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Murali Haran

Professor of Statistics
Murali Haran


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).





  • 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