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The DeepScale Team

Forrest Iandola

Forrest Iandola

Forrest Iandola completed a PhD in EECS at UC Berkeley, where his research focused on improving the efficiency of deep neural networks (DNNs). His best-known work includes deep learning infrastructure such as FireCaffe and deep models such as SqueezeNet and SqueezeDet. His advances in scalable training and efficient implementation of DNNs led to the founding of DeepScale, where he has been CEO since 2015.

Kurt Keutzer

Kurt Keutzer

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.

Anting Shen

Anting Shen

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 Sidhu

Sammy Sidhu

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 Brughera

Lisa Brughera

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 Tomasello

Paden Tomasello

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 Redmon

Nobie Redmon

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 Landen

Ben Landen

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 Woe

Daisyca Woe

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.

Paras Jain

Paras Jain

Paras Jain is passionate about integrating machine learning with techniques from distributed systems and computer architecture. He has research publications in the fields of large-scale graph analytics and realtime streaming anomaly detection. In industry, he helped ship the Twitter Ads Editor and optimize trading pipelines for high-frequency trading at Two Sigma Investments. He's also a proud graduate of Georgia Tech!

Matthew Moskewicz

Matthew Moskewicz

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 Phadte

Romi Phadte

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.

Judy Thrasher

Judy Thrasher

Along with her many years of HR experience building great companies, Judy brings her passion for people to the DeepScale team. Judy has led staffing and HR operations for several technology startups in her 20+ years of experience and has helped organizations grow from early stage to mature publicly traded companies. Judy has a BS in Business Administration from the University of San Francisco, with a focus on Organizational Behavior.

Abhijit Ghosh

Abhijit Ghosh

Abhijit’s experience combines extensive expertise in high-performance computing and computer vision, along with a comprehensive understanding of software development and professional software engineering practices for various platforms. With considerable experience in startup environments, Abhijit most recently led critical development work involving high performance software and computer vision at Sano and Stretch Inc. Previously, he was a director of engineering at Synopsys, where he spearheaded the successful development of SystemC, a C++ library for describing VLSI systems at a multiple levels of abstraction. Abhijit holds a doctorate and a master’s degree in EECS from the University of California at Berkeley.

Gayatri Joshi

Gayatri Joshi

Gayatri is a recent graduate of Carnegie Mellon University with a master’s degree in electrical and computer engineering where her studies focused on her special interest in multimodal machine learning, artificial intelligence and natural language processing. While working as a graduate research assistant in the robotics department, she used deep learning and classical computer vision techniques to successfully prioritize the repair of damaged roads and signs in a city environment. Gayatri received her undergraduate degree in electronics and telecommunications from the Maharashtra Institute of Technology, College of Engineering in Pune.

Kyle Bertin

Kyle Bertin

Kyle has extensive experience in corporate strategy and corporate development at both publicly-traded corporations and early-stage startups. Prior to DeepScale, he worked closely with multiple venture-backed startups in transportation and energy while pursuing his MBA. Before business school, he led corporate strategy and M&A efforts at U.S. Silica and had prior experience in management consulting and derivatives trading. He holds a BA from Northwestern University and an MBA from Berkeley-Haas.

Daniel Hunter

Daniel Hunter

Daniel discovered his passion for robotic control and design in middle school robotics competitions, and has pursued this career path through independent projects and hackathons until graduating recently with a BS in Robotic Engineering. During his undergraduate degree, he developed an award winning Indoor Autonomous Navigation System for factory automation. Daniel specializes in the intersection of deep learning and robotics, creating real time solutions to perception problems.

Kaushik Ravindran

Kaushik Ravindran

Kaushik brings 15 years of research and industrial experience in system design, CAD, programming models, and optimization algorithms. Previously, in his role as Principal Architect at National Instruments, he worked on modeling, analysis, and optimization tools for deploying real time applications on parallel embedded and FPGA platforms. He has a BS degree in Computer Engineering from the Georgia Institute of Technology and a PhD in Electrical Engineering and Computer Sciences from UC Berkeley. He is a co-recipient of the Richard Newton Technical Impact Award in Electronic Design Automation for his research at an internship in IBM on statistical timing analysis of digital circuits, which was also recognized in the top-10 most cited papers in 50 years of the Design Automation Conference.

Sven Lerner

Sven Lerner

Sven is interested in the mathematical underpinnings of machine learning and how systems can leverage data at scale. He most recently worked at Wealthfront on the data engineering team contributing to a variety of efforts, most notably helping to build out their Near Real-time Compute infrastructure on Spark Streaming and Kafka. Sven holds a degree in EECS from UC Berkeley.