CV & Resume

Latest Curriculum Vitae & Resume (updated Aug 2024).

Research

Publications

  1. C. Wei, B. Zelditch, J. Chen, A. A. S. T. Ribeiro, J. K. Tay, B. O. Elizondo, K. Selvaraj, A. Gupta, and L. B. De Almeida. Neural Optimization with Adaptive Heuristics for Intelligent Marketing System. To appear in KDD 2024.
  2. E. Tuzhilina, T. J. Hastie, D. J. McDonald, J. K. Tay, and R. Tibshirani. Smooth multi-period forecasting with application to prediction of COVID-19 cases. Journal of Computational and Graphical Statistics, 2023.
  3. J. K. Tay, B. Narasimhan and T. Hastie. Elastic net regularization paths for all generalized linear models. Journal of Statistical Software, 2023, 106(1): 1-31. [PDF] [R package]
  4. J. K. Tay, N. Aghaeepour, T. Hastie, and R. Tibshirani. Feature-weighted elastic net: using "features of features" for better prediction. Statistica Sinica, 2021. [PDF] [R package]
  5. D. Shung, J. Huang, E. Castro, J. K. Tay, M. Simonov, L. Laine, R. Batra, and S. Krishnaswamy. Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit. Scientific Reports, 2021, 11:8827. [PDF]
  6. J. K. Tay, J. Friedman, and R. Tibshirani. Principal component-guided sparse regression. Canadian Journal of Statistics, 2021, 49(4):1222-1257. [PDF] [R package]
  7. D. Shung, C. Tsay, L. Laine, D. Chang, F. Li, P. Thomas, C. Partridge, M. Simonov, A. Hsiao, J. K. Tay, and A. Taylor. Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules. Journal of Gastroenterology and Hepatology, 2021, 36(6):1590-7. [PDF]
  8. J. K. Tay, and R. Tibshirani. Reluctant generalized additive modeling. International Statistical Review, 2020, 88(S1):S205-S224. [PDF] [R package]
  9. D. L. Shung, B. Au, R. A. Taylor, J. K. Tay, S. B. Laursen, A. J. Stanley, H. R. Dalton, J. Ngu, M. Schultz, and L. Laine. Validation of a machine learning model that outperforms clinical risk scoring systems for upper gastrointestinal bleeding. Gastroenterology, 2020, 158(1):160-7. [PDF]

Conferences & Workshops

  1. A. Gupta, S. S. Keerthi, A. Acharya, M. Cheng, B. O. Elizondo, R. Ramanath, R. Mazumder, K. Basu, J. K. Tay, and R. Gupta. Practical Design of Performant Recommender Systems using Large-scale Linear Programming-based Global Inference. In KDD 2023.

Preprints & Theses

  1. PhD Dissertation (2021): Extending the reach of the lasso and elastic net penalties: Methodology and practice.
  2. J. K. Tay, and R. Tibshirani. A latent factor approach for prediction from multiple assays. arXiv:1807.05675 [stat.ME], 2018.
  3. Senior Thesis (2010): Maximizing expected logarithmic utility in a regime-switching model with inside information.
  4. Junior Paper (2009): Construction of space-time block codes from a decoding point of view.

Teaching

Course Instructor

  1. STATS 32. Introduction to R for Undergraduates. Stanford University, Autumn 2019-2020. Course website here.
  2. STATS 32. Introduction to R for Undergraduates. Stanford University, Autumn 2018-2019. Course website here.
  3. STATS 302. Qualifying Exams Workshop (Applied Statistics). Stanford University, Summer 2017-2018.
  4. STATS 32. Introduction to R for Undergraduates. Stanford University, Autumn 2017-2018. Course material available here.

Teaching Assistant

  1. CS 229M/STATS 214. Machine Learning Theory. Stanford University, Winter 2020-2021.
  2. STATS 200. Introduction to Statistical Inference. Stanford University, Autumn 2020-2021.
  3. STATS 315B. Modern Applied Statistics: Data Mining. Stanford University, Spring 2019-2020.
  4. STATS 216. Introduction to Statistical Learning. Stanford University, Winter 2019-2020.
  5. STATS 191. Introduction to Applied Statistics. Stanford University, Winter 2018-2019.
  6. STATS 216V. Introduction to Statistical Learning. Stanford University, Summer 2017-2018.
  7. STATS 305C. Methods for Applied Statistics II: Applied Multivariate Statistics. Stanford University, Spring 2017-2018.
  8. STATS 216. Introduction to Statistical Learning. Stanford University, Winter 2017-2018.
  9. STATS 116. Theory of Probability. Stanford University, Summer 2016-2017.
  10. STATS 290. Paradigms for Computing with Data. Stanford University, Winter 2016-2017.
  11. STATS 116. Theory of Probability. Stanford University, Autumn 2016-2017.

Grader

  1. MAT 200. Linear Algebra and Multivariable Calculus for Economists. Princeton University, Fall 2009-2010.
  2. MAT 104. Calculus. Princeton University, Spring 2008-2009.
  3. MAT 103. Calculus. Princeton University, Fall 2008-2009.