Events

NDMC Talk series invited talk by Prof. Swapnil Mishra
NDMC Talk series: Invited talk by Prof. Swapnil Mishra, Assistant Professor, Saw Swee Hock School of Public Health and Institute of Data Science, National University of Singapore (NUS)

November 27, 2024

Prof. Swapnil Mishra delivered an invited online talk on “Inferential Artificial Intelligence (iAI): Cases Studies in Computational Statistics, Machine Learning, and Global Health” on the 27th of November, 2024.

Swapnil Mishra is Assistant Professor at the National University of Singapore with a primary appointment at the Saw Swee Hock School of Public Health, and joint appointments at Institute of Data Science, and Department of Statistics and Data Science. His research focuses on applying and developing statistical machine learning techniques for the broader and messier world of science and public policy, especially global health. He develops flexible and scalable models for understanding various spatiotemporal data, for example, epidemics (COVID-19, Malaria, HIV) and crime. His contributions to COVID-19 pandemic modeling, Bayesian inference, computational social science and ma- chine learning earned him the prestigious NUS Presidential Young Professorship in 2024, NRF Fellowship in 2023, the Blackwell-Rosenbluth Award in 2022, and the SPI-M-O Award for Modelling and Data Support in 2022. In 2022, he co-founded the Machine Learning & Global Health network (www.MLGH.net), a collaborative initiative spanning three continents that brings together researchers to address global health challenges.

Abstract

Machine learning is the computational beating heart of the modern AI renaissance. Behind the hype, a range of machine learning and computational statistical methods are quietly revolutionizing our approach to difficult statistical and scientific inference problems. I will present my perspective on the emerging field of "inferential Artificial Intelligence" (iAI) through a series of case studies on important global health challenges. I conceive of iAI as a big tent, encompassing modern probabilistic programming, replicable data scientific workflows, methods for assessing Big Data quality, uncertainty quantification, active learning, and a range of computational and deep learning approaches to transform applied statistical analyses.