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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 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 |
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[Sep-2021] I will graduate in Jan 2022 and join 3M AI Lab as a Machine Learning Scientist. 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. The long-term goal of my research is to create ML systems that can assist human experts in scientific discoveries and innovations. There is nothing as practical as a good theory. — Kurt Lewin |
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Haozhu Wang , Zeyu Zheng, Chengang Ji, L. Jay Guo Machine Learning: Science and Technology, 2021 PDF/ code/ abridged NeurIPS workshop version/ 
Training a novel sequence generation network with Proximal Policy Optimization for automatically discovering near-optimal optical designs. |
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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. |
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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. |
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Jiaxuan Wang, Jeeheh Oh, Haozhu Wang , Jenna Wiens KDD, 2018 paper/ code Expert-yielded-estimates regularizer for incorporating expert knowledge into linear models. |
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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. |
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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. |
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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. |
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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 |
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| Conference review: | 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 |
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The source code of this website is from Jon Barron. (last update: Oct 2021)
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