Sat was most recently an Engineering Leader and Machine Learning Researcher at Google AI prior to joining Skovinen. His research focused on fundamental questions in deep learning (such as understanding why neural networks generalize at all) and on various applications of ML ranging from Chip Design to Finance.

Before Google, as a Senior Vice President at Two Sigma (a leading quantitative investment management firm), he founded one of the first successful deep learning-based alpha research groups on Wall Street and led a team that built one of the earliest end-to-end FPGA-based trading systems for general purpose ultra-low latency trading.

Prior to that, he was a Research Scientist at Intel where he worked on microarchitectural performance analysis and formal verification for on-chip networks. Sat has also been published in some of the top machine learning, design automation, and automated reasoning journals and has spoken at leading industry conferences around the world.

“To make the most of Al/ML solutions, one must analyze the business problem holistically to understand where the Al can provide the most advantage. This is followed by identifying the right Al/ML technology to use on these problems. Thus, a consultative approach is often better for applying Al/ML solutions to business issues.”