Haozhu Wang's webpage

Haozhu Wang

I'm a Ph.D. student in EECS Department at University of Michigan, where I develop machine learning methods for scientific and healthcare applications. Previously, I received B.Eng. in Optoelectronics from a joint program at Tianjin University and Nankai University. I visited QNN lab at MIT RLE during my last year of undergraduate study.

I always enjoy collaborating with aspiring researchers. Please feel free to send me an email if you want to discuss about research ideas.

PS: I have many genius friends and this link will redirect you to one of them.

Email / CV / Google Scholar / Twitter / LinkedIn / Blog

Research

I'm developing machine learning algorithms for healthcare and physical science applications. Specifically, I'm interested in reinforcement learning, meta-learning, domain adaptation, and representation learning.

There is nothing more practical than a good theory. — Kurt Lewin

	Automated Optical Multi-layer Design via Deep Reinforcement Learning Haozhu Wang , Zeyu Zheng, Chengang Ji, L. Jay Guo arXiv, 2020 arXiv/ code 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) report 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) report 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 paper 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 paper Superconduting nanowire single photon detector as a highly-sensitive imager.

Teaching

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

Service

Reviewer:

NeurIPS'20, AMIA'20, MLHC'18-20

Useful Links

The source code of this website is from Jon Barron.

(last update: Aug-21 2020)