Murali Haran

Biography

Murali Haran is Professor of Statistics at Penn State.

He received his Ph.D. and M.S. in Statistics from the University of Minnesota, and his B.S. in Computer Science from Carnegie Mellon University.

His research is in the areas of statistical computing, primarily Markov chain Monte Carlo algorithms. He also works on spatial models, particularly latent Gaussian random fields, complex computer models ("computer experiments"), and statistical emulation and calibration. Much of his research is heavily motivated by cross-disciplinary research in climate science and infectious diseases.

He served co-editor of Bayesian Analysis from 2016 to 2018, and has served as associate editor for a number of journals, including Technometrics, The American Statistician, Journal of Agricultural, Biological and Environmental Statistics, Biometrics and Bayesian Analysis. From 2013-2014 he was Chair of the American Statistical Association (ASA) Section on Risk Analysis, and was the treasurer for the International Society for Bayesian Analysis (ISBA) from 2014 to 2016.

At Penn State, he served as Chair of the Penn State Statistics Undergraduate Program from 2012 to 2016. He has been part of several climate science-related organizations/initiatives, including NSF-sponsored SCRiM (Sustainable Climate Risk Management), a multi-institution network with Penn State as the hub. He was the director of the Penn State Node of the NSF research network STATMOS, and a member of the ASA Advisory Committee on Climate Change Policy (2009 - 2014).

Honors and Awards

• Fellow of the American Statistical Association (2016).
   
• 2015 Abdel El-Shaarawi Young Researcher (AEYR) Award to "recognize and honor outstanding contributions to the field of environmetrics".
   
• 2014 Young Investigator Award given by the American Statistical Association (ASA) Section on Statistics and the Environment (ENVR).

Publications

• Lee, B.*, Haran, M., Fuller, R.W., Pollard, D., and Keller, K. (2020+) A Fast Particle-Based Approach for Calibrating a 3-D Model of the Antarctic Ice Sheet, to appear in the Annals of Applied Statistics.
   
• Park, J. and Haran, M. (2018) Bayesian Inference in the Presence of Intractable Normalizing Functions, Journal of the American Statistical Association, 113, 523, 1372-1390.
   
• Chang, W., Haran, M., Applegate, P., and Pollard, D. (2016) Calibrating an ice sheet model using high-dimensional binary spatial data, Journal of the American Statistical Association, 111, 513, 27-72.
   
• Goldstein, J., Haran, M., Simeonov, I., Fricks, J., and Chiaromonte, F. (2015) An attraction-repulsion point process model for respiratory syncytial virus infections Biometrics, 71, 2, pp 376--385 ( Winner of student paper competition at the Graybill/ENVR 2014 conference).
   
• Jandarov, R., Haran, M., Bjornstad, O.N. and Grenfell, B.T. (2014) Emulating a gravity model to infer the spatiotemporal dynamics of an infectious disease Journal of the Royal Statistical Society, Series C, 63, 3, pp. 423--444.
   
• Katz, R.W., Craigmile, P.F., Guttorp, P., Haran, M., Sanso, B. and Stein, M.L. (2013) Uncertainty Analysis in Climate Change Assessments, Nature Climate Change, 3, pp. 769--771.
   
• Hughes, J. and Haran, M. (2013), Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models, Journal of the Royal Statistical Society, Series B, 75, 1, 139--159.Software for this approach may be found here: ngspatial
   
• Tingley, M., Craigmile, P.F., Haran, M., Li, B., Mannshardt-Shamseldin, E. and Rajaratnam, B. (2012), Piecing together the past: Statistical insights into paleoclimatic reconstructions, Quaternary Science Reviews, 35, 1--22.
   
• Haran, M. (2011) Gaussian random field models for spatial data, in Handbook of Markov chain Monte Carlo, Editors, Brooks, S.P., Gelman, A.E. Jones, G.L. and Meng, X.L., Springer-Verlag. bibtex
   
• Flegal, J.M., Haran, M., and Jones, G.L. (2008) Markov chain Monte Carlo: Can we trust the third significant figure? Statistical Science, 23,250--260. bibtex
   
• Code for consistent batch means estimator for MCMC standard errors as described in the paper: R package on CRAN, R function and C function.

Teaching

• STAT 200 - Elementary Statistics
• STAT 380 - Data Science through Statistical Reasoning and Computation
• STAT 414 - Introduction to Probability Theory
• STAT 440 - Computational Statistics
• STAT 463 - Time Series
• STAT 515 - Stochastic Processes and Monte Carlo Methods
• STAT 540 - Statistical Computing
• STAT 597A - Spatial Models