Teaching CS DATA605 - Big Data Systems (Sprint 2023), CS Decentralized Finance (Fall 2023)

High-frequency and mid-frequency trading strategies for US equities and global futures.

Knowledge graph, data, and machine learning for predicting commodity futures

Pit stop due to non-compete

- Built trading high- and mid-frequency strategies for US equities and global futures- Built and run the data on-boarding and acquisition platform- Built research team in Berkeley

* Quant research (Python / R) - Futures, Spot FXs, US equities (long-short with various factor neutrality) - Machine learning (models ranging from minutes to days) - Stat-arb with structured / unstructured / alternative data - Factor model prediction - Continuous and event trading - Alpha research pipeline* Infrastructure (python / C++) - Implemented many tools for research and backtest - Portfolio mean-variance optimization (CVX, gradient descent)* Led team of 11 researchers with PhD in various quantitative disciplines

* Integrated into Synopsys tool suite technology from ZeroSoft simulator, which was developed by the company I started in 2007 and was acquired by Synopsys in 2010

* Raised $2m from top Silicon Valley VCs- Acquired by Synopsys (Nasdaq:SNPS) in 2010 for $24m* Technology- Developed software simulation technology to speed-up 10x verification of integrated circuits- High-performance computing, compiler, assembly code generation for CPU and GPU- C++ and Verilog- 2 patents- Directed team of 11 people

- Implemented verification code and tools in C++ for Fermi GPU- Designed GPU blocks in Verilog, prototypes in FPGA-based boards and emulators

- Research on soft errors- Research on techniques for circuit simulation