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: The goal of my research is to make generative models ready for the real-world, by focusing on improving three aspects: safety, robustness and efficiency. As part of my PhD, some areas that I have worked on include gradient flows, diffusion models, variational inference 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 quantum algorithms.

Email / CV / Google Scholar / GitHub / LinkedIn

Publications

	Out-of-Distribution Detection with a Single Unconditional Diffusion Model Alvin Heng, Alexandre H. Thiery, Harold Soh *Advances Neural Information Processing Systems 37 (NeurIPS), 2024* arxiv / code
	Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models Alvin Heng, Harold Soh *Advances Neural Information Processing Systems 36 (NeurIPS), 2023, Spotlight* arxiv / code
	Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series Abdul Fatir Ansari, Alvin Heng, Andre Lim, Harold Soh *International Conference on Machine Learning (ICML), 2023, Oral* arxiv / code
	Generative Modeling with Flow-Guided Density Ratio Learning Alvin Heng, Abdul Fatir Ansari, Harold Soh *Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 2024* arxiv / code
	Three-Magnon Bound State in the Quasi-One-Dimensional Antiferromagnet a-NaMnO2 Rebecca L Dally, Alvin Heng, Anna Keselman, Mitchell M Bordelon, Matthew B Stone, Leon Balents, Stephen D Wilson *Physical Review Letters* arxiv / paper
	Pair Hopping in Systems of Strongly Interacting Hard-Core Bosons Alvin Heng, Wenan Guo, Anders W Sandvik, Pinaki Sengupta *Physical Review B* arxiv / paper
	Optimal Fee Structure For Efficient Lightning Networks Alvin Heng, Ling Feng, Siew Ann Cheong, Rick Siow Mong Goh *IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS), 2018* paper

Website credits: 1, 2

Alvin Heng

Publications

Out-of-Distribution Detection with a Single Unconditional Diffusion Model

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