Christopher Frye Email & Phone Number

Designed the research program to support Faculty’s decision intelligence product and directed the R&D team (~15 people) that delivers it. Research topics include causality, explainability, and optimisation.

Developed a method to design experiments for the purpose of modelling causal effects that is significantly more sample-efficient than RCT. Applied linear programming to optimise hospital bed allocation.

Published work on AI explainability that respects both manifold structure and causal dependencies in the data. Designed explainable anomaly detection tool for financial fraud detection at a global institution.

Research focus on industry-relevant AI safety: explainability, fairness, privacy, and anomaly detection. Significant experience with variational autoencoders.

Projects on safe reinforcement learning from human preferences and on bias in AI.

Cambridge, Massachusetts

Developed a multi-purpose probabilistic model for particle showers at the LHC, using a novel and interpretable RNN framework. Predicted an LHC jet-substructure observable at state-of-the-art precision; later measured and confirmed by both ATLAS and CMS collaborations.

Taught sections, held discussions, graded homework and exams in both graduate and undergraduate courses: Electrodynamics, General relativity, Particle physics, Quantum field theory, Wave phenomena.

Geneva, Switzerland

Minneapolis, Minnesota

Orlando, Florida

Doctor Of Philosophy - Phd, Physics, 4.0

Bachelor'S Degree, Physics, Mathematics, 4.0