Edoardo M. Airoldi, Associate Professor of Statistics. BS, Mathematical Statistics, Bocconi University; MS, Statistics and Machine Learning, Carnegie Mellon University; PhD, Computer Science, Carnegie Mellon University. Design and analysis of experiments in the presence of interference. Inference from non-ignorable network sampling designs. Geometry of inference in ill-posed inverse problems. Theory and methods for the analysis of network data. Modeling and inference in high-throughput biology. Applications to computational social science and marketing. Statistical computing strategies for massive data sets. (Link to up-to-date information.)
Joseph Blitzstein, Professor of the Practice in Statistics. BS in Mathematics, California Institute of Technology; MS in Statistics, Stanford University; PhD in Mathematics, Stanford University. Statistical inference and models for networks and graphs in the social and health sciences, Monte Carlo algorithms, probability and combinatorics.
Stephen Blyth, Professor of the Practice in Statistics (Part-Time); Managing Director (Harvard Management Company). BA and MA in Mathematics, Cambridge University; AM and PhD in Statistics, Harvard University. Applied quantitative finance. Applications to financial derivative markets. Use of judgment in quantitative financial modelling.
Mark E. Glickman, Senior Lecturer on Statistics. B.A. in Statistics, Princeton University; M.A. & Ph.D. in Statistics, Harvard University. Research interests include sports analytics, paired comparison models, rating and ranking, tournament design, Bayesian statistics, methods applied to health services research.
David Harrington, Professor of Statistics; Acting Department Chair; Professor of Biostatistics, Harvard School of Public Health/Dana-Farber Cancer Institute. B.A. in Mathematics, Tufts University; M.A. and Ph.D. in Mathematics, University of Maryland. Statistical methods for clinical trials and prospective cohort studies in which the time to an event is a primary outcome; prediction with time-to-event data; observational studies in health services research, including methods for protecting confidentiality in public use data sets.
Pierre E. Jacob, Assistant Professor of Statistics. M.Sc. and Ph.D. in Statistics, Université Paris-Dauphine. Sequential and Markov Chain Monte Carlo methods for Bayesian Statistics. Methodological side of the field: finding new algorithms to tackle computational problems arising in Bayesian Statistics, for instance in high-dimensional, non-linear dynamical systems, or multi-modal static problems. Contribute to the theory supporting the methods, and to more computational aspects such as parallel computing.
S.C. Samuel Kou, Professor of Statistics. Computational Mathematics, Peking University; MS and PhD, Statistics, Stanford University. Stochastic modeling in natural sciences (such as nano-biophysics, chemistry and biology) and in economics and finance; inference about stochastic models (processes); statistical analysis of single-molecule experiments; non-parametric methods; model selection; Bayesian and empirical Bayesian methodology; Monte Carlo methods.
Xihong Lin, Professor of Statistics and of Biostatistics. B.S. in Applied Mathematics, Tsinghua University; M.S. in Statistics, University of Iowa; Ph.D. in Biostatistics, University of Washington. Statistical genetics and genomics. Development and application of statistical and computational methods for analysis of high-throughput genetic, genomic and ‘omics data in epidemiological, environmental and clinical sciences, especially genetic and epigenetic epidemiology, genes and environment, and medical genomics. Current research projects include genome-wide array association studies, whole genome sequencing association studies, gene-environment interactions, and genome-wide DNA methylation studies, pathway and network analysis, and integrative genetics and genomics.
Jun S. Liu, Professor of Statistics. BS, Mathematics, Beijing University; PhD, University of Chicago. Bayesian methodology: modeling, testing, and nonparametrics. Monte Carlo methodology: Gibbs sampling and MCMC methods; MC methods in physics, material science, chemistry, and structural biology; rate of convergence. Dynamic systems: nonlinear state-space models; target tracking; digital signal processing; financial data modeling. Bioinformatics and computational biology: gene regulation; sequence alignment; protein structure prediction; gene clustering and classification; genetics. Statistical missing data problems. (Link to Bioinformatics Lab description.)
Xiaole Shirley Liu, Professor of Statistics and of Biostatistics & Computational Biology. BA in Biochemistry & Computer Science, Smith College; PhD in Biomedical Informatics from Stanford University. Research focuses on computational models of transcription and epigenetic regulation by integrating data from genome-wide ChIP-chip / ChIP-Seq, nucleosome occupancy and histone modifications, gene expression microarray / RNA-sequencing, genomic sequence and conservation.
Xiao-Li Meng, Dean of the Graduate School of Arts and Sciences; Whipple V.N. Jones Professor of Statistics. BS, Mathematics, Fudan University; AM and PhD, Statistics, Harvard University. Statistical inference under complex settings, such as partially observed data, pre-processed data, and simulated data. Quantifying statistical information and efficiency in scientific studies. Statistical principles and foundational issues, especially regarding model uncongeniality, self-efficiency, and quantifying ignorance. Bayesian wavelet and multi-resolution methods, especially with missing data. Bayesian ranking and mapping. Stochastic and deterministic iterative algorithms, especially perfect sampling. Applications of the above research to astrophysics, genetic and environmental studies, demosaicing and image reconstruction, mental health surveys, and survival analysis.
Michael Parzen, Senior Lecturer on Statistics. BS and MS, Mathematics, Carnegie Mellon University; MS, Applied Mathematics, Brown University; DSc, Biostatistics, Harvard School of Public Health. Statistical methods for Missing Data, Non-Standard Regression, Computational Statistics and Statistical Education. Also developing and applying statistical models to data from business and biostatistical problems.
Natesh S. Pillai, Associate Professor of Statistics. BTech, Indian Institute of Technology; MS and PhD, Statistics, Duke University. Statistical Inference for stochastic processes and dynamical systems. Markov Chain Monte Carlo methods and their applications to Bayesian statistics. Bayesian inverse problems for dynamical systems. Ergodic properties of diffusions, stochastic differential equations and stochastic partial differential equations. Nonparametrics, machine learning.
Donald B. Rubin, John L. Loeb Professor of Statistics. AB, Psychology, Princeton University; MS, Computer Science, Harvard University; PhD, Statistics, Harvard University. Causal inference in experiments and observational studies, including complex situations with noncompliance and dropout; computation and inference in sample surveys with nonresponse and in missing data problems, including EM-type algorithms; application of Bayesian and empirical Bayesian techniques; and developing and applying statistical models to data in a variety of scientific and policy relevant disciplines.
Neil Shephard, Professor of Economics and of Statistics. BA, Economics and Statistics, York University (England); MSc and Ph.D., London School of Economics. Theoretical and applied econometrics. Problems inspired by financial issues. Statistical inference for stochastic processes. Simulation based strategies developed for Bayesian inference. On-line learning. Prediction. Data augmentation.
Herman Chernoff, Professor of Statistics. BS, Mathematics, City College of New York; MSc, and PhD, Applied Mathematics, Brown University. Sequential analysis, optimal design of experiments and pattern recognition, statistics applied to molecular biology.
Arthur P. Dempster, Research Professor of Theoretical Statistics. BA, Mathematics and Physics, University of Toronto; MA, Mathematics, University of Toronto; PhD, Mathematical Statistics, Princeton University. Statistical science as probabilistic reasoning from data and model assumptions with reference to unique inferential situations, primarily through methods and analyses based on the Dempster-Shafer calculus and its specializations to Fisherian and Bayesian inference. Areas of applied interest include biometric identification, machine learning applied to pattern and network identification in genomics, and physical and statistical modeling and analysis related to climate change and similar environmental issues.
Carl N. Morris, Professor of Statistics, Professor of Health Care Policy. BS, Engineering, California Institute of Technology; MS and PhD, Statistics, Stanford University. Hierarchical modeling, Bayesian and likelihood theory, exponential families, and statistical applications, with special emphasis on health and health policy research, and on sports and competition.
Luke Bornn, Visiting Scholar. B.Sc. in Mathematics, University of the Fraser Valley; M.Sc. and Ph.D. in Statistics, University of British Columbia. Bayesian modelling of large-scale dynamic systems, with applications to environmetrics, structural health monitoring, and the analysis of sports. Theoretical and methodological development of spatial process models. Monte Carlo methods.
D. James Greiner, Professor of Law, Harvard Law School. B.A. in Government and Foreign Affairs, University of Virginia; J.D., University of Michigan; Ph.D. in Statistics, Harvard University. Randomized control trials. Complex field operations. Quantitative legal empirics. Causal inference. Ecological inference. Redistricting. Access to justice. Court administration. Employment discrimination.
Rafael Irizarry, Professor of Biostatistics, Harvard T.H. Chan School of Public Health. BS in Mathematics, University of Puerto Rico; Ph.D. in Statistics, University of California, Berkeley. Research focuses on Genomics and Computational Biology problems. Analysis and signal processing of microarray, next-generation sequencing, and genomic data. Developing diagnostic tools and discovering biomarkers. Implementing open source software (Bioconductor Project) for analysis of genomic data.
Luke Miratrix, Assistant Professor of Education, Harvard Graduate School of Education. B.A in Mathematics, Reed College; B.S in Computer Science, California Institute of Technology; M.S in Computer Science, Massachusetts Institute of Technology; Ph.D in Statistics, University of California, Berkeley. Methods for various applications of statistics in the social sciences. Methods for assessing and modeling heterogeneous treatment effects in randomized experiments and observational studies. Analysis of high dimensional data, in particular text corpora. Randomization, permutation, and bootstrap methods.
Alan M. Zaslavsky, Professor of Health Care Policy (Statistics), Harvard Medical School. AB, Government, Harvard University; MS, Mathematics, Northeastern University; PhD, Applied Mathematics-Statistics, Massachusetts Institute of Technology. Hierarchical Bayes models, design and analysis of surveys, discrete data, small area estimation, government statistics, with applications to health care policy, health services research, and psychiatric epidemiology.