Download Curriculum Vitae - Duke Statistical

CURRICULUM VITAE
Rebecca Carter Steorts
Department of Statistical Science
Duke University
Old Chemistry Hall
Durham, NC 27708
Cell: (704) 453–1597
Phone: (919) 681-8843
Fax: (919) 684-8594
Email: [email protected]
Education
Ph.D. in Statistics, May 2012, University of Florida. Advisor: Malay Ghosh.
Thesis title: “Bayes and and empirical Bayes benchmarking for small area estimation.”
Honorable Mention (Second Place), Leonard J. Savage Award for best thesis in application
methodology from the International Society for Bayesian Analysis.
M.S. in Mathematical Sciences, May 2007, Clemson University.
B.S. in Mathematics, May 2005, Davidson College.
Positions
Duke University: Assistant Professor of Statistical Science, affiliated faculty with the Social Science
Research Institute (SSRI) and the Information Initiative at Duke (iid), since June 2015 – present.
Duke University, Visiting Scholar, Department of Statistical Science, May 2015.
Carnegie Mellon University: Visiting Assistant Professor of Statistics, June 2012 – May 2015.
Research Interests
Bayesian methodology, entity resolution (record linkage), machine learning, locality sensitive hashing, big data, privacy-preserving record linkage, scalable clustering methods, decision theory, survey methodology, small area estimation, Markov chain Monte Carlo methods, variational Bayesian
methods, Bayesian nonparametric methods, human rights violations.
Honors and Awards
• MIT Review Magazine’s 35 Innovators Under 35, Humanitarian for Software Design in estimating death counts for the Syrian civil war. Feature piece in MIT Review,
September/October, 2015. http://www.technologyreview.com/emtech/15/video/watch/
innovators-under-35-rebecca-steorts/
• Honorable Mention (Second Place), Leonard J. Savage Award for best thesis in application
methodology from the International Society for Bayesian Analysis, Spring 2013.
• Data Analysis Consultant, Human Rights Data Analysis Group, San Francisco, CA, 2015.
• Data Science Consultant, Food and Agricultural Organization (FAO) of the United Nations,
2014.
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• Visiting Scholar at the University of Trier, Department of Economics and Social Sciences,
August 2014.
• Visiting Scholar at the University of Rome “La Sapienza,” Department of Methods and Models
for Economics, Geography and Finance, May 2014, June 2015.
• Visiting Scientist in Summer at Census Program, U.S. Census Bureau, Washington D.C.,
June 3–7, 2013.
• United States Census Bureau Dissertation Fellowship Program, August 2010–May 2012.
• UF Innovation through Institutional Integration (I-Cubed) Program (funded by
NSF); Teaching Award for development of new course to department curriculum, Spring
2011.
• University of Florida Graduate Alumni Fellowship, University of Florida, August 2007–2010.
• Mathematical Sciences Graduate Student Teaching Award, Clemson University, May 2007.
Other Awards
NSF Travel Grant, New Directions in Monte Carlo Methods Workshop, University of Florida,
Gainesville, FL, January 2013; Association for Women in Mathematics/National Science Foundation Travel Grant, WiML and NIPS, December 2012; NSF Travel Grant for Workshop on Measuring
People in Place, Boulder, CO, October 2012; ASA Survey Research Methods, Government Statistics, and Social Statistics Travel Award for the Joint Statistical Meetings, San Diego, CA, August
2012; ISBA Young Traveler Support Award, Kyoto, Japan, June 2012; ASA Survey Research Methods Section (SRMS) Travel Award for the Joint Statistical Meetings, Vancouver, BC, August 2010;
Division of Sponsored Research Matching Travel Award for the Joint Statistical Meetings, Vancouver, BC, August 2010. MCM (Mathematical Contest in Modeling) Meritorious Award for paper:
DualQueue: Model for Optimal Quantity of Tollbooths, May 2005.
Current Grant Support
As co-PI: Record Linkage and Privacy-Preserving Methods for Big Data, National Science Foundation, SES – 1534412, PIs: Slavkovic (PSU) and Steorts (Duke), Total direct costs to Steorts:
$265,579 over 07/28,15–08/31/18.
As PI: Computationally Scalable Statistical Methods for High Dimensional Record Linkage, The
John Templeton Foundation (http://www.knowledgelab.org/news/detail/1.4_million_in_grants_
awarded_to_metaknowledge_projects). Total direct costs to Steorts: $110,00 over 1/1/15–
11/1/17. PI: Steorts.
As PI: Synthetic Data Release: the Tradeo↵ between Privacy and Utility of Big Data, NCSU LAS
/ NSA Prime. PI: Steorts.
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As co-PI: Incorporating dynamic Electronic Health Records data into a model for patient deterioration, 2016 Collaborative Quantitative Approaches to Problems in the Basic and Clinical Sciences
seed funding program, Duke University. PIs: Steorts, Goldstein, O’Brien (Duke University). Total
direct costs to Steorts: $40,000.
Past Grant Support
As collaborator (38.3% e↵ort plus graduate student support): NIH MIDAS Informatics Services
Group (ISG), 8/1/14–06/01/2015. PIs: Wagner, Espino (University of Pittsburgh), Brown (CMU).
As collaborator (8.33% e↵ort plus graduate student support): NSF SES1130706, “Census Research
Node: Data Integration, Online Data Collection, and Privacy Protection for Census 2020” (Total
direct costs: $256,857 over 10/1/11–06/01/2015). PIs: Fienberg/Eddy.
Postdoctoral Visiting Assistant Professor (49.98% e↵ort): NSF DMS1043903, “Statistics and Machine Learning for Scientific Inference” (Total direct costs: $433,261 over 7/15/11–06/01/2015).
PI: Kass.
Invited Publications
• Steorts, R. and Ugarte, D.M. (2014). Discussion of “Single and Two-Stage Cross-Sectional
and Time Series Benchmarking Procedures for SAE,” TEST, 23 :680–685, arxiv:1405.6416.
• Fienberg, S. and Steorts, R. (2014). Discussion of “Estimating the Distribution of Dietary
Consumption Patterns,” Statistical Science, 29 1:95–96, arxiv:1403.0566.
In Preparation and Refereed Publications
• Miller, J., Betancourt, B., Zaidi, A, Wallach, H. and Steorts, R. (2016). The Microclustering
Problem: When the Cluster Sizes Don’t Grow with the Number of Data Points, To be
Submitted.
• Betancourt, B., Durante, D. Steorts, R. (2016). Random Shades of Colors: Multilayer
Clustering and Community Detection in Networks, To be Submitted.
• Karwa. V., Bai, L. Steorts, R. (2016). Di↵erentially Private and Hashed Synthetic Data
Release, To be Submitted.
• Sadosky, P., Shrivastava, A., Price, M. and Steorts, R. (2015). Blocking Methods Applied
to Casualty Records from the Syrian Conflict, (Submitted), arxiv:1510.07714.
• Steorts, R., Hall, R., and Fienberg, S. (2015). A Bayesian Approach to Graphical Record
Linkage and De-duplication, Journal of the American Statistical Association: Theory and
Methods (In Press), arxiv:1312.4645.
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• Wehbe, L., Ramdas, A., Steorts, R. and Shalizi, C.R (2015). Regularized Brain Reading
with Smoothing and Shrinkage Using Bayesian and Frequentist Methods, Annals of Applied
Statistics (In Press), arxiv:1401.6595.
• Steorts, R. (2015). Entity Resolution using Empirically Motivated Priors, Bayesian Analysis, 10(4) 849–875, arxiv:1409.0643, Finalist for Lindley Prize.
• Steorts, R., Ventura, S., Sadinle, M. and Fienberg, S. (2014). Blocking Comparisons for
Record Linkage, Privacy in Statistical Databases (Lecture Notes in Computer Science 8744),
ed. J. Domingo-Ferrer, Springer, 253-268; arxiv:1407.3191.
• Steorts, R., Hall, R. and Fienberg, S. (2014). SMERED: A Bayesian Approach to Graphical
Record Linkage and De-duplication, JMLR W&CP, 33 922–930, arxiv:1403.0211.
• Ghosh, M. and Steorts, R. (2013). Two-stage Bayesian benchmarking as applied to small
area estimation, TEST, 22(4) 670–687, arxiv:1305.6657
• Steorts, R. and Ghosh, M. (2013). On estimation of mean squared errors of benchmarked
empirical Bayes estimators, Statistica Sinica, 23(2) 749–767, arxiv:1304.1600
• Datta, G., Ghosh, M., Steorts, R. and Maples, J. (2011). Bayesian benchmarking with
applications to small area estimation, TEST, 20(3) 574–588.
Refereed Workshop Papers
• Miller, J., Betancourt, B., Zaidi, A, Wallach, H. and Steorts, R. (2015). The Microclustering Problem: When the Cluster Sizes Don’t Grow with the Number of Data Points, NIPS
Bayesian Nonparametrics: The Next Generation Workshop Series, arxiv:1512.00792, Top
Five Best Workshop Papers.
• Broderick, T. and Steorts, R. (2014). Variational Bayes for Merging Noisy Databases, NIPS
Workshops in Advances in Variational Inference, NIPS 2014, arxiv:1410.4792.
• Hall, R., Steorts, R. and Fienberg, S. (2012). Bayesian parametric and nonparametric
inference for high dimensional multiple record linkage, NIPS Modern nonparametric methods
in machine learning workshop paper, NIPS 2012.
Unpublished Manuscipts (Books)
• Steorts, R. (2013). Bayesian Theory and Applications: The Essential Parts with R . A
graduate manuscript for Bayesian theory and applications.
• Steorts, R. (2011). Some of Bayesian Statistics using R. An undergraduate manuscript on
Bayesian statistics with minimal calculus.
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Software and Products
• (Updated 2015) Microclutering software R, C++ software. Developed and coded by Brenda
Betancourt, Je↵rey W. Miller, Abbas Zaidi, and Rebecca C. Steorts.
• (Updated 2015) Empirical Bayes Record Linkage and De-duplication R software. Developed
and coded by Rebecca C. Steorts.
• (Updated 2015) Faculty member of Models of Infectious Disease Agent Study (MIDAS) Group
(joint with Pittsburgh Bioinformatics). MIDAS scientists are creating synthetic ecosystems
that endeavor to contain multiple populations useful for agent based models. Developed and
coded by Shannon Gallagher, Jerzy Wieczorek, Lee Richardson, Rebecca C. Steorts, and
William F. Eddy. http://www.epimodels.org/drupal/?q=node/32.
• (Updated 2014) Record Linkage and De-duplication Java software (SMERED) with postprocessing software in R. Developed and coded by Rob Hall and Rebecca C. Steorts.
Invited Talks
• Modern Bayesian Record Linkage: Some Recent Developments and Open Challenges, Opening Workshop Plenary Talk, Isaac Newton Programme: Data Linkage and Anonymization;
Cambride, UK, July 2016 (upcoming).
• The microclustering problem: What if the clusters don’t grow with the data?, ISBA World
Meeting, Sardinia, Italy; July 2016 (upcoming).
• The microclustering problem: What if the clusters don’t grow with the data?, ENAR, Austin,
TX; March 2016 (upcoming).
• Modern Bayesian Record Linkage: Some Recent Developments and Open Challenges, NISS
Affiliates Meeting, Austin, TX; March 2016 (upcoming).
• The microclustering problem: What if the clusters don’t grow with the data?, Late Breaking
News Session, Sixth IMS-ISBA joint meeting Bayes Comp at MCMSki V, January 2016.
• The microclustering problem: What if the clusters don’t grow with the data?, Spotlight presentation, Bayesian Nonparametrics: The Next Generation Workshops, December 2015.
• Spotlight presentation for Big Data Analytics for Estimating Death Casualties in the Syrian
Civil War, EmTech, November 2015.
• Apply it like you do: Being a modern applied statistician from start to finish, FOCUS Interdisciplinary Discussion Course For Duke Freshman, Duke University, September 2015.
• Propagating Record Linkage Uncertainty in the Estimation of the Number of Killings in Civil
Wars, Invited Session at Joint Statistical Meetings (JSM), Boston, August 2015.
• The small clustering problem: What if the clusters don’t grow with the data?, University of
Padua Faculty Seminar, June 2015.
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• Spatial Small Area Methods Applied to Poverty Estimation, ITACOSM 2015, 4th ITAlian
Conference on Survey Methodology, Rome, Italy, June 2015.
• The small clustering problem: What if the clusters don’t grow with the data?, IMS-Microsoft
Research Workshop: Foundations of Data Science, Boston, MA, June 2015, http://research.
microsoft.com/apps/video/default.aspx?id=249219&r=1.
• The small clustering problem: What if the clusters don’t grow with the data?, NCRN Spring
Meeting, National Academy of Science, Washington, DC, May 2015.
• Discussion of Doing Data Science, Straight Talk from the Frontline by Rachel Schutt, Chief
Data Scientist and Senior Vice President of NewsCorp, Special Invited Session, ENAR 2015.
• Methods for Quantifying Conflict Casualties in Syria, University of Minnesota Biostatistics
Faculty Seminar, October, 2014; Bayes in Paris Seminar, Universite Paris Dauphine, Paris,
France, November, 2014, Cornell University, Statistical Science Faculty Seminar, December
2014, The Hopkins Department of Biostatistics Faculty Seminar, December 2014, North Carolina State University Statistics Faculty Seminar, January 2015; Florida State University
Statistics Faculty Seminar, January 2015. University of Texas A&M, Statistics Faculty Seminar, Pennsylvania State University Faculty Seminar, January 2015; University of Minnesota
Statistics Faculty Seminar, January 2015, UC Berkley Faculty Seminar, January 2015; Duke
University Department of Statistical Science Faculty Seminar, January 2015; University of
North Carolina at Chapel Hill, Department of Biostatistics Faculty Seminar, February 2015;
Duke University Computer Science Faculty Seminar, September 2015; Joint Machine Learning
MIT & Microsoft Research Seminar, Boston, November 2015.
• K-Means Locality Sensitive Hashing as Applied to Human Rights Violations. Privacy and
Statistical Databases, Ibiza, Spain, September 2014.
• An Empirical Bayesian Approach to Graphical Entity Resolution. University of Trier Faculty
Seminar, Department of Economics and Social Statistics; Trier Germany, August 2014.
• Spatial Small Area Methods Applied to Poverty Estimation, NSF-Census Research Network
Annual Meeting, New York City, September 2014; Small Area Estimation Conference, Poland,
September 2014; School of Economics Faculty Seminar, La Spienza, Univerita di Roma, May
2014.
• Record Linkage and Other Statistical Models for Quantifying Conflict Casualties in Syria.
Computational Methods for Surveys and Census Data in the Social Sciences, University of
Montreal, June 2014.
• Will the Real Malay Ghosh Please Stand Up: A Graphical Approach to Record Linkage,
Plenary Talk, Frontier of Hierarchical Modeling in Observational Studies, Complex Surveys,
and Big Data: A Conference Honoring Professor Malay Ghosh, College Park, MD, May 2014.
• An Empirical Bayesian Approach to Graphical Record Linkage with Applications to Human
Rights Violations in Syria. Joint Conference in Data Mining in Business and Industry,
Durham, NC, June 2014.
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• SMERED: A Bayesian Approach to Graphical Record Linkage and De-duplication, Seventeenth International Conference on Artificial Intelligence and Statistics, Reykjavik, April
2014.
• Will the real Steve Fienberg please stand up: Getting to know a population from multiple
incomplete files. School of Statistical Sciences Faculty Seminar, Spienza, Univerita di Roma,
May 2014; Columbia University Faculty Seminar, Department of Applied Mathematics and
Physics, April 2014; Iowa State Faculty Seminar, Department of Statistics, March 2014;
Carnegie Mellon University, Statistics Department Faculty Seminar, March 2014; Columbia
University, Statistics Department Faculty Seminar, February 2014; Joint Statistical Meetings,
Invited Session, Montreal, August 2013; 5th IMS New Researchers Conference, Montreal,
August 2013; Center for Survey and Research Methodology Seminar, U.S. Census Bureau,
Suitland, MD, June 2013; Department of Statistics Faculty Seminar, UCLA, Los Angeles,
CA, March 2013.
• Clustering Approaches to Human Rights Violations in Syria. Machine Learning and the Social
Sciences Seminar, February 2014; SAMSI Workshop: Censuses and Surveys, Washington, DC
January 2014.
• The NSA Knows Who You Are, Alan Gelfand: An Approach to Bayesian Graphical Linkage.
Department of Statistical Sciences, Duke University, October 2013.
• Balancing the books by benchmarking: What to do when small area estimates just don’t add
up. Department of Statistics Faculty Seminar, Pennsylvania State University, November
2012; Department of Statistics and Probability Faculty Seminar, Michigan State University,
September 2012.
• On Estimation of Mean Squared Errors of Benchmarked Empirical Bayes Estimators. Fields
Institute Symposium on the Analysis of Survey Data and Small Area Estimation in honour
of the 75th Birthday of Professor J.N.K. Rao, Carleton University, Ottawa, Canada, June
2012.
• Bayes and Empirical Bayes Benchmarking for Small Area Estimation. U.S. Census Bureau,
Survey and Research Methodology Division, Washington, D.C., December 2011; Williams
College; Wake Forest University; Bucknell University; Clemson University; University of Missouri; Carnegie Mellon University; January – February 2012.
Invited Short Courses
• Teaching Bayes: the Essential Parts. Short Course (3-hour short course), ISBA World Meeting, Sardinia, Italy; July 2016 (upcoming).
• Fundamentals and Applications of Bayesian Analysis. Short Course (12-hour course), joint
with Malay Ghosh, Novartis Oncology Global Development, Florham Park, NJ, May 2011.
• An Introduction to Privacy and Statistical Disclosure. Short Course at the Universite Paris
Dauphine, Mathematiques, Apprentissage et Sciences Humaines (MASH), 12-hour course,
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Paris, France; November 2014.
Contributed Talks and Posters
• The small clustering problem: What if the clusters don’t grow with N?, G70: A Celebration
of Alan Gelfand’s 70th Birthday, Duke University, Durham, NC 2014 (upcoming) 2014.
• Variational Bayes for Merging Noisy Databases, Advances in Variational Inference, NIPS,
Montreal, December 2014.
• Locality Sensitive Hashing and Entity Resolution Approaches as applied to Syrian Human
Rights Violations, Twelfth World Meeting of ISBA, Cancun, July 2014.
• The NSA Knows Who You Are, Steve Fienberg: An Approach to Bayesian Graphical Linkage. Bayes 250 and O’Bayes Meeting, Department of Statistical Sciences, Duke University,
December 2013.
• Small Areas, Benchmarking, and Political Battles: Today’s Novel Demands in Small-Area
Estimation.
The First Asian ISI Satellite Meeting on Small Area Estimation, Bangkok,
September 2013; Joint Statistical Meetings, Invited Session, Montreal, August 2013.
• Will the real Steve Fienberg please stand up: Getting to know a population from multiple
incomplete files. and Struggles with small area estimation: benchmarking and weighting.
NSF–Census Research Network, Poster Session, May 2013, NISS Headquarters, Research
Triangle Park; New Directions in Monte Carlo Methods Workshop, University of Florida,
Gainesville, FL, January 2013; Women in Machine Learning Workshop, Poster Presentation
and NIPS Workshops Spotlight and Poster Presentation; Lake Tahoe, NV, December 2012;
• On Estimation of Mean Squared Errors of Benchmarked Empirical Bayes Estimators. TopicContributed Session, Joint Statistical Meetings, San Diego, July 28–August 2, 2012; Poster
Presentation, ISBA 2012 World Meeting, Kyoto, Japan, July 2012.
• Bayes and Empirical Bayes Benchmarking for Small Area Estimation. Faculty Seminar,
University of Florida, April 2012.
• Two-Stage Benchmarking with Applications to Small Area Estimation. Topic-Contributed
Session, Joint Statistical Meetings, Miami Beach, FL, August 2011.
• Bayesian Benchmarking with Applications to Small Area Estimation. Joint Statistical Meetings, Vancouver, BC, August 2010.
• Bayesian Benchmarking as Applied to Small Area Estimation. Graduate Student Seminar,
Department of Statistics, University of Florida, Gainesville, FL, March 2010.
• An Introduction to JAGS. Graduate Student Seminar, Department of Statistics, University
of Florida, Gainesville, FL, October 2009.
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• Novel Method for Solid-Phase Peptide Synthesis Using Microwave Energy. Collins, J., Collins,
M. and Steorts, R. Presented at the 18th American Peptide Symposium, Boston, MA, July
22, 2003; Poster P267 (abstract published in Biopolym., 2003, 71 371).
Teaching
Instructor
Bayesian Methods and Modern Statistics, 360/601, Duke University, Spring 2015.
• Wrote all notes for the course based on unpublished lecture notes.
• Course website, notes, and materials here: https://stat.duke.edu/~rcs46/bayes.html.
• 70 students in the course (undergraduate and graduate).
Predictive Modeling, 521, Duke University, Fall 2015.
• Revised course material on predictive modeling to incorporate both data mining and Bayesian
methods. Made the course more data driven. Focused homeworks, exams, and data analysis on big data sets, reproducible research, and version control. Also, focused on students
getting experience with communication with interactive exercises during class and “spotlight
presentations” at the start of class.
• Course website, notes, and materials here: http://www.stat.duke.edu/~rcs46/predict.
html.
• 41 students in the course (full capacity).
Applied Multivariate Methods, 36-464/36-664, Carnegie Mellon University, Spring 2014.
• Revised course material on multivariate methods, data mining, and Bayesian analysis. Focused homeworks, exams, and data analysis on big data sets.
• Course website, notes, and materials here: http://www.stat.cmu.edu/~rsteorts/multivar.
html. Covered high-dimensional concepts and students worked on high-dimensional real
datasets, e.g. FMRI and reconstructing images using Bayesian methods.
• Overall evaluations (60 students—30 profession Masters and 30 undergraduate): 3.6/5.
Bayesian Theoretical Statistics I and II, 36-786/36-787, Carnegie Mellon University, Spring 2013.
• Proposed/developed entire course including notes/manuscript.
• Theoretical and applied notes can be found at http://www.stat.cmu.edu/~rsteorts/teaching.
html.
• Overall evaluation of course (15 students): 4.33/5.
Introduction to Bayesian Statistics (Honors), STA 4930, University of Florida, Spring 2011.
• Proposed/developed entire course for UF Innovation through Institutional Integration Program.
Developed notes and manuscript for the course.
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Elementary Statistical Inference, MTHSC 203, Clemson University, Fall 2006–Spring 2007.
Teaching Assistant
Introduction to Statistics II, STA 3024, University of Florida, Spring 2008.
Elementary Statistical Inference, MTHSC 203, Clemson University, Spring 2006.
Precalculus and Introductory Di↵erential Calculus, MTHSC 104, Clemson University, Fall 2005.
Research Students
Undergraduate Thesis Supervision:
• Michael Pane, Honors Thesis and Census Research (2012–2014), now at Kroger Inc as data
analyst.
• Peter Sadosky, Honors Thesis on Locality Sensitive Hashing Applied to Human Rights Violations (2013–2015), now at Uber as data scientist
Undergraduate Supervision:
• Sepideh Mosaferi (2012), now at JPSM for PhD
• Kairavi Chahal (2013), now at American Express as data analyst
• Dahiana Jiminez (2013);
• Emily Furnish (2013 – 2014), now at William and Mary Law school
• Stephanie Stern (2013 – 2014), now at University of Michigan for MS
• Angie Shen (2016 – ).
MS Supervision: Lei Bai (2015–).
Graduate Student Collaboration: Rob Hall (2012–2015), Aaditya Ramdas (2013–2015),
Mauricio Sadinle (2014), Samuel Ventura (2013–2014), Leila Wehbe (2013–2015), Anshumali Shrivastava (2014–2015).
Postdoctoral Student Supervision: Nabanita Muhkerjee (2015–), Brenda Bentacourt (2015–
).
PhD Student Supervision: Nicolas Kim (ADA Project), joint with Stephen E. Fienberg and
William F. Eddy, The E↵ect of Data Swapping on Contingency Table Analysis (2013–2014).
Abbas Zaidi, (2015–).
Thesis Committees: Zachary Kurtz (2012–2014), Mauricio Sadinle (2013–2015), Samuel Ventura
(2013–2015), Rafael Stern (2013–2015).
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Service to Profession
• Refereeing for the Annals of Applied Statistics, Computational Statistics and Data Analysis,
Journal of Agricultural, Biological, and Environmental Statistics, Journal of the American
Statistical Association, Journal of Machine Learning Research, Journal of Official Statistics, Journal of Privacy and Confidentiality, Journal of Multivariate Analysis, Journal of the
Royal Statistical Society, Series A Journal of Survey Statistics and Methodology, Journal of
Statistical Planning and Inference, Proceedings of the National Academia of Sciences of the
United States, Statistical Methods and Applications, Statistics in Medicine, TEST, Springer
Publishing; New York, Transactions of Knowledge and Data Engineering.
• ICML Program Committee, 2016
• Scientific Program Committee, Small Area Estimation Conference, Netherlands, Summer
2016.
• Duke IT Strategic Planning Working Group in Research Computing, 2015–
• National Science Foundation reviewer, 2015.
• Committee on Scientific Freedom and Human Rights, American Statistical Association, 2015–
2017.
• Tenure Track Search Committee, Duke University, 2015–2016.
• Seminar Chair, Department of Statistical Science, Duke University, Spring 2016–
• Machine Learning Seminar Organizing Committee, Duke University, 2015–.
• Computation Committee, Department of Statistical Science, Duke University, 2015–.
• Invited Program Committee Member – NIPS, Nonparametric Bayesian Workshop 2015.
• Invited Program Committee Member: IEEE ICDM (International Conference on Data Mining) Workshop on Data Integration and Applications (DINA) 2014, 2015.
• Organizing Committee of the Frontier of Hierarchical Modeling in Observational Studies,
Complex Surveys, and Big Data: A Conference Honoring Professor Malay Ghosh; College
Park, May 29–31, 2014.
• Committee of Graduate Admissions, Carnegie Mellon University, Department of Statistics,
December 2012–January 2013.
• Co-chair of Faculty Seminars and co-organizer of event planning committee, Carnegie Mellon
University, Department of Statistics, 2012–2013; Chair 2013–2015.
• Co-Organized Invited Session (with Megan Price, HRDAG) for Joint Statistical Meetings on
human rights violations for entity resolution and capture recapture, Boston, August 2014.
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• Organized Invited Session for Joint Statistical Meetings on entity resolution, Montreal, QC,
August 2013.
• Organized Topic-Contributed Session for Joint Statistical Meetings on small area estimation
and benchmarking, San Diego, CA, August 2012.
Professional Memberships
American Statistical Association (ASA), Institute of Mathematical Statistics (IMS), International
Society for Bayesian Analysis (ISBA).
Computer Skills
R, Java, C++ Perl, Python, MySQL, LATEX.
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