I am interested in statistics and machine learning with a Bayesian flavor. My research encompasses statistical research in engineering disciplines, cybersecurity, and classical antiquity. I have worked in scalable estimation for graphical models, variational inference, robustness to errors-in-variables, and Bayesian machine learning.
Joseph Dexter, Theodore Katz, Nilesh Tripuraneni, Tathagata Dasgupta, Ajay Kannan, James Brofos, Jorge Bonilla Lopez, Lea Schroeder, Adriana Casarez, Maxim Rabinovich, Ayelet Haimson Lushkov, and Pramit Chaudhuri. Quantitative criticism of literary relationships. Proceedings of the National Academy of Sciences, 114(16), 2017.
James Brofos. Man vs. machine: The rivalry in chess. Dartmouth Undergraduate Journal of Science, 16(2), 2014.
James Brofos, Rui Shu, and Frank Zhang. The optimistic method for model estimation. In 15th International Symposium on Intelligent Data Analysis (IDA). Springer International Publishing, 2016.
James Brofos and Rui Shu. Parallelization of minimum probability flow on binary Markov random fields. In 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2015.
James Brofos, Michael Downs, and Rui Shu. Detecting evasive malware with loss-calibrated Bayesian neural networks. In International Conference on Machine Learning (ICML) Workshop on Machine Learning for Safety-Critical Applications in Engineering, 2018.
James Brofos, Michael Downs, and Rui Shu. Deep Bayesian defenses against adversarial malware. In Applied Machine Learning Days (AMLD), 2018.
Rui Shu, James Brofos, Frank Zhang, Hung Hai Bui, Mohammad Ghavamzadeh, and Mykel Kochenderfer. Stochastic video prediction with conditional density estimation. In European Conference on Computer Vision (ECCV) Workshop on Action and Anticipation for Visual Learning, 2016.
James Brofos, Rui Shu, Michael Downs, and Matthew Jin. Leveraging deep neural networks as kernels for survival analysis. In Neural Information Processing Systems (NIPS) Workshop on Machine Learning in Healthcare, 2015.
James Brofos, Frank Zhang, and Rui Shu. A statistical approach to the circular error probable, 2017.
James Brofos, Frank Zhang, and Rui Shu. Batch Implicit Gradient Descent, 2017.
Statistical Estimation of Ising Graphical Models. Dartmouth College Department of Mathematics.
Communication Complexity of Distributed Statistical Algorithms. Dartmouth College Department of Computer Science.