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Assistant Professor of Neuroscience at Baylor College of Medicine
Previously, I was a postdoctoral fellow at Harvard University, where I studied how the activity of populations of neurons are involved in helping us predict rewards. I completed my PhD in Machine Learning and Neural Computation at Carnegie Mellon University. My thesis concerned learning using brain-computer interfaces. Before that, I worked as a software engineer and consultant at Biarri, an Australian company specializing in using mathematical optimization to help clients make data-driven business decisions. In my research I use a variety of statistical and machine learning methods and tools, including artificial neural networks and reinforcement learning. I love programming; I'm most comfortable with Python and Matlab, and have experience in industry using C++, Javascript, PostgreSQL, and HTML/CSS. I graduated with a B.S. in pure mathematics and have had extensive exposure to statistical methods, testing, and estimation in both research and industry.
Carnegie Mellon University
Doctor of Philosophy (PhD), Machine Learning and Neural Computation
August 1, 2016 – March 1, 2021
The University of Texas at Austin
B.Sc. Mathematics
January 1, 2009 – January 1, 2011
Baylor College of Medicine
Assistant Professor of Neuroscience
October 1, 2024 – Present
Houston, Texas, United States · On-site
Harvard University
Postdoctoral Fellow in Psychology
August 1, 2021 – October 1, 2024
Cambridge, Massachusetts, United States
The University of Texas at Austin
Research Assistant
April 1, 2014 – July 1, 2015
Huk Lab, Center for Perceptual Systems
Biarri
Consultant and Software Engineer
November 1, 2011 – May 1, 2013
Melbourne, Victoria, Australia
University of Texas at Austin
Research assistant
May 1, 2009 – August 1, 2011
Huk Lab, Center for Perceptual Systems
UT Southwestern Medical Center at Dallas
Research assistant
June 1, 2006 – January 1, 2009
Bioinformatics lab
Cultural Fit Analysis
The candidate's career path has been predominantly academic and research-focused, with a brief stint as a Consultant and Software Engineer. While the analytical and technical skills are relevant, the transition to a corporate Data Analyst role might require adjustment to different work rhythms, project structures, and business objectives. The diversity of research topics indicates adaptability and intellectual curiosity.
Soft Skills & Operational Fit
The candidate's extensive research background suggests strong analytical thinking, problem-solving, and independent work capabilities. Experience in academic settings implies good presentation and collaboration skills within research teams. However, direct experience in a corporate data analyst operational environment is limited.