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.

Slaughterbots video shows why we should ban lethal autonomous weapons

Prof. Stuart Russell teamed up with to create a provocative video called Slaughterbots, which shows what can go wrong when autonomous weapons are designed to target humans.  "This short film is more than just speculation" Russell says. "It shows the results of integrating and miniaturizing technology that we already have."

New Master of Cybersecurity degree now accepting applications

The School of Information (I School) is now accepting applications for its new, online Master of Information and Cybersecurity (MICS) program, also known as cybersecurity@berkeley.  MICS is an accredited program designed to train students with professional experience for a career in cybersecurity. Delivered online, MICS provides the technical skills and contextual knowledge students need to assume leadership positions in private sector technology companies as well as government and military organizations.  The program was developed in partnership with the College of Engineering and in collaboration with the Center for Long-Term Cybersecurity (CLTC).  A number of EECS faculty are involved with the CLTC, which seeks to create effective dialogue among industry, academia, policy makers, and practitioners around a future-oriented conceptualization of cybersecurity — what it could imply and mean for human beings, machines, and the societies that will depend on both.  The first application deadline is January 30, 2018, and the first cohort of students will begin classes in May 2018.

UC Berkeley ranks #3 in 2017 U.S. and global CSRankings

UC Berkeley is ranked #3 overall in the U.S. and global computer science rankings (CSRankings) for 2017.  CSRankings is designed as a more meaningful and transparent alternative to the US News and World Report computer science ranking system--which is entirely reputation-based and relies on surveys sent to department heads and directors of graduate studies.  The CSRankings system is entirely metrics-based: it weighs departments by their presence at the most prestigious publication venues.    Berkeley ranked in the top 10 in all 4 fields:  Theory (1), Artificial Intelligence (3), Systems (6) and Interdisciplinary Areas (6).  And of the 26 areas ranked, Berkeley made the top 10 in 11 of them: computer vision(2), robotics(2), machine learning and data mining(3), computer security(3), cryptography(3), design automation(3), operating systems(4), natural language processing(5), software engineering(6), algorithms & complexity(7), computer networks(8).

Embodied Intelligence raises $7M in seed round

Start-up Embodied Intelligence,  founded by Prof. Pieter Abbeel and his grad students Peter Chen, Rocky Duan, and Tianhao Zhang, raised $7M in a seed round yesterday led by venture capital firm Amplify Partners.  VC firms Lux Capital, SV Angels, FreeS, 11.2 Capital, and A. Capital also supplied capital.  Embodied Intelligence is building AI software to enable robots to learn tasks performed by the user via a virtual reality headset.  It claims existing robots will be compatible with the "robot brain," which would supplant coding scripts tailored to each task.  Embodied will use the seed capital to write its first robotics applications.

Students learn to think like hackers for 'cyberwar' course

CS students enrolled in CS 194, an experimental “cyberwar” course led by Prof. Doug Tygar,  have joined forces with the white hat hackers at HackerOne, a vulnerability coordination and bug bounty platform.  This is the first time that HackerOne--which connects hackers with tech companies, private businesses and federal agencies to hunt for vulnerabilities--has partnered with a university.  Students are gaining real-world cyberwar experience. “Unless students can learn to ‘think like a hacker,’ they will not be able to effectively defend systems” says Tygar.

BRETT training with VR

EECS-affiliated startup uses virtual reality to show robots how to perform

The start-up Embodied Intelligence and its founders, Prof. Pieter Abbeel and grad students Peter Chen, Rocky Duan, and Tianhao Zhang, are the focus of two news articles: one from the New York Times titled "A.I. Researchers Leave Elon Musk Lab to Begin Robotics Start-Up," and one from Berkeley News titled "Berkeley startup to train robots like puppets."  The start-up is backed by $7 million in funding from Amplify Partners and other investors and will specialize in complex algorithms that allow machines to learn new tasks on their own through extreme trial and error.  The researchers are augmenting the algorithms with a wide range of techniques, like using virtual reality tools to show a robot how to perform a task--translating the movements into digital data.  “With our advances in machine learning, we can write a piece of software once — machine learning code that enables the robot to learn — and then when the robot needs to be equipped with a new skill, we simply provide new data.” Abbeel explains.