Keegan Hines

Scientific Interests

I am interested in applying machine learning techniques to various domains (including cybersecurity and finance) and assuring that these algorithms are reliable and safe.

My research focus in graduate school was in modeling the biophysics of protein signalling and conformational change. I've been particularly interested in approaching basic questions of protein biophysics with methods developed in the statistics and machine learning communities, especially nonparametric Bayesian methods.


Counterfactual Explanations for Machine Learning: A Review

Sahil Verma, John Dickerson, and Keegan Hines

NeurpIPS 2020 RSA Workshop (Best Paper Award)

DeepTrax: Embedding Graphs of Financial Transactions

Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Antonia Gogoglou, and Keegan Hines


A Multi-task Network for Localization and Recognition of Text in Images

Reza Sarshogh and Keegan Hines


A Primer On Bayesian Inference For Biophysical Systems

Keegan Hines

Biophysical Journal, May 2015

Analyzing Single-Molecule Time Series via Nonparameteric Bayesian Inference

Keegan Hines, John Bankston, Rick Aldrich

Biophysical Journal, February 2015

Bayesian Approaches for Modeling Protein Biophysics

Keegan Hines

My disseration, a real page turner.

Determination of parameter identifiability in nonlinear biophysical models: A Bayesian Approach

Keegan Hines, Tom Middendorf, Rick Aldrich

Journal of General Physiology, March 2014

Inferring subunit stoichiometry from single molecule photobleaching

Keegan Hines

Journal of General Physiology, June 2013


Here is a current academic CV, and a more succinct resume.