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Quantitative Software Research Group at Georgia Tech

The Quantitative Software Research Group investigates systematic algorithms for trading and investing. Our focus is on Machine Learning, but we are also interested in other types of algorithms that inform us about markets and trading.

Members of Our Group

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Tucker Balch, Ph.D., Director, Quant Software Research Group
Professor, Interactive Computing, Georgia Tech
Instructor for CS 7646
Chief Scientist, Lucena Research, Inc.
website

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Maria Hybinette, Ph.D.
Associate Professor, Computer Science, University of Georgia

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David Byrd, Graduate Student and Head TA for CS 7646
Research Scientist, Interactive Media Technology Center, Georgia Tech
Instructor for CS 7646, Summer 2016

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Brian Hrolenok, Ph.D. Student and Head TA
Multiagent Robotics and Systems Lab
website

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Jianling Wang, TA for CS 7646
Graduate Student, College of Computing, Ga Tech

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Vivek George, TA for CS 7646
Graduate Student, College of Computing (CSE), Ga Tech
2016 Summer Intern, Electronic Arts
website: https://www.linkedin.com/in/vivekjohn

Alumni

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Sourabh Bajaj, MSCS, Georgia Tech
Software Engineer, Coursera Inc
website

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Devpriya Dave, MSCS, Georgia Tech
Analyst, Data Division, Morgan Stanley
website

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Jayita Bhattacharya, MSCS, Georgia Tech
Software Engineer (Playlist), Pandora Media

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Alexander Moreno, MSCS, Georgia Tech
Ph.D. student, Georgia Tech
website

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Rohit Sharma, MS QCF, Georgia Tech
Blackrock Capital

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Vishal Shekhar, MS QCF, Georgia Tech
Software Engineer, Axioma Inc.
website

Publications

  • Moreno, Alexander, and Tucker Balch. "Speeding up large-scale financial recomputation with memoization." Proceedings of the 7th Workshop on High Performance Computational Finance. IEEE Press, 2014. (conference)
  • Moreno, Alexander, and Tucker Balch. "Improving financial computation speed with full and subproblem memoization." Concurrency and Computation: Practice and Experience (2015). (Journal) [1]