Assistant Professor
(205) 934-2154
University Hall 4041
Research and Teaching Interests: Distributed Learning and Federated Learning; Variable Selection; Generalized Linear Models; Bioinformatics; Deep Learning; Optimal Design; Undirected Graphic Models; Financial Derivatives
Office Hours: By appointment
Education:
- B.S., Nankai University, Mathematics
- M.S., Louisiana State University, Mathematics
- Ph.D., University of Illinois at Chicago, Statistics
I originated from Chongqing, the biggest city in China, where I grew up until my college age. After I got my Ph.D. degree in Statistics from University of Illinois at Chicago, I joined Northwestern University as a Postdoctoral Fellow in the Department of Statistics and Data Science and NSF-Simons Center for Quantitative Biology.
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Research Interests
One of my major research interests is the communication constraint scenarios in distributed learning and federated learning. I suggested the representative framework to fulfill the challenge of data privacy, data localization, and low bandwidth. In the representative framework, artificial observations are constructed based on local data and system information. Regular analysis and inferences are performed on these pseudo data points (representatives). I proposed the Score-Matching Representative approach for Generalized Linear Models, where the representatives are constructed by matching score function values locally. I am working on the extension of representative framework to general machine learning problems.
Another research area of mine is bioinformatics. I worked on ribosome footprint discrepancy and nucleosome binding problems. I am currently interested in single cell RNA analysis.
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Select Publications
- Li, K., Carroll, M., Vafabakhsh, R., Wang, X., Wang, J., “DNAcycP: a Novel Tool for DNA Cyclizability Prediction”, Nucleic Acids Research, March 2022; 50(6): 3142-3154. DOI:10.1093/nar/gkac162.
- Li, K., Yang, J. , “Score Matching Representative Approach for Big Data Analysis with Generalized Linear Model”, Electronic Journal of Statistics, 2022; 16(1): 592-635. DOI:10.1214/21-EJS1965.
- Li, K., Hope, M., Wang, X., Wang, J., “Ribo-DiPA: A Novel tool for differential pattern analysis in Ribo-seq data”, Nucleic Acids Research, December 2020; 48(21): 12016-12029. DOI:10.1093/nar/gkaa1049.
- Developed Software and Packages