Keshav Motwani
I am a third-year PhD student in biostatistics at the University of Washington, funded by an NSF Graduate Research Fellowship.
I am currently working with Ali Shojaie and Eardi Lila on methods for high-dimensional statistical learning and inference for dependent data settings, with applications to neuroimaging data. In my first project I developed an efficient method to estimate covariance matrices in multivariate mixed models. I previously worked with Daniela Witten on inference after using a machine learning model to predict the outcome variable of interest and data thinning, an alternative to sample splitting in unsupervised settings.
I did my undergraduate degree at the University of Florida in mathematics and statistics. I worked with Aaron Molstad and Rhonda Bacher on high-dimensional multinomial regression with multiresolution/hierarchical categories. I also worked with Todd Brusko and Victor Greiff on analyzing and developing software for immune receptor sequencing and single-cell genomics data in the context of type 1 diabetes.
You can see some of my work on my Google Scholar profile and my Github page.
My CV is available here.
Publications
Methods
- Keshav Motwani and Daniela Witten. Revisiting valid inference after prediction. Journal of Machine Learning Research,
2023 Paper, Code
- Ameer Dharamshi, Anna Neufeld, Keshav Motwani, Lucy L Gao, Daniela Witten,
and Jacob Bien. Generalized data thinning using sufficient statistics. Journal of the American Statistical Association, 2024 Paper
- Aaron J Molstad and Keshav Motwani. Multiresolution categorical regression for
interpretable cell type annotation. In press at Biometrics, 2023 Paper, Software
- Keshav Motwani, Rhonda Bacher, and Aaron J Molstad. Binned multinomial logistic regression for integrative cell type annotation. In press at Annals of Applied Statistics, 2023 Paper, Software
Applied
- Melanie R Shapiro, Xiaoru Dong, Daniel Perry, James M McNichols, Puchong Thirawatananond, Amanda L Posgai, Leeana Peters, Keshav Motwani, Richard Musca, Andrew Muir, and others. Human immune phenotyping reveals accelerated aging in type 1 diabetes. JCI Insight, 2023 Paper
- Chakravarthi Kanduri, Milena Pavlovic, Lonneke Scheffer, Keshav Motwani, Maria Chernigovskaya, Victor Greiff, and Geir K Sandve. Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification. GigaScience, 11, 2022 Paper
- Milena Pavlovic, Lonneke Scheffer, Keshav Motwani, Chakravarthi Kanduri, Rad- mila Kompova, Nikolay Vazov, Knut Waagan, Fabian LM Bernal, Alexandre Almeida Costa, Brian Corrie, and others. immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nature Machine Intelligence, 2021 Paper, Software
- Peter S Linsley, Fariba Barahmand-pour Whitman, Elisa Balmas, Hannah A DeBerg, Kaitlin J Flynn, Alex K Hu, Mario G Rosasco, Janice Chen, Colin ORourke, Elisavet Serti, Vivian H Gersuk, Keshav Motwani, and others. Autoreactive T cell receptors with shared germline-like α chains in type 1 diabetes. JCI Insight, 2021 Paper
- Keshav Motwani, Leeana D Peters, Willem H Vliegen, Ahmed Gomaa El-Sayed, Howard R Seay, M Cecilia Lopez, Henry V Baker, Amanda L Posgai, Maigan A Brusko, Daniel J Perry, and others. Human regulatory T cells from umbilical cord blood display increased repertoire diversity and lineage stability relative to adult peripheral blood. Frontiers in Immunology, 11:611, 2020 Paper, Code
- Emmi-Leena Ihantola, Henna Ilmonen, Anssi Kailaanmaki, Marja Rytkonen-Nissinen, Aurelien Azam, Bernard Maillere, Cecilia S Lindestam Arlehamn, Alessandro Sette, Keshav Motwani, Howard R Seay, and others. Characterization of proinsulin T cell epitopes restricted by type 1 diabetes–associated HLA class II molecules. The Journal of Immunology, 204(9):2349–2359, 2020 Paper
- Mohsen Khosravi-Maharlooei, Aleksandar Obradovic, Aditya Misra, Keshav Motwani, Markus Holzl, Howard R Seay, Susan DeWolf, Grace Nauman, Nichole Danzl, Haowei Li, and others. Cross-reactive public TCR sequences undergo positive selection in the human thymic repertoire. The Journal of Clinical Investigation, 129(6):2446–2462, 2019 Paper