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    Alex Papanicolaou

    CTO at DashlyHR

    Alex Papanicolaou is a knowledgeable data scientist and quantitative researcher, currently holding the position of Chief Technology Officer at infima Technologies, a Stanford University spinoff focusing on predictive analytics for the credit market.

    infima Technologies uses innovative deep learning systems and computational algorithms to provide accurate and timely predictions on borrower, security, and market behaviors, offering a competitive edge to investors, dealers, and originators.

    With a strong background in FX market microstructure and execution, Alex has leveraged signal processing, machine learning, and time series techniques to address various challenges in currency markets.

    Having completed a Ph.D. in computational mathematics from Stanford University, Alex's thesis work centered on developing statistical tests for dynamic stochastic processes within financial mathematics.

    His educational journey includes a Bachelor of Arts in Economics from UCLA, a Ph.D. in Computational and Mathematical Engineering from Stanford University, and a Bachelor of Science in Applied Mathematics from UCLA.

    Throughout his career, Alex has held key roles such as Chief Data Scientist at infima Technologies, Postdoctoral Researcher at Consortium for Data Analytics in Risk (CDAR), Senior Data Scientist at Integral Development Corp., Instructor at Stanford University, Research Intern at Resolution Economics, and Course Assistant at Stanford University.

    Related Questions

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    Experience

    CTO at DashlyHR
    Chief Technology Officer and Chief Data Scientist at infima Technologies, Inc. (January 2019 - Present), Postdoctoral Researcher at CDAR (August 2016 - January 2019), Senior Data Scientist at Integral Development Corp. (October 2013 - August 2016), Instructor at Stanford University (January 2013 - June 2013), Course Assistant at Stanford University (January 2008 - December 2012), Research Intern at Resolution Economics (June 2009 - August 2009)

    Education

    Ph.D in Computational and Mathematical Engineering from Stanford University (2007 - 2013), BA in Economics from UCLA (2002 - 2007), BS in Applied Mathematics from UCLA (2002 - 2007)

    Location

    San Francisco Bay Area