Some of the scientific questions I have worked on in environmental science include:

Climate Science

- The Atlantic Meridional Overturning Circulation (AMOC) is part of the global ocean conveyor belt circulation and transfers heat between low and high latitudes in the Atlantic basin. The AMOC might collapse in a "tipping point" response to anthropogenic (human-induced) changes to the climate; the collapse of the AMOC can have major impacts on global climate, particularly in Northern Europe. An important question is: What is the risk of a collapse of a future collapse of the AMOC?
- What is our prediction for the future behavior of the Greenland ice sheet? The melting rate of this ice sheet can have a profound impact on future sea level rise. Answering this questions requires combining output from ice sheet models with observational data regarding the past and present state of the ice sheet.
- What can we learn from data and computer models about climate sensitivity, the (equilibrium) global mean surface temperature change following a doubling of atmospheric CO2 concentration? What are some of the leading uncertainties in our estimates?
- What are careful ways to reconstruct past climate (paleoclimate) based on tree rings, ice cores and other indirect sources of information?
- How can our reconstruction of past climate inform us about state-of-the-art climate models?

- How is the progression of a virus affected by the properties of individual cells? How do those infected cells interact?
- What are the impacts of the environment/weather on the transmission of meningitis?
- How does the flowering season impact the progression of the wheat crop disease Fusarium Head Blight?
- What is the direction and rate at which gypsy moth infestations have occurred in the U.S.?
- What are the spatio-temporal dynamics of diseases like measles and rotavirus and how do these dynamics impact policy?

- Gaussian process models; process convolutions/kernel mixing for large spatial data
- Markov chain Monte Carlo (MCMC), Approximate Bayesian computing (ABC)
- Emulation and calibration for complex computer models with spatial output
- Compartmental models (e.g. SIR models)
- Gaussian Markov random fields; spatial generalized linear mixed models
- Composite likelihood
- Hierarchical Bayesian Inference
- Spatial interaction point processes

- Chang, W., Haran, M., Applegate, P., and Pollard, D. (2015) Calibrating an ice sheet model using high-dimensional binary spatial data , accepted for publication in
*Journal of the American Statistical Association*

- Tingley, M., Craigmile, P.F., Haran, M., Li, B., Mannshardt-Shamseldin, E. and Rajaratnam, B. (2015), "On discriminating between GCM forcing configurations using Bayesian reconstructions of Late-Holocene temperatures",
*to appear in J. of Climate*

- Chang, W., Applegate, P., Haran, M. and Keller,
K. (2014) Probabilistic calibration
of a Greenland Ice Sheet model using spatially-resolved synthetic
observations: toward projections of ice mass loss with
uncertainties> ,
*Geoscientific Model Development, 7 (2), 1933–1943, DOI:10.5194/gmd-7-1933-2014, also: Geoscientific Model Development Discussion, 7, 1905-1931.*

- Chang, W., Haran, M., Olson, R., and Keller, K. (2014) Fast dimension-reduced climate model calibration and the effect of data aggregation,
*Annals of Applied Statistics,*8, 2, pp 649--673. (**Winner**of the 2014 American Statistical Association Section on Statistics and the Environment Student Paper Competition)

- Chang, W., Haran, M., Olson, R., and Keller, K. (2014) A composite likelihood approach to computer model calibration with high-dimensional spatial data,
*Statistica Sinica*, 25 (2015), 243-259.

- Chang, W., Applegate, P., Haran, M. and Keller, K. (2014) Probabilistic calibration of a Greenland Ice Sheet model using spatially-resolved synthetic observations: toward projections of ice mass loss with uncertainties> ,
*online discussion paper in Geoscientific Model Development, the scientific discussion forum of GMD*

- 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, 769–771*.

- Olson, R., Sriver, R., Chang, W., Haran, M., Urban, N.M., Keller, K. (2013) Uncertainty in climate sensitivity estimates due to random realizations of unresolved climate noise,
*to appear in Journal of Geophysical Research - Atmospheres.*

- Bhat, K.S., Haran, M., Olson, R., and Keller, K. (2012) Inferring likelihoods and climate system characteristics from climate models and multiple tracers ,
*Environmetrics, 23, 24, 345--362.*

- Olson, R., Sriver, R., Goes, M., Urban, N.M., Matthews, H.D., Haran, M., and Keller, K. (2012) A climate sensitivity estimate using Bayesian fusion of instrumental observations and an Earth System model,
*Journal of Geophysical Research - Atmospheres, 117, D4, 1984--2012.*

- 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 ,
*Quarternary Science Reviews, 35, 1--22.*

- Bhat, K.S., Haran, M., Terando, A.J. and Keller, K. (2011) Climate Projections Using Bayesian Model Averaging and Space-Time Dependence.
*Journal of Agricultural, Biological and Environmental Statistics, 16, 4, pp. 606--628.*

- Haran, M. and Urban, N. (2010), Discussion of McShane and Wyner ``A
statistical analysis of multiple temperature proxies: are
reconstructions of surface temperatures over the last 1000 years
reliable?
*Annals of Applied Statistics.*See all the discussions and the rejoinder here

- Goes, M., Urban, N., Olson, R., Haran, M., and Keller, K. (2010)
The skill of different ocean tracers in reducing uncertainties about projections of the Atlantic Meridional Overturning Circulation ,
*Journal of Geophysical Research--Oceans, 115, C12006, 12 pp. {\tt doi:10.1029/2010JC006407}*bibtex

- K.S. Bhat (2010)
*Inference for complex computer models and large multivariate spatial data with applications to climate science*(now: Scientist at Los Alamos National Labs) - Won Chang (2014)
*Climate Model Calibration using High-dimensional and Non-Gaussian Spatial Data*(now: Postdoctoral Scholar, University of Chicago) - Yawen Guan (current)
- Saksham Chandra (current)
- Ben Seiyon Lee (current)

- Kira White (expected summer 2014)
- Evan Bittner (expected summer 2016)

- Russell, J., Hanks, E.M., and Haran, M. (2015), Dynamic Models of Animal Movement with Spatial Point Process Interactions,
*to appear in the Journal of Agricultural, Biological and Environmental Statistics*

- Goldstein, J., Haran, M., Bjornstad, Liebhold, A. (2015) "Quantifying SpatioTemporal Variation of Invasion Spread,"

- 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.*

- Recta, V., Haran, M., and Rosenberger, J.L. (2012)
A two-stage model for incidence and prevalence in point-level spatial count data ,
*Environmetrics, 23, 2, 162--174.*bibtex

- Jandarov, R., Haran, M., and Ferrari, M. (2012) A Compartmental Model for Meningitis: Separating Transmission from Climate Effects on Disease Incidence,
*Journal of Agricultural, Biological and Environmental Statistics, 2012, 17, 3, 395--416.*

- Haran, M., Bhat, K.S., Molineros, J, and De Wolf, E. (2009)
Estimating the risk of a crop epidemic from coincident spatiotemporal processes ,
*Journal of Agricultural, Biological and Environmental Statistics, 1085-7117.*bibtex

- Yang, T., Teng, H., and Haran, M. (2009) The impacts of social capital on infant mortalities in the U.S.: a spatial investigation
*Applied Spatial Analysis and Policy, 2, 3, 211--227.*

- Yang, T., Jensen, L., and Haran, M. (2009) Social Capital and
Human Mortality: Explaining the Rural Paradox with County-Level Mortality
Data, accepted for publication in
*Rural Sociology.*

- Yang, T., Jensen, L., and Haran, M. (2008) Context and death: A spatial investigation of the impacts of social capital and natural amenities on mortality in the U.S. counties, refereed discussion paper,
*Proceedings of the Population Association of America.*

- Haran, M.,
Carlin, B.P., Adgate, J.L., Ramachandran, G., Waller, L.A.,
and Gelfand, A.E.,
Hierarchical
Bayes models for relating particulate matter exposure measures,,
Case Studies in Bayesian Statistics, Volume VI, eds. C.Gatsonis, et
al., New York: Springer-Verlag, 2002. bibtex

- Virginia Recta (2009) (joint with J.L.Rosenberger)
*A model-based analysis of semi-continuous spatial data*(now: Mathematical Statistician at the Food and Drug Administration) - John Hughes (2011) (joint with J.Fricks)
*Motor Proteins and Non-Gaussian Areal Data: Advances in Stochastic Modeling and Computation*(now Assistant Professor at University of Minnesota Biostatistics) - Matthew Tibbits (2011) (joint with J.C.Liechty)
*Parallel Markov chain Monte Carlo*, used parallel MCMC to fit dynamic epidemic models and spatial models (now: Mathematical Statistician in Washington DC) - Roman Jandarov (2012)
*Inference with Implicit Likelihoods for Infectious Disease Models*(first job: Postdoctoral Fellow at University of Washington Biostatistics, now: Assistant Professor, Department of Environmental Health, Division of Biostatistics and Bioinformatics, University of Cincinnati) - Josh Goldstein (2015)
*Compartmental, Spatial and Point Process Models for Infectious Diseases* - James Russell (joint with E. Hanks); anticipated graduation in Summer 2016)
*Space-time Models for Animal Movement Data*

- National Science Foundation (NSF-CDS&E) "Statistical Methods for Ice Sheet Projections using Large Non-Gaussian Space-time Data Sets and Complex Computer Models", 8/1/2014--7/31/2017. $500,500.
- National Science Foundation (NSF-SRN) "Sustainable Climate Risk Management (SCRIM)", 9/30/2012--9/30/2017.
- National Science Foundation (NSF-SES) ``Informing climate-related decisions with Earth system models'', 3/01/2011--2/28/2014
- The Bill and Melinda Gates Foundation ``Evaluate candidate vaccine technologies using computational models'', 1/1/2008--12/31/2011
- National Science Foundation (NSF-HSD) ``What is a better prediction system?'', 9/1/2007--8/31/2010
- National Oceanic and Atmospheric Administration (NOAA) ``Predicting impacts of stressors at the land-water interface,'' 9/1/2009--8/31/2014.
- U.S. Geological Survey (USGS) "Developing regionally downscaled probabilistic climate change projections", 7/1/2009--12/1/2010.