Forrest started his career in high-performance computing (i.e. writing fast and scalable software), and he has spent the last several years as a PhD student at Berkeley accelerating and rethinking computer vision algorithms. He has published more than twenty papers, and all of his most recent papers are on accelerating and improving deep learning for computer vision. Forrest is also a huge car enthusiast, and when not stuck in traffic, he enjoys few things more than spending the day driving on a racetrack.
After leaving his position as CTO and Senior Vice-President of Research at Synopsys to become Professor of EECS at University of California, Berkeley, Kurt and his students have focused on applying commodity parallelism to accelerate emerging applications. In particular, Kurt's research group has achieved significant speedups in machine learning (SVMs), computer vision, speech recognition, multimedia analytics, computational finance, and, most recently, training and deployment of deep neural networks. As a researcher, Kurt has published six books and over 200 refereed articles. As an entrepreneur, Kurt has been an investor and advisor to thirteen startups and an advisor to seven more.
With a M.S. in EECS from Berkeley, Anting's expertise includes his experience with computer vision and deep learning from his time in Berkeley's Robot Learning Lab & the ASPIRE lab. He has experience in application development and machine learning through his time at Google and Yelp, and entrepreneurial experience from founding and working at other startups.
Sammy has a wide array of experience ranging from working on Stem Cell Research in a genetics laboratory, performing Medical AI research at Berkeley, building low latency ML systems at Apple's Applied Machine Learning division to building High Frequency Trading (HFT) systems on Wall Street at Two Sigma Investments. Sammy's specialities in computing are designing highly performant systems, Artificial Intelligence (including Deep Learning), and Computer Architecture. Sammy also has a degree in EECS from Berkeley.
Lisa brings her experience in project management, accounting, and business administration to all things financial and administrative at DeepScale. Lisa provides financial systems set-up, bookkeeping and general guidance to a range of startup endeavors. In her ten years as a Project Manager in the non-profit housing sector, Lisa built 1000 units of housing, overseeing the feasibility, financing and construction of properties with budgets of up to $14 million in equity, bond, bank and public funds. Lisa holds a Masters from The School of Global Policy and Strategy at UC San Diego.
Paden became passionate for HPC and ML while pursuing his undergraduate degree in EECS at UC Berkeley. While in school, he was awarded achievements on projects including parallel ray tracing optimization and digit recognition using neural nets. His industry experience includes working at Cloudera, where he helped develop Impala’s JIT compiler, and Graphistry, where he scaled its core clustering algorithms using efficient GPU implementations.
Nobie has worked on a variety of technical challenges ranging from developing feedback systems for the LHC to building an algorithmic trading platform for Quantiacs. He has an M.S in physics from Stanford. From Nobie's studies and software development he has both a deep understanding of computation and sensing physics. He was most recently at Google implementing scaled anti-abuse workflows.
Ben's career has been fully dedicated to automotive technology. Ben joined Maxim Integrated during the early stages of the company's automotive business expansion, which enabled him to garner expertise over the gamut of automotive product development, business management, sales/marketing, and quality. Ben has held P&L responsibility for a variety of infotainment and ADAS semiconductors, which are deployed in vehicles all over the world. Ben holds a BSEE from Cal Poly (San Luis Obispo) and an MBA from Berkeley-Haas.
Daisyca has worn many, many hats in order to unearth her desire to be active in business operations. After double-majoring and making the difficult decision to reroute her career path from pre-medicine at UC Berkeley, she has had the good fortune to fine-tune her skill sets for office management and client relations within the health and wellness industry. When not in workaholic-mode, you can find Daisyca in a yoga class or globetrotting and eating her way through town.
Matthew Moskewicz has deep experience with software development and a wide range of algorithms in Optimization, CAD, and Machine Learning. After four years as a grad student at UC Berkeley, he left to become Chief Software Architect at CommandCAD from 2004 until it was acquired by Cadence in 2007. He then worked part-time as a principal engineer at Cadence while completing his PhD. Splitting his time between academia and industry, he has accumulated more than 15 patents and over 5000 citations. He has received numerous awards for his early work on Boolean Satisfiability, including the DAC 50 year retrospective most-cited-paper award. More recently, he received his PhD from UC Berkeley, where his thesis focused on the efficient and portable implementation of deep neural network computations.
Romi has a range of experiences focused on product. He has prototyped on Pinterest Lens coming up with new product use cases for neural networks focused on image classification. He has also spent time working on product approaches to improve accuracy for classification. He has worked on medical AI research at UC Berkeley, launched products internationally at Pinterest, developed applications at Yahoo and Google, worked with data sets from biomedical robots at Accuracy, worked on sensor applications for room automation, and designed robots in CAD. Romi holds a degree in EECS from UC Berkeley.
Interesting Startups to Watch in 2017