Kevin Jiang, Best Use of Statistical Innovations Award – Fall 2023

Congratulations to Kevin Fu Jiang, a mathematics-statistics major in Columbia College, on winning the “Best Use of Statistical Innovations” Award at the 2023 Undergraduate Computer and Data Science Research Fair. His research topic was, OpenDataVal: A Unified Benchmark for Data Valuation, and he was advised by Yongchan Kwon, Assistant Professor in the Department of Statistics.

This work has also been accepted at Advances in Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks. In this paper, Kevin Fu Jiang, Weixin Liang, James Zou, and Yongchan Kwon introduce OpenDataVal, an easy-to-use and unified benchmark framework that empowers researchers and practitioners to apply and compare various data valuation algorithms . . . OpenDataVal is publicly available with comprehensive documentation.

The year’s Undergraduate Computer and Data Science Research Fair was hosted by the Data Science Institute, and was inspired by the way interdisciplinary research brings together ideas from different fields to generate new insights and perspectives. Approximately 20 exhibitors displayed posters on original research projects conducted by undergraduate students across Columbia and Barnard. The Fair was sponsored in part by the Department of Statistics; the Vagelos Computational Science Center; Computer Science, Barnard College; and Columbia Data Science Society. Judges included Joyce Robbins and Alex Pijyan, Lecturers in Discipline in the Department of Statistics; Susan McGregor, Research Scholar in the Data Science Institute; Eugene Wu, Associate Professor in the Department of Computer Science; and Rebecca Wright, Professor and Director of the Vagelos Computational Science Center at Barnard College.

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Before data analysis: Additional recommendations for designing experiments to learn about the world.
Andrew Gelman (2023).
Pathfinder: Parallel quasi-Newton variational inference.
Andrew Gelman (2022).
Toward a taxonomy of trust for probabilistic machine learning. Science Advances, 9(7), eabn3999.
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Tagasovska, N., Chavez-Demoulin, V., & Vatter, T. (2020, November).
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Credit Risk, Liquidity, and Bubbles. International Review of Finance, 20(3), 737-746.
Jarrow, R., & Protter, P. (2020).
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Li, H., Aue, A., Paul, D., Peng, J., & Wang, P. (2020).
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A Joint MLE Approach to Large-Scale Structured Latent Attribute Analysis. Journal of the American Statistical Association, (just-accepted), 1-39.
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The continuous categorical: a novel simplex-valued exponential family. In International Conference on Machine Learning (pp. 3637-3647). PMLR.
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High-frequency analysis of parabolic stochastic PDEs. The Annals of Statistics, 48(2), 1143-1167.
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