A winner of the Samsung AI Researcher of the Year award (2020), Flaxman has a Ph.D. in machine learning and public policy from Carnegie Mellon University.
He was previously faculty at Imperial College London where he led landmark studies on the effectiveness of non-pharmaceutical interventions during the first wave of the Covid-19 pandemic in Europe (Flaxman et al, Nature, 2020) and on pandemic-associated orphanhood (Hillis et al, Lancet, 2021).
Within the field of machine learning, he works on statistical machine learning methods for spatiotemporal data, Bayesian methods, and kernel methods and has published in NeurIPS, ICML, KDD, AISTATS, and UAI.
You can visit his personal webpage here.
Advanced Machine Learning
Thursday, October 13 –
Saturday, October 15, 2022
This seminar assumes a basic familiarity with machine learning and covers statistical machine learning, Bayesian machine learning, kernel methods and Gaussian processes, Bayesian probabilistic programming with MCMC and variational inference, Bayesian Additive Regression Trees, deep generative modeling with variational autoencoders,...View Details
Machine learning has emerged as a major field at the intersection of statistics and computer science where the goal is to create reliable and flexible predictive models. This seminar offers a thorough introduction to supervised machine learning methods. Topics covered...View Details