Cameron Freer

Cameron Freer is a Research Scientist in the MIT Probabilistic Computing Project, with prior roles including Instructor in Pure Mathematics at MIT, Postdoctoral Fellow at CSAIL, and Project Associate Professor at Keio University. His work bridges probabilistic computing, logic, and theoretical computer science.

Education

• PhD in Mathematics, Harvard University, 2008 (Thesis: Models with High Scott Rank)

Research Interests

Freer's research explores the deep interplay between randomness and computation, focusing on:

• Foundations of probabilistic programming languages and systems
• Efficient samplers for discrete and continuous distributions
• Mathematics of random structures like graphons and exchangeable processes
• Computability in measure theory and probabilistic inference

Publications Overview

His recent work (2020–2024) advances probabilistic programming systems (e.g., GenSQL), theoretical frameworks for random graphs via Markov categories, and computable approaches to PAC learning. Earlier contributions include exact sampling algorithms, computable exchangeability, and algorithmic barriers in conditional probability.

Academic Service

• Steering Committee Member, LAFI (formerly PPS) workshop series (2017–2025)
• Program Committee Chair/Co-chair, PPS 2017–2018
• Session Chair, POPL 2017 (PPS track)

Industry & Visiting Roles

• Chief Scientist, Remine (2017–2018)
• Research Scientist, Gamalon Labs (2013–2016)
• Lyric Labs Visiting Fellow, Analog Devices (2013–2014)
• Project Associate Professor, Keio University (2021–2024)

Labs & Collaborations

Freer collaborates extensively with the MIT Probabilistic Computing Project, Harvard Logic Group, and international partners in Oxford, CMU, and Keio University. His work integrates theoretical insights with practical systems in AI and probabilistic inference.