I am a Research Scientist at Amazon ML Solutions Lab. I received my Ph.D. in Electrical and Computer Engieering from EECS Department at University of Michigan, where I was advised by Prof. L. Jay Guo. My research focuses on reinforcement learning, meta-learning, and generative models. On the application side, I collaborate with domain experts to solve challenging problems in physical sciences and healthcare. My long-term research goal is to create ML systems that can assist human experts in scientific discoveries and innovations. Previously, I received my B.Eng. in Optoelectronics from a joint program at Tianjin University and Nankai University.
I always enjoy collaborating with aspiring researchers. Please feel free to send me an email if you want to discuss about research ideas.
Dynamic prediction of work status for workers with occupational injuries: assessing the value of longitudinal observations
Brian T Denton
Journal of the American Medical Informatics Association, 2022
We develop a forecasting model to predict return-to-work after occupational injuries based on longitudinal claim data. The model may allow case managers to better allocate medical resources and help speed up patients' recover process.
NEUTRON: Neural Particle Swarm Optimization for Material-Aware Inverse Design of Structural Color Haozhu Wang,
L. Jay Guo
We propose a hybrid machine learning and optimization method that combines mixture density networks and particle swarm optimization for accurate and efficient structural color inverse design.
Benchmarking Deep Learning-based Models on Nanophotonic Inverse Design Problems
Haozhu Wang* ,
L. Jay Guo*
Opto-Electronic Science, 2022 (*: correspondence)
We provide extensive benchmarking results on accuracy, diversity, robustness for commonly used deep learning models in nanophotonic inverse designs. The findings can help researchers select models that best suit their design problems.
An Analytical Method for Evaluating the Robustness of Photonic Integrated Circuits
Haozhu Wang ,
Journal of Lightwave Technology, 2022
We provide an analytical approach for evaluating the robustness of photonic integrated circuits. The method is verified by genetic algorithms.
Automated Optical Multi-layer Design via Deep Reinforcement Learning Haozhu Wang ,
L. Jay Guo
Machine Learning: Science and Technology, 2021
abridged NeurIPS workshop version/
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.