About
I received my Ph.D. in Computer Sciences (advisor: David Page) from the University of Wisconsin, Madison in 2018. My Ph.D. research was dedicated to developing novel machine learning methods for massive quantities of clinical data to investigate some of the pressing problems in healthcare (e.g. signal detection, personalization).
Before Madison, I spent two wonderful years at the University of Minnesota, Duluth pursuing a master degree in Applied and Computational Mathematics (advisors: Zhuangyi Liu and Richard Maclin). I received my bachelor degree in Electrical Engineering from Honors School, Harbin Institue of Technology in 2012.
Selected Awards
Outstanding Graduate-Student Research Award, Computer Sciences Department, University of Wisconsin, Madison, 2018
Outstanding Graduate Award, Department of Mathematics and Statistics, University of Minnesota, Duluth, 2014
Graduate Student Service Award, Department of Mathematics and Statistics, University of Minnesota, Duluth, 2014
Recent Work
2018
Zhaobin Kuang, Yujia Bao, James Thomson, Michael Caldwell, Peggy Peissig, Ron Stewart, Rebecca Willett, and David Page.
A Machine-Learning Based Drug Repurposing Approach Using Baseline Regularization.
Invited book chapter. In Silico Methods for Drug Repurposing: Methods and Protocols.
Methods in Molecular Biology Series. Springer 2018.
Zhaobin Kuang.
Towards Learning with High Causal Fidelity from Longitudinal Event Data.
Doctoral Dissertation, 2018.
Zhaobin Kuang*, Sinong Geng*, Jie Liu, Stephen Wright, and David Page.
Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error.
Uncertainty in Artificial Intelligence, 2018 (UAI 2018).
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Zhaobin Kuang*, Sinong Geng*, Peggy Peissig, and David Page.
Temporal Poisson Square Root Graphical Models.
International Conference on Machine Learning, 2018 (ICML 2018).
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2017
Zhaobin Kuang, Sinong Geng, and David Page.
A Screening Rule for L1-Regularized Ising Model Estimation.
Neural Information Processing Systems, 2017 (NIPS 2017).
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Zhaobin Kuang, Peggy Peissig, Vitor Santos Costa, Richard Maclin, and David Page.
Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data.
Knowledge Discovery and Data Mining, 2017 (KDD 2017).
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Yujia Bao, Zhaobin Kuang, Peggy Peissig, David Page, and Rebecca Willett.
Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data.
Machine Learning in Health Care, 2017 (MLHC 2017).
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Finn Kuusisto, John Steill, Zhaobin Kuang, James Thomson, David Page, and Ron Stewart.
A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications.
American Medical Informatics Association Joint Summit 2017 (AMIA 2017).
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Sinong Geng, Zhaobin Kuang, and David Page.
An Efficient Pseudo-likelihood Method for Sparse Binary Pairwise Markov Network Estimation.
Technical Report, Arxiv, 2017.
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2016
Zhaobin Kuang, James Thomson, Michael Caldwell, Peggy Peissig, Ron Stewart, and David Page.
Computational Drug Repositioning Using Continuous Self-controlled Case Series.
Knowledge Discovery and Data Mining, 2016 (KDD 2016).
[PDF]
Zhaobin Kuang, James Thomson, Michael Caldwell, Peggy Peissig, Ron Stewart, and David Page.
Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data.
International Joint Conference on Artificial Intelligence, 2016 (IJCAI 2016).
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Note: * indicates equal contribution, authors are listed in alpha-beta/beta-alpha order.
Contact Information
Email: zkuang@wisc.edu
LinkedIn: https://www.linkedin.com/in/zhaobinkuang
Website: http://pages.cs.wisc.edu/~zhaobin/