Trexquant Investment LP
Wenjun Deng is a Senior Data Scientist at Trexquant Investment LP, with experience in data science since June 2015. Prior to this role, Wenjun held the position of Postdoctoral Research Associate at Princeton University, focusing on particle-in-cell (PIC) simulation studies of laser-excited high-energy-density plasmas from February 2014 to May 2015. At Princeton Plasma Physics Lab, from February 2012 to January 2014, contributions included the development of a phase space optimization technique for δf particle simulations, significantly reducing computation time, as well as contributions to open-source projects. Wenjun began the academic career as a Graduate Student Researcher at UC Irvine from July 2007 to January 2012, participating in the development of a large-scale fusion simulation code and serving as a Teaching Assistant for various physics courses. Educational credentials include a PhD in Physics from UC Irvine (2006-2011) and a Bachelor of Science in Applied Physics from the University of Science and Technology of China (2002-2006).
Trexquant Investment LP
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Being a quantitative finance firm that uses Machine Learning (ML) to create multi-asset portfolios and seek profit from the market, Trexquant has continuously improved its investment and research platform since starting operations, leveraging new and emerging technologies. Trexquant uses rigorous quantitative methods to create multi-asset portfolios in global markets. To do this, Trexquant develops trading signals using its vast and continuously growing collection of data variables used as inputs for more complex trading models called Strategies. The result is an ever-growing and adapting engine built from thousands of intricate models and tens of thousands of signals, tailor-made with the goal to outperform the market during any condition. Capital is managed across 5,500+ cash equity positions across the United States, Europe, Japan, Australia, and Canada.