MRI coils that will improve image quality as well as patient comfort, and  have spun off a company called InkSpace Imaging to speed development.  “What would be best would be electronics that were almost like a second skin,” Arias said. “No adhesive. No straps. Almost like underwear — you forget that you’re wearing it.”

Doug Tygar's class of "ethical hackers" learns to wage cyberwar

Prof. Doug Tygar and his CS 194 Cybewar class are the focus of a New Yorker article titled "At Berkeley, a New Generation of “Ethical Hackers” Learns to Wage Cyberwar." The students have teamed up with the white hat hackers at HackerOne, a vulnerability coordination and bug bounty platform.  Companies, organizations, and government agencies use HackerOne to solicit help identifying vulnerabilities in their products––or, as Tygar put it, “subject themselves to the indignity of having undergraduate students try to hack them.”  Junior Vy-An Phan decided to focus on various secretary-of-state Web sites around the country, which house tools central to the electoral process—voter registration, ballot measures, candidate information, Election Day guidelines.  She has already found eight bugs spread across four sites.  “I could trick someone into registering for the wrong party, or not registering at all,” Phan said.

Randy Katz inducted into Silicon Valley Engineering Hall of Fame

Prof. Randy Katz has been inducted into the Silicon Valley Engineering Hall of Fame "for his contributions to storage and computer systems, distinguished national service, and by his exemplary mentorship and teaching that have contributed to the Silicon Valley technical community and industries."  Katz, who is also an alumnus (M.S. '78/Ph.D. '80), co-developed the redundant array of inexpensive disks (RAID) concept for computer storage along with Prof. Emeritus David Patterson and fellow alumnus Garth Gibson, in their 1988 SIGMOD Conference paper "A Case for Redundant Arrays of Inexpensive Disks (RAID)."  Silicon Valley Engineering Council (SVEC) Hall of Fame inductees have demonstrated significant engineering or technical achievements, provided significant guidance in new and developing fields of engineering-based technology, and/or have managed or directed an organization making noteworthy contributions in design, manufacturing, production, or service through the uses of engineering principles and applications.

Thomas Budinger wins IEEE Medal for Innovations in Healthcare Technology

Prof. Thomas Budinger has won the 2018 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."  Budinger, who was the founding chair of the Bioengineering department, a division director at LBNL and Director of the Magnetic Resonance Science Center at UCSF,  is cited “For pioneering contributions to tomographic radiotracer imaging."

Eli Yablonovitch wins IEEE Edison Medal

Prof. Eli Yablonovitch has been awarded the 2018 Institute of Electrical and Electronics Engineers (IEEE) Edison Medal.  Named for Thomas Edison and presented since 1909, the IEEE Edison Medal is awarded for a career of meritorious achievement in electrical science, electrical engineering, or the electrical arts.  Yablonovitch, who co-founded the field of of photonic crystals, was cited for "For leadership, innovations, and entrepreneurial achievements in photonics, semi-conductor lasers, antennas, and solar cells.”

Profs. Sanjit Seshia and Pieter Abbeel

Pieter Abbeel and Sanjit Seshia elected 2018 IEEE fellows

Profs. Pieter Abbeel and Sanjit Seshia have been elected fellows of the Institute of Electrical and Electronics Engineers (IEEE) class of 2018.  The objectives of the IEEE, the world's largest association of technical professionals, are the educational and technical advancement of electrical and electronic engineering, telecommunications, computer engineering and allied disciplines.  The Fellow grade is the highest level of membership and is conferred by the IEEE Board of Directors in recognition of a high level of demonstrated extraordinary accomplishment.  Abbeel was selected "for contributions to apprenticeship and reinforcement learning for robotics and autonomous systems" and Seshia was selected for "for contributions to formal methods for inductive synthesis and algorithmic verification."

Bernd Sturmfels wins 2018 George David Birkhoff Prize in Applied Mathematics

CS Prof. Bernd Sturmfels has won the 2018 George David Birkhoff Prize in Applied Mathematics for "his instrumental role in creating the field of applied algebraic geometry." This prize is jointly awarded by the American Math Society (AMS) and the Society for Industrial and Applied Math (SIAM) every 3 years for "an outstanding contribution to applied mathematics in the highest and broadest sense." Previous winners include Emmanuel Candes, Bjorn Engquist, and many other luminaries.

Jennifer Listgarten joins EECS Department

Dr. Jennifer Listgarten will join the EECS faculty effective Jan 1, 2018.  Listgarten received her B.S. in CS and Physics at Queen's University in Canada, and her M.S. (CS/computational vision) and Ph.D. (CS/bioinformatics/machine learning) from the University of Toronto.  She has spent the past 10 years as a researcher at Microsoft; her area of expertise is machine learning and applied statistics for computational biology.   She is interested in both methods development as well as application of methods to enable new insight into basic biology and medicine.  She will be co-teaching CS189 Introduction to Machine Learning with Prof. Anant Sahai starting in January.

professor ruzena bajcsy

Philly honors Ruzena Bajcsy

The life and contributions of CS Prof. Ruzena Bajcsy are profiled in a Philadelphia Inquirer article titled "Philly honors experts in robotics, genetics, evolution."  Bajcsy, who helped launch the field of computer vision, is being presented with a John Scott Award in science and medicine at the American Philosophical Society this evening.  Her early research paved the way for medical imaging such as MRIs, and she is noted for her cross-disciplinary approach, applying theories from mathematics and biology to her work.  She has devoted her life to research in the fields of robotics, artificial intelligence and machine perception, and took the helm of the Center for Information Technology Research in the Interest of Society (CITRIS) from 2001 to 2005.  Recipients of the Scott Awards are chosen each year based on recommendations from a panel of scientists. The award comes with a $10K prize.

Stampede2 (Sean Cunningham / TACC)

EECS-affiliated team break record for fastest deep learning training

Grad student Yang You, Prof. James Demmel and Prof. Kurt Keutzer, along with Prof. Cho-Jui Hsieh of UC Davis and Dr. Zhao Zhang of the Texas Advanced Computing Center (TACC), have created, in collaboration with researchers at NVIDIA, a new algorithm which enables them to harness the power of supercomputers to train a deep neural network (DNN) for image recognition at record speed. Deep learning researchers currently use trial-and-error to design new models, requiring them to run training processes tens or even hundreds of times for each model.  The team's effort efficiently used 1024 Skylake processors on the Stampede2 supercomputer at TACC to complete a 100-epoch ImageNet training with AlexNet in 11 minutes - the fastest time recorded to date.  Also, using 1600 Skylake processors, they bested Facebook's prior results by finishing a 90-epoch ImageNet training with ResNet-50 in 32 minutes and, for batch sizes above 20,000, their accuracy was much higher than Facebook's.   The group's breakthrough involved the development of the Layer-Wise Adaptive Rate Scaling (LARS) algorithm that is capable of distributing data efficiently to many processors to compute simultaneously using a larger-than-ever batch size (up to 32,000 items). The LARS algorithm was jointly developed with Nvidia. The findings show an alternative to the trend of using specialized hardware - either GPUs, Tensor Flow chips, FPGAs or other emerging architectures—for deep learning. The team wrote the code based on Caffe and utilized Intel-Caffe, which supports multi-node training. The results are published in Arxiv.