Michael Sklar, PhD
Founder and CEO. Previously, Michael was a Stein Fellow at Stanford Department of Statistics. His 2021 PhD thesis at Stanford invented the proof-by-simulation method. Subsequently his invited talk and group discussion with FDA statisticians laid the groundwork for this project. Michael has also given talks on proof-by-simulation at Stanford, Google, Cruise, Berry Consultants, and a number of pharmaceutical companies. Linkedin,  twitter, Substack 

T. Ben Thompson, PhD
Professional software engineer since 2007. Ben has deep experience in numerical integration and supercomputing, beginning with his PhD in geophysics from Harvard. His talk at Bayes2022 explains how Confirm can do billions of Bayesian simulations per second. Ben is the main developer on cppimport, a popular open-source tool to speed up Python code. Ben has also launched popular packages for geophysics and statistics, and contributed to popular scientific computing tools like scipy.

James Yang
Doctoral student at Stanford Department of Statistics. James developed the Tilt-Bound, the state-of-the-art method implemented in Confirm Solutions' open-source repository and described in our latest white paper. James has a B.A. in computer science-mathematics and B.A. in statistics from Columbia University. He loves anything with an intersection between statistics and low-level scientific computing.

Gary Mulder
Chief Toy Operator - cloud devops, performance engineer, and data scientist. Gary has been working in IT for over 25 years, providing consulting services to a wide range of companies, from start-up to Fortune 500, across a diverse range of industries, including biotech, telco, fintech, and internet.

Alex Constantino
Experienced software engineer. Alex has six years of experience delivering data and machine-learning solutions. At Google, he applied machine learning to ensure quality in news search results. At Two Sigma Investments, he developed tools for researchers and delivered $2 million dollars in annual cost savings via infrastructure optimizations. He also published a state-of-the-art model for automated seizure detection using one of the world’s largest proprietary seizure datasets.

Daniel Kang
Doctoral student at the Stanford Department of Computer Science DAWN Lab. Daniel did his bachelors and M.Eng at MIT, with his thesis in computational biology. He then went on to do another masters at the University of Cambridge. Currently, his research focuses on deploying (unreliable) machine learning models efficiently and with guarantees.