Wisconsin Announces Fall ’21 and Spring ’22 RAs

Yuchen Zeng (Computer Science), advised by Kangwook Lee (Electrical and Computer Engineering) and Stephen J. Wright (Computer Science), is interested in Trustworthy AI with a particular focus on fairness. Her recent work investigates training fair classifiers from decentralized data. Currently, she is working on developing a new fairness notion that considers dynamics.

Liu Yang (Computer Sciences), advised by Robert Nowak, Dimitris Papailiopoulos and Kangwook Lee (Electrical and Computer Engineering), works on the intersection of machine learning and deep learning. Currently, she is working on developing the structured pruning algorithm based on path norm regularization. She is also interested in finding the sparse network at initialization that can be further trained to achieve SOTA performance.

Zhiyan Ding (Mathematics), advised by Qin Li (Mathematics), works on applied and computational mathematics. More specifically, he uses PDE analysis tools for analyzing machine learning algorithms, such as Bayesian sampling and over-parameterized neural networks. The PDE tools, including gradient flow equation, and mean-field analysis, are helpful in formulating the machine (deep) learning algorithms into certain mathematical descriptions that are easier to handle. 

Sijia Fang (Statistics), advised by Karl Rohe (Statistics) and Sebastian Roch (Mathematics), works on social network and spectral analysis. More specifically, she is interested in hierarchical structures in social networks. She is also interested in phylogenetic tree and network recovery problems.

Max Hill (Mathematics), advised by Sebastien Roch (Mathematics) and Cecile Ane (Statistics), works in probability and mathematical phylogenetics. His recent work focuses on the impact of recombination in phylogenetic tree estimation.

Jeffrey Covington (Mathematics), advised by Nan Chen (Mathematics) and Sebastien Roch (Mathematics), works on data assimilation for model state estimation and prediction. He focuses on developing techniques for high-dimensional, nonlinear models with model error.

Jiaxin Hu (Statistics), advised by Miaoyan Wang (Statistics) and Jerry Zhu (Computer Science), works on statistical machine learning. Currently, she focuses on tensor/matrix data modeling and analysis with applications in neuroscience and social networks.

Shubham Kumar Bharti (Computer Science), advised by Jerry Zhu (Computer Science), and Kangwook Lee (Electrical and Computer Engineering), is interested in Reinforcement Learning, Fairness and Machine Teaching. His recent work focuses on fairness problems in sequential decision making. Currently, he is working on defenses against trojan attacks in Reinforcement Learning.