Just a short post on past and current research.
A theme of my research has been about the various uncertainties in the data and how those affect the rest of the modeling process, e.g., the selection of a parametric vs non-parametric family for fitting, the metrics used, etc. What mathematical tools do we need to be able to carry out inference confidently when almost every component of the model is noisy?
The titles of a few papers I have published are below. You can find a more up-to-date list on Google Scholar which also includes a list of patents I have authored with colleagues from Babylon Health.
2019 Tuning the semantic consistency of active medical diagnosis: a walk on the semantic simplex, with A. Buchard, A. Navarro, et al. Presented at the Stanford Symposium “Fronters of AI-assisted care”
2018 Universal Marginalizer for Amortised Inference and Embedding of Generative Models, with R. Walecki, A. Buchard, et al. Submitted to AISTATS. arXiv: 1811.04727
2017 A Universal Marginalizer for Amortized Inference in Generative Models, NeurIPS workshop on Advances in Approximate Bayesian Inference, 2017, with L. Douglas, I. Zarov, et al. arXiv: 1711.00695.
2017 Information criteria for quantifying loss of reversibility in parallelized KMC, with M. Katsoulakis, L. Rey-Bellet. Accepted at the Journal of Computational Physics 328, 438-454.
2017 How biased is your model? Concentration Inequalities, Information and Model Bias, with M. Katsoulakis, L. Rey-Bellet and J. Wang. Accepted at the IEEE Transactions on Information Theory.
2016 Information metrics for long-time errors in splitting schemes for stochastic dynamics and parallel Kinetic Monte Carlo, with M. Katsoulakis and L. Rey-Bellet. Accepted at the SIAM Journal on Scientific Computing 38 (6), A3808-A3832.