Economics | Finance & Strategy | Data Science
I am an applied microeconomist with a Ph.D. in Business Administration from the Haas School of Business at U.C. Berkeley. I combine expertise in economics, data science, and business, and work primarily at their intersection.
In the private sector, I principally provide economic analysis in antitrust and intellectual property matters, build enterprise and investment decision systems, and optimize R&D portfolios and innovation strategies. I work with organizations from pre-incorporation startups to Fortune 500 companies. I also provide expert testimony to government bodies and advise federal, state, and local policymakers on a range of policy topics.
From 2020 to 2023, I co-led the scalable Bayesian machine learning team in Amazon's Core AI Group. In 2023, I founded Petabyte Economics, a private lab that provides economic analysis and builds AI/ML tools for litigation, investment, policy, and operations optimization.
Early in my career, I was a software developer and systems architect, co-founded two startups, and worked as a venture capital associate. I later studied economics, finance, and strategy, and held appointments as a professor and fellow at business schools and think tanks.
My academic research mostly applies theory from industrial organization and corporate finance to questions in entrepreneurship and innovation, with a focus on venture capital, private equity, mergers and acquisitions, and intellectual property. It often uses advanced econometric techniques, particularly for causal inference and big data analysis, and I have developed new computational economic methods.
I have designed and estimated hundreds of economic models, including cross sectional, time series, panel, structural, clustering, event study, and Bayesian specifications, using Stata, MATLAB, R, SAS, SPSS, Python, Julia, and other languages. My work emphasizes causal inference, model selection, and big data analysis. I focus on reproducible workflows and scalable data architectures. I have taught database systems and led workshops on quantitative methods and SQL for big data analytics. I have also built high performance computing clusters for universities and companies to support empirical research.
I have developed machine learning (ML) methods for clustering, predictive modeling, MCMC simulation, and Bayesian inference, and have written ML libraries in MATLAB, Python, Spark, Perl, and other languages. However, I primarily apply machine learning to big data in distributed GPU-based computing environments, using CUDA and high-level frameworks such as TensorFlow and PyTorch. I also adapt traditional empirical and data science models for these contexts, and have led teams implementing ML technologies in production. I founded Petabyte Economics to evaluate new ML approaches that may support the first generation of broad-purpose, cloud-scalable economic models.
I provide economic analysis and develop AI/ML tools for litigation and public policy. Through Legal Economics, I managed an antitrust matter and led data and damages analysis for Einer Elhauge of Harvard Law School. I worked with David Teece at Berkeley Research Group on smartphone patent litigation and with Jim Brander at Delta Economics on Canadian competition litigation. My research is focused on entrepreneurship and innovation policy. As a Baker Institute and NBER fellow, I engaged with more than fifty public bodies and policymakers, including federal agencies and congressional committees, and gave policy testimony before state legislative committees. Through Petabyte Economics, I have designed large-scale economic models, including a forensic system for the U.S. Treasury and a policy simulation for the SBA.
My research mainly focuses on the efficiency and performance of venture capital (VC), private equity (PE), mergers and acquisition (M&A), and initial public offerings (IPOs). In 2005, I founded a data analytics firm specializing in Canadian venture capital, and until 2023 maintained several large research databases on VC, M&A, and IPOs. From 2015 to 2018, I ran a research center focused on these topics at Rice. I developed the entrepreneurship and entrepreneurial finance curricula at Georgetown and Imperial College, and taught an executive course on IPO preparation at Imperial. I have negotiated term sheets, prepared offering materials, and worked with startups and entrepreneurship organizations for over twenty years.
My second research focus is on how innovation characteristics and other factors influence intellectual property and commercialization outcomes. I developed and taught graduate courses in innovation economics at Imperial College and classes on innovation strategy at Georgetown. I contributed to the NBER patent data project and maintained near-population datasets on USPTO filings, federal grants, and other public R&D funding. I provided annual R&D portfolio analyses for five years to a Fortune 500 firm in the oil and gas sector, including assessments of patenting activity, R&D management, and joint venture performance. I also worked with two other Fortune 500 companies on innovation strategy in long-term engagements.