Haozhu Wang 

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.

Email  /  CV  /  Google Scholar  /  GitHub  /  LinkedIn / 

[Aug-2022] Our paper Dynamic prediction of work status for workers with occupational injuries: assessing the value of longitudinal observations has been published by Journal of the American Medical Informatics Association!

[Aug-2022] Our AWS Machine Learning blog on real-time fraud detection is online.

[July-2022] I served as a session chair in ICML 2022.

[Mar-2022] I joined Amazon ML Solutions Lab as a Research Scientist.

[Jan-2022] I defended my thesis Learning to Optimize: Applications in Physical Designs and Manufacturing on Jan 14th, 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.

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.

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.

We provide an analytical approach for evaluating the robustness of photonic integrated circuits. The method is verified by genetic algorithms.

Training a novel sequence generation network with Proximal Policy Optimization for automatically discovering near-optimal optical designs.

Apply LSTM and LR models for spatio-temporal COVID-19 risk prediction.

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.

Group-ordered-weighted lasso (GrOWL) for deep model compression.

Surface plasmon polariton laser with a novel device structure.

Superconduting nanowire single photon detector as a highly-sensitive imager.

The source code of this website is from Jon Barron.

(last update: Sep 2022)