Bio (Murali Haran)
Murali Haran Bio
Murali Haran is Professor and Head of the Department of Statistics at Penn State University. He has a PhD in Statistics from the University of Minnesota, and a BS in Computer Science (with minors in Statistics, Mathematics and Film Studies) from Carnegie Mellon University. His research interests are in Monte Carlo algorithms, spatial models, statistical analysis of complex computer models, and interdisciplinary research in climate science and infectious disease modeling. He is a Fellow of the American Statistical Association and received the 2015 Young Researcher Award from The International Environmetrics Society to ``recognize and honor outstanding contributions to the field of environmetrics''.
I was jointly advised by Luke Tierney* and Brad Carlin** on my
dissertation "Efficient Perfect and MCMC Sampling Methods for Bayesian
Spatial and Components of Variance Models" (see my math genealogy
). I worked on computation for Bayesian spatial models and some
hierarchical models. In particular, I investigated sampling techniques
for hierarchical models that use Gaussian Markov Random Fields
(GMRFs). A major portion of my thesis contains work on perfect
sampling approaches for Bayesian disease mapping models. For more on
perfect sampling, see D.B.Wilson's website . While in graduate school,
I also studied hierarchical Bayesian modeling techniques used for
disease mapping and particulate matter exposure studies. As a
postdoctoral fellow I studied statistical approaches for software
Computer Science (with minors in statistics, mathematics and film), Carnegie Mellon University, 1997.
- M.S., Ph.D. Statistics, University of Minnesota, 2003.
- Postdoctoral fellow, NISS (courtesy appointment at
Duke University) (2003--2004).
- New Research Fellow, SAMSI (2009--2010).
- Visiting Associate Professor (sabbatical, 2011--2012) Department of Statistics, University of Washington.
- Xiaoxiao Li (pre-comp): Model Based Clustering and Point Processes for Infectious Diseases
- Claire Kelling (pre-comp): Point Process Models for Criminology
- John Mattiace (pre-comp): Statistical Methods for Vaccine Hesitancy
- Bokgyeong Kang (pre-comp): Methods for computing with approximate likelihood functions
- Samantha Roth (pre-comp; joint with K. Keller): Statistical methods for climate projections
- (Postdoc) Zhou Lan (2019 --), joint with Le Bao
- Virginia Recta (2009; joint with J.L. Rosenberger): A model-based analysis of semi-continuous spatial data. Position after graduation: Mathematical Statistician, Food and Drug Administration, VA.
- K. Sham Bhat (2010): Inference for complex computer models and large multivariate spatial data with applications to climate science. Position after graduation: Mathematical Scientist, Los Alamos National Laboratories, Los Alamos, NM.
- Matthew Tibbits (2011; joint with J.C. Liechty): Parallel Markov chain Monte Carlo. Position after graduation: Mathematical Statistician, Washington DC
- John Hughes (2011; joint with J. Fricks): Motor Proteins and Non-Gaussian Areal Data: Advances in Stochastic Modeling and Computation. Position after graduation: Assistant Professor, Department of Biostatistics, University of Minnesota.
- Roman Jandarov (2012): Inference with Implicit Likelihoods for Infectious Disease Models. Position after graduation: Postdoctoral Fellow, Department of Biostatistics, University of Washington--Seattle. Now: Assistant Professor, Department of Environmental Health, University of Cincinnati
- Won Chang (2014; joint with K. Keller, Department of Geosciences, Penn State): Climate Model Calibration using High-dimensional and Non-Gaussian Spatial Data. Position after graduation: Postdoctoral Fellow, Department of Statistics, University of Chicago. Now: Assistant Professor, Department of Mathematical Sciences, University of Cincinnati
- Josh Goldstein (2015): Compartmental, Spatial and Point Process Models for Infectious Diseases. Position after graduation: Social and Decision Analytics Laboratory, Virginia Tech.
- James Russell (2016; joint with Ephraim Hanks): Space-time Models for Animal Movement Data Position after graduation: Assistant Professor, Dept of Mathematics and Computer Science, Muhlenberg College, Allentown, PA.
- Yawen Guan (2017): Reduced-dimensional Non-Gaussian Spatial Models and Statistical Methods for Studying the West Antarctic Ice Sheet Position after graduation: Postdoctoral fellow, SAMSI (Statistics and Applied Mathematical Sciences Institute)/North Carolina State University. Now: Assistant Professor, Department of Statistics, University of Nebraska.
- Jaewoo Park (2019): Computational Methods for Intractable Likelihoods Position after graduation: Assistant Professor, Department of Applied Statistics, Yonsei University, Seoul, South Korea.
- Ben Seiyon Lee (2020): Computational Methods for Hierarchical Spatial Models and Ice Sheet Model Calibration Position after graduation: Assistant Professor, Department of Statistics, George Mason University.
- Muhammad Atiyat (2008). Ph.D., Department of Statistics, Penn State University. Now: United Nations.
- Chris Groendyke (2008). Ph.D., Department of Statistics, Penn State University. Now: Robert Morris College.
- Matthew Tibbits (2009). Ph.D., Department of Statistics, Penn State University. Now: Mathematical Statistician, Washington DC
- Meng Chen (2019) (ongoing)
- Elected Member of the International Statistical Institute 2017
- 2016 Fellow of the American Statistical Association
- 2015 Abdel El-Shaarawi Young Researcher (AEYR) Award given by The International Environmetrics Society 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) for contributions to statistical methods for environmental science.
- New Researcher Fellow, Statistical and Applied Mathematical Sciences Institute (SAMSI), Fall 2009.
- Outstanding Poster Award, ``Case Studies in Bayesian Statistics'' conference. Carnegie Mellon University, Pittsburgh. 2007.
- Student Service Award, Statistics, University of Minnesota. 2003.
- School of Computer Science Honors, Carnegie Mellon University. May 1997.
- Inducted into Lambda Sigma National Honor Society. 1994.
- Markov chain Monte Carlo (MCMC) algorithms
MCMC algorithms are very widely used for both Bayesian and frequentist
statistical inference. I am interested in designing
algorithms that have good properties so they produce accurate
estimates quickly. I am particularly interested in algorithms that are
automated, that is, where the user needs to provide minimal input.
- Exact/perfect sampling: This is a sophisticated class of
algorithms that use Markov chains to produce i.i.d. draws from the
target distribution. It has generally been difficult to get this to
work for non-toy Bayesian models. I have worked on implementing these
for a class of Gaussian Markov random field models.
- Stopping rules and standard errors for MCMC:
My collaborators and I have shown that using a consistent
batch means estimate of standard errors and then using the
standard errors as a criteria for stopping an MCMC run works well
in theory and practice.
- Fast mixing algorithms: I have worked on designing MCMC
algorithms that explore the state space quickly, demonstrating their
effectiveness via empirical studies and theoretical results wherever
- Parallel computing: With Matt Tibbits (Ph.D. student)
and John Liechty, I have recently been working on constructing MCMC algorithms that exploit the
latest in parallel computing resources.
- Spatial models
- Spatial models for non-Gaussian data: I am working on modeling and computation for `automodels' and spatial generalized linear mixed models for spatially dependent data that are non-Gaussian, for e.g. spatial binary (0-1) data, count data, and zero-inflated data.
- Complex computer models: emulation, calibration, model averaging
- Computer experiments: complex computer models increasingly have output that is in the form of spatial fields. There are computational and modeling challenges involved in parameter inference/calibration based on such computer model output and physical observations of the process being modeled by the computer model. I have been working on flexible and computationally efficient approaches for these problems.
- Often multiple computer models are available for the same phenomenon. Bayesian model averaging (BMA) is an approach that allows for information from multiple models to be combined in a probabilistic fashion. I have worked on BMA approaches for computer models with spatial output.
- Interdisciplinary work in environmental and ecological
My methodological work in this area is motivated
by the challenge of carrying out statistical inference when the
scientific models, questions and data sets are complex. Statistical
computing and spatial modeling often play crucial roles in these
- Statistics in Software Engineering
I have worked with computer
scientists on statistical issues in software engineering. In
particular, we have explored the use of a classification technique
called random forests to identify failing executions in large computer
* A member of the R core group and author of Xlisp-stat .
** Author of the books Bayes and Empirical Bayes Methods for Data Analysis and Hierarchical Modeling and Analysis for Spatial Data .
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