Alvin Heng

Hello! I am currently a PhD student at the National University of Singapore, where I work on deep generative models as part of CLeAR Lab with Harold Soh.

Research: My research interests lie in the field of deep generative models. Some areas that I have worked on include gradient flows, diffusion models, variational inference, neural ordinary differential equations (ODEs) and time-series prediction. To learn more about my background and research, please refer to my CV and Google Scholar profile linked below.

Previously: In my past life, I worked as a physicist, specializing in the field of condensed matter physics during my undergraduate studies. My primary focus involved conducting numerical simulations to study exotic physical phenomena in quantum materials. During this time, I worked with Pinaki Sengupta at the Nanyang Technological University, as well as Anna Keselman and Leon Balents at the Kavli Institute for Theoretical Physics at UC Santa Barbara. As a Masters student at the University of Toronto, I worked with Nathan Wiebe to explore how classical deep learning techniques can be used to improve the performance of certain quantum algorithms.

Email  /  CV  /  Google Scholar  /  GitHub  /  LinkedIn

Publications

Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models

Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series

Generative Modeling with Flow-Guided Density Ratio Learning

Three-Magnon Bound State in the Quasi-One-Dimensional Antiferromagnet a-NaMnO2

Pair Hopping in Systems of Strongly Interacting Hard-Core Bosons

Optimal Fee Structure For Efficient Lightning Networks

Website credits: 1, 2