Sanvesh Srivastava is currently an Assistant Professor in the Department of Statistics and Actuarial Science at The University of Iowa. His research aims to develop flexible Bayesian methods and efficient computational algorithms for big data sets, tailored for both their complexity and size. Motivating examples include big data in genomics, medical imaging, and recommender systems. Simultaneously optimizing for the size and complexity is a challenge with current Bayesian methods. He is developing novel and computationally tractable Bayesian methods using principles from machine learning and optimal transportation. Before coming to the University of Iowa, Sanvesh received his Ph.D. in Statistics in August, 2013 from Purdue University, where he also won I.W. Burr Award for "promise of contribution to the profession as evidenced by academic excellence in courses and exams, by the quality of research, and by excellence in teaching and consulting." After Ph.D., he spent two years at Duke University and Statistical and Applied Mathematical Sciences Institute (SAMSI) as a postdoctoral researcher. He has extensive experience in collaborating with scientists and teaching statistics to students from diverse areas and varied expertise.
Areas of Research Interest
- Bayesian methods for complex and large data (big data); Computational Statistics; Machine Learning: My current research is motivated by big data in genomics and medical imaging. Bayesian non-parametric methods provide a flexible way to model the complexity of these data that is characterized by nonlinear dependence and low signal strength. This modeling framework also facilitates extensions that are able to capture data-specific patterns. The computational cost of this flexibility scales with the size of the data and the number of parameters used to model the complexity of the data; therefore, Bayesian computations are expensive for big data, which in turn challenges classical Bayesian inferential approaches. As such, my specific research focus is twofold: (a) data analysis with computationally feasible parameter estimation and posterior sampling; and (b) data analysis with only computationally feasible parameter estimation.
- Srivastava, S., Xu, Y. (2021). Distributed Bayesian Inference in Linear Mixed-Effects Models. Journal of Computational and Graphical Statistics (appeared online).
- Yao, H., Srivastava, S., Swyers, N. C., Han, F., Doerge, R. W. & Birchler, J. A. (2020). Inbreeding depression in genotypically matched diploid and tetraploid maize. Frontiers in Genetics, 11, 1380.
- Srivastava, S., Xu, Y. (In Press). Location-Scatter WASP.
- Bathla, G., Derdeyn, C. P., Moritani, T., Freeman, C. W., Srivastava, S., Song, J. & Soni, N. (2020). Retrospective, Dual-Center Review of Imaging Findings In Neurosarcoidosis At Presentation: Prevalence And Imaging Sub-types. Clinical Radiology, 75(10), 796.e1-796.e9.
- Srivastava, S. (2019). Distributed Expectation Maximization.
- Srivastava, S., DePalma, G. & Liu, C. (2019). Distributed Expectation-Maximization algorithm for massive data: The DEM algorithm. Journal of Computational and Graphical Statistics, 28(2), 233-243. DOI: https://doi.org/10.1080/10618600.2018.1497512.
- Srivastava, S., Li, C. (2018). Wasserstein Barycenter.
- Savitsky, T. D., Srivastava, S. (2018). Scalable Bayes under Informative Sampling. Scandinavian Journal of Statistics, 45(3), 534-556. DOI: 10.1111/sjos.12312.
- Srivastava, S., Li, C. & Dunson, D. B. (2018). Scalable Bayes via Barycenter in Wasserstein Space. Journal of Machine Learning Research, 19(1), 312-346.
- Minsker, S., Srivastava, S., Lin, L. & Dunson, D. B. (2017). Robust and scalable Bayes via a median of subset posterior measures. Journal of Machine Learning Research, 18(1), 4488-4527.
- Srivastava, S. (2020, June) Distributed Bayesian varying coefficient modeling using a Gaussian process prior. Conference Presentation presented at Symposium on Data Science and Statistics, American Statistical Association, Pittsburgh, Pennsylvania.
- Srivastava, S. (2017, May) Scalable Bayes via Barycenter in Wasserstein Space. Workshop presented at Workshop on Optimal Transport meets Probability, Statistics, and Machine Learning, BIRS-CMO, Oaxaca, Mexico.
Selected Grants and Contracts
- Guhaniyogi, Rajarshi (Co-Investigator), Srivastava, Sanvesh (Co-Investigator) Grant Research, Basic. Aggregated Monte Carlo: A General Framework for Distributed Bayesian Inference In Massive Spatiotemporal Data. Sponsored by National Science Foundation. Funded. September 17, 2018 - May 31, 2022.