Jacek Ossowski

Machine/Deep Learning, Reinforcement Learning, Probability, Stochastic Processes

Jacek Ossowski holds a Ph.D. degree in Mathematics from Courant Institute, NYU. After graduation he worked as a quantitative research analyst at several financial institutions such as Franklin Templeton, Royal Bank of Canada, Credit Suisse, and Goldman Sachs. He has also taught Mathematics and Computer Science at several universities including Northeastern University in Boston, Fordham University in New York, and the University of Connecticut in Stamford. In his work Jacek focused on optimization and back-testing of statistical arbitrage portfolios, implementation of cost-efficient trade-out algorithms, and factor model-based research on mutual fund performance. Throughout his career Jacek participated in numerous software development projects related to financial infrastructure and research. More recently Jacek has worked on machine learning applications to medical imaging and finance.

Industry
2017-2021 Quantitative Research Analyst at QS Investors, L.L.C (now Franklin Templeton), Multi-asset Research

2011 Vice President, Quantitative Research Analyst at Royal Bank of Canada, Global Arbitrage Trading

2010 Vice President, Quantitative Research Analyst at Credit Suisse, Prime Services

2000-2005, 2007-2008 Vice President, Quantitative Research Analyst at Goldman Sachs, Quantitative Trading, Program Trading

Teaching
2021-2022 Assistant Professor in Residence, Computer Science & Engineering Department, University of Connecticut

2015-2017 Lecturer, Northeastern University, College of Computer and Information Science

2015 Lecturer, Fordham University, Mathematics Department

2011-2014 Computer Science/Mathematics teaching appointments at Fordham University and Framingham State University.

2009 Consultant for OneMarketData, a leading provider of state of the art solutions for capturing, storing and analyzing high-frequency financial data.

2022 Application of Deep Learning to Statistical Arbitrage
2020 Reduction of Motion Artifacts on Brain MRI Using Deep Learning

CS 334: Theory Of Computation
CS 570: Data Structures & Algorithms
CS 135: Discrete Structures