Wisconsin Adds Eight RA’s for IFDS

Changhun Jo (Mathematics), advised by Kangwook Lee (Electrical and Computer Engineering) and Sebastien Roch (Mathematics), is working on the theoretical understanding of machine learning. His recent work focuses on finding an optimal data poisoning algorithm against a fairness-aware learner. He also works on finding the fundamental limit on sample complexity of matrix completion in the presence of graph side information.

Lorenzo Najt (Mathematics), advised by Jordan Ellenberg (Math), investigates algorithmic and inferential questions related to ensemble analysis of gerrymandering. Related to this, he is interested in sampling connected graph partitions, and is working with Michael Newton (Statistics) to investigate applications to hypothesis testing with spatial data. He is also working on some questions about the fixed parameter tractability of polytope algorithms.

Yuetian Luo is working with Anru Zhang (Statistics) and Yingyu Liang (Computer Science) on non-convex optimization problems involving low rank matrices and implicit regularization for non-convex optimization methods. We develop novel fast algorithms with provable faster convergence guarantees than common first order methods. The algorithm is based on a new sketching scheme we developed for high dimensional problems. Also we are interested in investigating implicit regularization for different non-convex optimization methods and different initialization schemes.

Yingda Li (Mathematics) is working with Nan Chen (Mathematics) and Sebastien Roch (Mathematics) on uncertainty quantification and data assimilation. She aims to develop statistically accurate algorithms for solving high-dimensional nonlinear and non-Gaussian dynamical systems. She also works on predicting complex nonlinear turbulent dynamical systems with imperfect models and insufficient training data.

Mehmet Furkan Demirel (Computer Sciences), advised by Yingyu Liang (Computer Sciences) and Dimitris Papailiopoulos (Electrical and Computer Engineering), focuses on molecule property prediction and construction of proper representations of molecules for learning algorithms. He also works on representation learning for graph-structured data and graph neural networks.

Cora Allen-Savietta (Statistics) works with Cécile Ané (Statistics, Botany) and Sebastien Roch (Mathematics) to develop efficient methods to infer evolutionary history and identify ancient hybridizations. As the challenge of this work is to reconstruct ancestry using only modern data, she explores the identifiability of a network’s topology from sequence data only at the leaves. She is also expanding tree rearrangements and existing search strategies to explore the large and discrete space of semi-directed networks. Her work has applications to understanding the history of eukaryotes, viruses, languages, and more.

Shashank Rajput (Computer Science), advised by Dimitris Papailiopoulos (Electrical and Computer Engineering), Kangwook Lee (Electrical and Computer Engineering) and Stephen Wright (Computer Science), works on problems in distributed machine learning and optimization. He works on developing techniques which are fast and scalable for practical use, and have theoretical guarantees. Currently, he is working on finding better shuffling mechanisms that beat random reshuffling as well as developing algorithms for training neural networks by only pruning them.

Shuqi Yu (Mathematics), advised by Sebastien Roch (Mathematics) and working with Karl Rohe (Statistics) on large scale network models. She aims to establish theoretical guarantees for a new estimator of the number of communities in a stochastic blockmodel. She is also interested in phylogenetics questions, in particular, she works on the identifiability of the species phylogeny under an horizontal gene transfer model.