CalSol's Zephyr wins 2017 Formula Sun Grand Prix

Zephyr, the solar vehicle built by UC Berkeley's CalSol team (including sophomore Wen Rui Liau, one of Prof. James Demmel's research students), has won the 2017 Formula Sun Grand Prix (FSGP).  The FSGP is an annual nationwide solar vehicle race that takes place on closed-loop race tracks. From July 6-8, in Austin, Texas, competing teams tested the limits of their vehicles in handling curves, braking, and acceleration.  The winner, determined by the total number of laps completed--minus penalties--over three days of racing, was Zephyr with 228 laps completed and zero penalties.

ESPIRiT paper is the most-cited Magnetic Resonance in Medicine article from 2014

The paper titled "ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA" co-written by Associate Prof. Michael Lustig,  his graduate student Pat Virtue, and alumnus Mark J. Murphy (Ph.D. '11 advisor: Kurt Keutzer) has been named the most-cited Magnetic Resonance in Medicine article from 2014.    The article bridges the gap between the two main approaches for parallel imaging (SENSE and GRAPPA) allowing the reconstruction of images from undersampled multicoil data.  It presents a new autocalibration technique combining the extended reconstruction of SENSE with GRAPPA-like robustness to errors.

Authors of the paper are listed as Martin Uecker, Peng Lai, Mark J. Murphy, Patrick Virtue, Michael Elad, John M. Pauly, Shreyas S. Vasanawala, and Michael Lustig.

CS Assistant Teaching Prof. Josh Hug

Thank you, Josh Hug

In an article for the Daily Cal, undergraduate Taylor Choe thanks CS Assistant Teaching Prof. Josh Hug for helping her overcome her negative first impression of Berkeley and discover what makes it so special.   "My mindset going into CS 61B was definitely not a positive one. I struggled with 61A and felt discouraged, making me really come to dislike computer science." she wrote.  But Dr. Hug made her fall in love with computer science and helped her find faith in the public school system.   "You could tell that he wanted to be at lecture and wasn’t thinking about being somewhere else. His projects, homeworks and labs were entertaining and engaging, displaying the time and thought that went into each of them. He constantly emphasized the importance of being an honest person in addition to being an honest programmer. He was somehow able to make a 1,400-person class feel a little smaller. And I don’t think there is anything more you can ask of a professor, especially at a school as large as UC Berkeley."

Vasuki Narasimha Swamy and César Torres win Microsoft Research Dissertation Grants

EE graduate student Vasuki Narasimha Swamy (adviser: Anant Sahai) and CS graduate student César Torres (adviser: Eric Paulos) have won inaugural Microsoft Research Dissertation Grants.  These grants offer financial support to selected doctoral students from groups that are under-represented in the field of computing.  Of the 200 applicants, only 12 were chosen.  Vasuki's research topic is “Real-time Ultra-reliable Wireless Communication” and César's is “Hybrid Aesthetics – A New Media Framework for the Computational Design of Creative Materials, Tools, and Practices within Digital Fabrication.”

Wenting Zheng wins the 2017-18 IBM PhD Fellowship

EECS graduate student Wenting Zheng (advised by Ion Stoica and Raluca Ada Popa)  has won the prestigious 2017-18 IBM PhD Fellowship.   Wenting works in the RISELab and her research involves system security and distributed systems. The IBM Ph.D. fellowship is an "intensely competitive worldwide program that honors exceptional Ph.D. students who have an interest in solving problems that are important to IBM and fundamental to innovation in many academic disciplines and areas of study." Only 50 fellowships are awarded worldwide annually.

UC Berkeley alumni are 2017's most wanted tech employees

According to an analysis by online recruiting company HiringSolved, UC Berkeley has the most undergraduate and graduate alumni hired by the 25 biggest Silicon Valley employers in 2017.  Using data from more than 10,000 public profiles for tech workers hired or promoted into new positions in 2016 and the first two months of 2017, the company determined that Berkeley alumni were hired more frequently than any other, followed by Stanford, CMU, and USC.  A Quartz Media article attributes some of that success to the close relationships our faculty and administrators have with Bay Area tech firms.  HiringSolved also determined which skills were the best indicators for getting entry-level jobs and the most likely job titles for new graduate applicants.

Two sophomores are using AI to fight fake news on Facebook

EECS sophomore Rohan Phadte and Interdisciplinary Studies major Ash Bhat have built a Messenger bot called NewsBot to help users discern whether articles are "fake news" on Facebook. Besides determining the validity of an article, it also offers a barometer that shows where an article might fit on the left-right bias spectrum, and is one of the only tools of its kind.  The idea for the algorithm first came to them during machine-learning classes.  Although it still has some bugs, they are making updates every day and the tool will improve as more users provide feedback.   "We want people to read more than just the headline. We want them to understand what the news they see says and who it's coming from." Bhat says.  Read the article on Mic.

Sam Kumar is 2017 University Medal runner up

EECS major Sam Kumar is a runner up for the 2017 University Medal.  Candidates for the University Medal must have overcome significant challenges, made a difference in the lives of others and carry a GPA of 3.96 or higher.  Sam is involved in research related to software-defined buildings, has spent each semester volunteering time as a tutor, allowing him to give back to a community that he said has been instrumental to his time as a student. One of his favorite memories from Cal involves a class in Sanskrit.  "Before we got to the literature, we had to learn the basics. We started were reading simple passages and doing vocabulary exercises to get the basics, but eventually I was able to read authentic texts from thousands of years ago. Being able to read an ancient language – after only two semesters of studying – was a breathtaking moment for me.”    Sam will receive a $500 award as a tribute to his academic efforts and, after graduation, he plans to work on a Ph.D. in computer science.

Tomás Vega Gálvez and Corten Singer chosen Lemelson-MIT “Drive it!” Undergraduate Team Winners

CS undergraduates Tomás Vega Gálvez and Corten Singer have been chosen the $10,000 Lemelson-MIT “Drive it!” Undergraduate Team Winner for an open-source smart add-on system for wheelchairs. Vega and Singer created WheelSense, a modular, customizable add-on system for wheelchairs that provides spatial awareness for visually impaired users to identify obstacles and ease their navigation. It has three features: frontal staircase detection through auditory feedback, backward obstacle-avoidance assistance through auditory feedback, and lateral ramp-edge detection through haptic feedback. They hope to disrupt the expensive market for assistive technologies for the disabled community by making their technology open source.  The “Drive it!” Lemelson-MIT Student Prize rewards students working on technology-based inventions that can improve transportation.

Meet Ray, the Real-Time Machine-Learning Replacement for Spark

CS Prof. Michael Jordan, graduate students Philipp Moritz and Robert Nishihara, and research in the RISELab are featured in a Datanami article titled "Meet Ray, the Real-Time Machine-Learning Replacement for Spark."  Ray is one of the first technologies to emerge from RISELab, the successor to AMPLab and its host of influential distributed technologies including Spark, Mesos, and Tachyon. Ray is a new distributed framework designed to enable Python-based machine learning and deep learning workloads to execute in real-time with MPI-like power and granularity. This framework is ostensibly a replacement for Spark, which is seen as too slow for some real-world AI applications.