I'm developing machine learning methods for speeding up scientific discoveries. Specifically, I'm intereseted in sample efficient reinforcement learning algorithms useful for real-world science and engineering applications. In the past, I've worked on model compression and ML applications in healthcare.
There is nothing more practical than a good theory. — Kurt Lewin
Automated Optical Multi-layer Design via Deep Reinforcement Learning Haozhu Wang ,
L. Jay Guo
Training a novel sequence generation network with Proximal Policy Optimization for automatically discovering near-optimal optical designs.
COVID-19 Risk Scoring in Los Angeles County
Litian Zhou, Wenxue Li, Zhangxing Bian, Yuxuan Cao, Xinyu Li, Weixiao Wang, Zixian Ma
Junhwan Kim, Zijin Chu, Yuxi Xie, Yueze Song, Chaoyi Wang, Ruopeng Wang, Linh Tran
Haozhu Wang*, L. Jay Guo*
RMDS COVID-19 Challenge, 2020 (*: correspondence)
Apply LSTM and LR models for spatio-temporal COVID-19 risk prediction.
Return to Work After Injury: A Sequential Prediction & Decision Problem
Haozhu Wang *,
Machinet Learning for Healthcare (clinical abstract), 2019 (*: equal contribution)
Apply Q-learning to insurance claim data to learn near-optimal dynamic treatment regimes.
Expert-yielded-estimates regularizer for incorporating expert knowledge into linear models.
Learning to Share: Simultaneous Parameter Tying and Sparsification in Deep Learning
Haozhu Wang *,
Mario A.T. Figueiredo,
ICLR, 2018 (*: equal contribution)
Group-ordered-weighted lasso (GrOWL) for deep model compression.
Surface Plasmon Polariton Laser based on a Metallic Trench Fabry-Perot Resonator
Haozhu Wang ,
Parag B. Deotare,
Henri J. Lezec
Science Advances, 2017
Surface plasmon polariton laser with a novel device structure.
Single-Photon Imager based on a Superconducting Nanowire Delay Line
Andrew E. Dane,
Adam N. McCaughan,
Daniel F. Santavicca,
Karl K. Berggren
Nature Photonics, 2017
Superconduting nanowire single photon detector as a highly-sensitive imager.