Using funds from the National Science Foundation grant (under NSF’s TRIPODS and Convergence Programs), IFDS is funding five Research Assistants this semester to collaborate across departments on IFDS research. Each person is advised by one primary and one secondary adviser, all of them members of IFDS and all affiliated with the Wisconsin Institute for Discovery.
Zachary Charles (Electrical and Computer Engineering) works with Dimitris Papailiopoulos (Electrical and Computer Engineering) and Stephen Wright (Computer Science) on problems at the intersection of machine learning and optimization. He focuses on understanding the optimization landscape of machine learning problems from a geometric perspective as well as designing efficient distributed algorithms. He is also interested in adversarial attacks in machine learning and their underlying geometry.
Blake Mason (Electrical and Computer Engineering), advised by Rob Nowak (Electrical and Computer Engineering) and Jordan Ellenberg (Mathematics), is investigating problems of learning from comparative data, such as ordinal comparisons and similarity/dissimilarity judgements. In particular, he is studying metric learning and clustering problems in this setting with applications to personalized education, active learning, and representation learning. Additionally, he studies approximate optimization techniques for extreme classification applications.
Tun Lee Ng (Statistics), advised by Michael Newton (Statistics and BMI) and collaborating with Stephen Wright (Computer Science), is working on a general-purpose approximation approach to Bayesian analysis, in which repeated optimization of a randomized objective function provides approximate samples from the joint posterior distribution. The aim of this project is to extend existing theoretical support to a wider class of models. The weighting approach could become a useful alternative to other approximations (e.g. Markov chain Monte Carlo; variational Bayes). Potential data-analysis application includes improving hypothesis testing in brain imaging and genomics.
Alisha Zachariah (Mathematics) is working with Justin Hsu (Computer Science) and Nigel Boston (Mathematics) on applications of differential privacy to the multiparty optimal power flow problem. She is investigating further ways to secure existing algorithms for the multi-party optimal power flow problem that involve techniques in the area of joint differential privacy. We are looking to achieve this additional level of security while keeping efficiency in mind.
Xiaomin Zhang (Computer Sciences), advised by Po-Ling Loh (Electrical and Computer Engineering) and Jerry Zhu (Computer Sciences) is investigating problems in statistical M-estimation, particularly in relation to applications involving “machine learning debugging.” She is formulating statistical algorithms for identifying buggy subsets of data with rigorous theoretical guarantees, and understanding the role of clean vs. buggy data pools in informing the quality of a statistical estimator.