Haozhu Wang 

I am a Machine Learning Scientist at 3M. I earned 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  /  Twitter /  LinkedIn /  Blog

profile photo

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

[Sep-2021] I will graduate in Jan 2022 and join 3M AI Lab as a Machine Learning Scientist.

NEUTRON: Neural Particle Swarm Optimization for Material-Aware Inverse Design of Structural Color
Haozhu Wang, L. Jay Guo
Under review, 2022
preprint/ code

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
Taigao Ma, Mustafa Tobah, 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
Hanfa Song, Haozhu Wang , Vien Van
Journal of Lightwave Technology, 2022

Derived a bound for evaluating how fabrication errors affect the performance of photonic integrated circuits. The results were verified by a numerical apporach based on genetic algorithms.

Automated Optical Multi-layer Design via Deep Reinforcement Learning
Haozhu Wang , Zeyu Zheng, Chengang Ji, L. Jay Guo
Machine Learning: Science and Technology, 2021
PDF/ code/ abridged NeurIPS workshop version/ DOI

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
Erkin Ötleş* Haozhu Wang *, Suyanpeng Zhang, Brian Denton, Jenna Wiens, Jon Seymour
Machinet Learning for Healthcare (clinical abstract), 2019 (*: equal contribution)

Apply Q-learning to insurance claim data to learn near-optimal dynamic treatment regimes.

Learning Credible Models
Jiaxuan Wang, Jeeheh Oh, Haozhu Wang , Jenna Wiens
KDD, 2018
paper/ code

Expert-yielded-estimates regularizer for incorporating expert knowledge into linear models.

Learning to Share: Simultaneous Parameter Tying and Sparsification in Deep Learning
Dejiao Zhang*, Haozhu Wang *, Mario A.T. Figueiredo, Laura Balzano
ICLR, 2018 (*: equal contribution)
paper/ code

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

Surface Plasmon Polariton Laser based on a Metallic Trench Fabry-Perot Resonator
Wenqi Zhu, Ting Xu, Haozhu Wang , Cheng Zhang, Parag B. Deotare, Amit Agrawal, 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
Qing-Yuan Zhao, Di Zhu, Niccolò Calandri, Andrew E. Dane, Adam N. McCaughan, Francesco Bellei, Hao-Zhu Wang, Daniel F. Santavicca, Karl K. Berggren
Nature Photonics, 2017

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

cs188 EECS 442 Computer Vision (F20), Prof. Andrew Owens
Graduate Student Instructor

EECS 504 Fundations of Computer Vision (W20), Prof. Andrew Owens
Graduate Student Instructor

EECS 545 Machine Learning (F17), Prof. Mert Pilanci
Graduate Student Instructor

Conference review: ICML'22, ICLR'22, NeurIPS'20-21, AMIA'20-21, MLHC'18-21
Journal review: Journal of Physics Communications, AIP Advances
Workshop review: NeurIPS'20-21 Meta-Learning Workshop, NeurIPS'21 Machine Learning and the Physical Sciences Workshop
Useful Links

There is nothing as practical as a good theory.Kurt Lewin

The source code of this website is from Jon Barron.

(last update: Jan 2022)