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CV

Department of Statistics and Data Science 1198 Comstock Hall

Cornell University Ithaca, New York, 14853-2601

e-mail: fb238 “at” cornell “dot” edu

Education

  • Ph.D. in Statistics, University of Washington, Seattle, 2000.
    Advisor: Jon A. Wellner.
  • B.S. / M.S. in Mathematics, First Class, University of Bucharest, Romania, 1991.

Employment

  • Professor: Cornell University, Department of Statistics and Data Science, 2011 –
  • Assistant/Associate Professor: Florida State University, Department of Statistics, 2000- 2011.
  • Research/Teaching Assistant : University of Washington, Department of Statistics and the National Research. September 1995 – August 2000.
  • Assistant Professor: University Politehnica Bucharest, Department of Mathematics (Romania). September 1991 – September 1995.

Research Interests

  • Foundations of data science; statistical machine learning theory.
  • High dimensional statistical inference in parametric and non-parametric models: regression, covariance estimation, graphical models, model-based clustering (hard clustering and over- lapping clustering), cluster-based models, latent variable models, topic models, networks, new perspectives in prediction problems, comparison of clustering schemes in Wasserstein distance.
  • Applications to Immunology, Systems Biology, Genetics, Neuroscience and other areas.

Editorship

  • Series Editor: Monographs on Statistics and Applied Probability (The Green Series), Chapman & Hall/CRC (2009 – ongoing).
  • Associate Editor: The Annals of Statistics (2007 – 2010; 2016 – ongoing).
  • Associate Editor: The Electronic Journal of Statistics, EJS (2007 – ongoing). Guest Editor for a special 2016 issue on High dimensional modeling.
  • Associate Editor: The Journal of the American Statistical Association, JASA – Theory and Methods, (2011 – 2017).
  • Associate Editor: Bernoulli (2010 – 2016).
  • Associate Editor: Journal of the Royal Statistical Society (JRSS)-B (2012- 2014)
  • Associate Editor: The Annals of Applied Statistics (2011 – 2012).
  • Associate Editor: The International Journal of Biostatistics, Berkeley Press (2007 – 2012).

Grants

  • NSF-DMS award 2015195, PI, Learning from hidden signatures in high dimensional models, 2020 – 2023.
  • NSF-DMS award 1712709, PI, Statistical foundations of model-based variable clustering, 2017 – 2020.
  • NSF-DMS award 1310119, co-PI, Estimation of high dimensional matrices of low effective rank with applications to structural copula models, 2013 – 2016.
  • NSF-DMS award 10007444, PI, Matrix estimation under rank constraints for complete and incomplete noisy data, 2010 – 2013.
  • (NSF-DMS) award 0925275, PI, conference grant, From Probability to Statistics and Back: High-Dimensional Models and Processes, Seattle, July 28 – 31, 2010.
  • (NSF-DMS) award 0706829, co-PI: Sparsity oracle inequalities via l1 regularization in non- parametric models, 2007 – 2010.
  • (NSF-DMS) 0406049, PI: Curve aggregation and classification, 2004 – 2007.

Invited Fellow/Scholar/Honors

  • Elected Fellow of the Institute of Mathematical Statistics (IMS), 2017.
  • Fellow of the Newton Institute, Cambridge University, UK, 2008, 2016, 2018.
  • SAMSI fellow, Spring 2014.
  • Invited Professor, Universit ́e de Paris VI and CREST, France, 2003, 2009 (Host: Alexandre Tsybakov.).
  • Invited Visiting Scholar, The John Hopkins School of Public Health, Jan/Feb 2008.
  • Invited Researcher, LInstitut Henri Poincar ́e, Paris, France, April – July 2001. (Host: Lucien Birg ́e.)
  • Invited Researcher, Universit ́e Rennes II, France, August 2001. (Host: Marian Hristache.)
  • The Centre National de la Recherche Scientifique (CNRS) Award in Statistics for young researchers, 2001 (CNRS is the French equivalent of the National Science Foundation.)

Service to the academic community at large, diversity and education

  • Institute of Mathematical Statistics (IMS) Council Member (elected), 2014 – 2017.
  • IMS representative to the selection committee of the COPSS F.N. David Award, 2014 – 2018.
  • NSF – DMS Panel member: 2008, 2009, 2011, 2012, 2014, 2015, 2017, 2018.

Conference and/or Program co-organizer

  • Re-thinking high dimensional statistics, Mathematisches Forschungsinstitut Oberwolfach (MFO), Germany, 2022 (to come).
  • CIS @ 20, A conference celebrating 20 years since the creation of the college of Computing and Information Science (CIS) at Cornell University, October 2019.
  • Matrix Estimation Meets Statistical Network Analysis: Extracting low-dimensional structures in high dimension, Mathematisches Forschungsinstitut Oberwolfach (MFO), Germany, 2018.
  • Theory of Big Data, London, UK, 2017.
  • Cornell Day of Statistics, Cornell University, September 2015, 2016.
  • LDHD Workshop: Statistical Inference in Sparse High-dimensional Models: theoretical and computational challenges, SAMSI, North Carolina, February 2014.
  • Summer school organizer for the LDHD SAMSI Program, SAMSI, North Carolina, August 2013.
  • Program leader for the 2013 – 2014 SAMSI Program on Low-dimensional Structure in High- dimensional Systems (LDHD).
  • From Probability to Statistics and Back: High-Dimensional Models and Processes, Seattle, Washington, July 2010.
  • Nonparametrics Statistics, International Conference,Tallahassee, Florida, Jan 2003.

Publication Record

For most recent papers, please see my Recent Publications page.

  • Xin Bing, Florentina Bunea and Marten Wegkamp. Detecting approximate replicate compo- nents of a high-dimensional random vector with latent structure, 2020, Submitted, https://arxiv.org/abs/2010.02288.
  • Xin Bing, Florentina Bunea, Seth Strimas-Mackey and Marten Wegkamp. Prediction in latent factor regression: Adaptive PCR and beyond, 2020, Submitted, https://arxiv.org/abs/2007.10050.
  • Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp, Interpolation under factor regres- sion models, 2020, Submitted, https://arxiv.org/abs/2002.02525
  • Xin Bing, Florentina Bunea, Marten Wegkamp, Inference in interpretable latent factor re- gression models, 2020, Submitted, https://arxiv.org/abs/1905.12696;
    Winner of the Larry Brown Award.
  • Florentina Bunea, Christophe Giraud, Martin Royer, Nicolas Verzelen, and Xi Luo, Model- assisted variable clustering: minimax-optimal recovery and algorithms, The Annals of Statis- tics, 2020, Vol. 48, pp. 111 – 137, https://arxiv.org/pdf/1508.01939.pdf.
  • Xin Bing, Florentina Bunea, Marten Wegkamp, Optimal estimation of sparse topic models, 2020, Journal of Machine Learning Research, https://arxiv.org/abs/2001.07861
  • Xin Bing, Florentina Bunea and Marten Wegkamp, A fast algorithm with minimax op- timal guarantees for topic models with an unknown number of topics, 2020, Bernoulli, https://arxiv.org/abs/1805.06837
  • C. Eisenach, F. Bunea, Y. Ning and C. Dinicu, High Dimensional Inference in Cluster-Based Graphical Models, https://arxiv.org/abs/1806.05139, Journal of Machine Learning Re- search, 2020.
  • Xin Bing, Florentina Bunea, Yang Ning, Marten Wegkamp, Adaptive Estimation in Struc- tured Factor Models with Applications to Overlapping Clustering, The Annals of Statistics, To appear, 2020 https://arxiv.org/abs/1704.06977.
  • Xin Bing, Florentina Bunea, Martin Royer, Jishnu Das, Latent model-based clustering for biological discovery, iScience, 125 – 135, 2019.
  • F. Bunea, C. Giraud, M. Royer and N. Verzelen, PECOK: a convex optimization approach to variable clustering, https://arxiv.org/abs/1606.05100 (2016, 2017).
  • F.Bunea, C. Giraud and X. Luo, Minimax optimal variable clustering in G-models via CORD https://arxiv.org/pdf/1508.01939.pdf (2015, 2017).
  • J. Bien, F. Bunea and L. Xiao, Convex banding of the covariance matrix, Journal of Amer- ican Statistical Association, Volume 111, 834-845, 2016.
  • J. Das, K.M. Gayvert, F. Bunea, M. Wegkamp and H. Yu: ENCAPP: elastic-net-based prog- nosis prediction and biomarker discovery for human cancers, BMC Genomics, Vol 16, 2015.
  • Florentina Bunea and Luo Xiao, On the sample covariance matrix estimator of reduced effec- tive rank population matrices, with applications to fPCA, Bernoulli, Volume 21, 1200-1230, 2015.
  • L. Xiao and F. Bunea: On the theoretical and practical merits of the banding estimator for large covariance matrices, http://arxiv.org/abs/1402.0844, EJS (2014).
  • Florentina Bunea, Johannes Lederer and Yiyuan She, The Group Square-Root Lasso: Theo- retical Properties and Fast Algorithms, IEEE-Information Theory, Vol 60, pages 1313 – 1325, (2014)
  • Edited volume. From Probability to Statistics and Back: High-Dimensional Models and Pro- cesses, A Festschrift in Honor of Jon Wellner; IMS Collections, Volume 9, 2012, Banerjee, M., Bunea, F., Huang, J., Koltchinskii, V., and Maathuis, M. H., Editors
  • Florentina Bunea, Yiyuan She and Marten Wegkamp Joint variable and rank selection for parsimonious estimation of high dimensional matrices. The Annals of Statistics, Vol 40, 2359-2388, (2012).
  • Florentina Bunea, Yiyuan She and Marten Wegkamp Optimal selection of reduced rank esti- mators of high dimensional matrices. The Annals of Statistics, Vol 39, 1282- 1309, (2011).
  • Florentina Bunea, Andrada Ivanescu and Marten Wegkamp Adaptive inference for the mean of a Gaussian process in functional data. J.R. Statist. Soc. B, Vol 73 (4), 531 – 558, (2011).
  • Florentina Bunea, Yiyuan She, Hernando Ombao et al. Penalized Least Squares Regression Methods and Applications to Neuroimaging. NeuroImage, Vol. 55, 1519 – 1527 (2011).
  • Florentina Bunea, Alexandre Tsybakov, Marten Wegkamp and Adrian Barbu SPADES and mixture models, The Annals of Statistics, Vol 38, 2525 – 2558, (2010).
  • Florentina Bunea and Adrian Barbu Dimension reduction and variable selection in case- control studies via regularized likelihood optimization, Electronic Journal of Statistics, Vol. 3, 1257 – 1287, ( 2009 )
  • Florentina Bunea Honest variable selection in linear and logistic regression models via l1 and l1 + l2 penalization, Electronic Journal of Statistics, Vol 2, pp 1153 – 1194, (2008).
  • Florentina Bunea and Andrew Nobel Online prediction algorithms for aggregation of arbitrary estimators of a conditional mean. IEEE Transactions on Information Theory, Vol 54 (4), pp 1725 – 1735, (2008).
  • Florentina Bunea Consistent selection via the Lasso for high dimensional approximating mod- els. IMS Lecture Notes-Monograph Series, Vol 123, pp. 123 – 137, (2008).
  • Florentina Bunea, Alexandre Tsybakov and Marten Wegkamp Sparsity oracle inequalities for the lasso. Electronic Journal of Statistics, pp. 169 – 194, (2007).
  • Florentina Bunea, Alexandre Tsybakov and Marten Wegkamp Sparse density estimation with l1 penalties. Annual Conference on Learning Theory, COLT: Lecture Notes in Artificial Intelligence, pp. 530 – 544, Springer, (2007).
  • Florentina Bunea, Alexandre Tsybakov and Marten Wegkamp Aggregation for Gaussian re- gression. Annals of Statistics, Vol 35, 1674 – 1697, (2007).
  • Florentina Bunea, Alexandre Tsybakov and Marten Wegkamp Aggregation and sparsity via l1 penalized least squares. COLT: Lecture Notes in Artificial Intelligence, Springer, pp. 379 – 391, (2006).
  • Florentina Bunea, Marten Wegkamp and Anna Auguste Consistent covariate selection in high dimensional regression via multiple testing. Journal of Statistical Planning and Infer- ence, Elsevier, Vol 136, pp 4349-4364, (2006).
  • Florentina Bunea, Hernando Ombao and Anna Auguste Minimax adaptive spectral estima- tion from an ensemble of signals. IEEE Transactions on Signal Processing, Vol. 54, pp. 2865 – 2874, (2006).
  • Gerard Biau, Florentina Bunea and Marten Wegkamp Function Classification in Hilbert Spaces. IEEE Transactions on Information Theory, Vol. 51, pp 2163 – 2172, (2005).
  • Florentina Bunea and Ian McKeague Covariate selection for semiparametric hazard function regression models. The Journal of Multivariate Analysis, Vol. 92, pp. 186 – 204, (2005).
  • Florentina Bunea Consistent covariate selection and postmodel selection inference in semi- parametric regression. The Annals of Statistics, Vol. 32, No. 3, pp. 898 – 927, (2004).
  • Florentina Bunea and Marten Wegkamp Two-stage model selection procedures in partially linear regression. The Canadian Journal of Statistics, Vol. 32, pp. 105 – 118, (2004).
  • Florentina Bunea and Marten Wegkamp. A Note on Penalized Minimum Distance Estima- tion in Nonparametric Regression. The Canadian Journal of Statistics, Vol. 31, No 3, pp. 267-274, (2003).
  • Florentina Bunea and Julian Besag Markov Chain Monte Carlo in I × J × K contingency tables, Fields Institute Communications, AMS, Providence, Rhode Island. Vol. 26, pp. 25 – 36, (2000).
  • Florentina Bunea Geometrical restrictions in self deconvolution, The Annals of University of Bucharest, Mathematics and Informatics Series, Vol. 44, pp. 41-50, (1995).

Invited Session Organizer and Invited Presentations

Conference session organizer

  • Invited chair and organizer of the session ”High Dimensional Data Inference” IMS/Bernoulli World Congress of Probability and Statistics, Seoul, South Korea, 2020.
  • Invited chair and organizer of the session ”High Dimensional Inference in Structured Models”, the European Meeting of Statisticians, Palermo, Italy, 2019.
  • Invited chair and organizer of the session Model selection in high dimensions, 9th World Congress of Probability and Statistics, Fields Institute, Toronto, July 11-15, 2016.
  • Invited chair and organizer of the session New directions in big data theory, 3rd conference of the International Society for Non-Parametric Statistics (ISNPS), Avignon, France 11-16 June 2016.
  • Invited chair and organizer of the session High-Dimensional Inference, the European Meet- ing of Statisticians, Amsterdam, 2015.
  • Invited chair and organizer of the session ”High-Dimensional Statistical Inference for Matrix Models”, at WNAR, Colorado, June 2012.
  • Invited chair and organizer of the session ”High dimensional inference and matrix models”, at the 1st conference of the International Society for Non-Parametric Statistics, Greece, June 2012.
  • Program committee member and chair of the ”Machine Learning” session for SRCOS (South- ern Regional Conference on Statistics), Norfolk, Virginia, June 2010.
  • Invited chair and organizer of the session ”Recent results in theoretical machine learning” at the eighth ICSA International Conference: Frontiers of Interdisciplinary and Methodological Statistical Research, Guangzhou University, China, Dec 2010.
  • Invited chair and organizer of the invited IMS session “Advances in Statistical Aggregation” at the Joint Statistical Meeting, Salt Lake City, August 2007.
  • Invited chair and organizer of the invited IMS session “Semiparametric Inference in Practice” at the Joint Statistical Meeting, Seattle, August 2006.
  • InvitedchairandorganizeroftheinvitedIMSsession“Aggregationinnon-parametricmodels” at the Joint Statistical Meeting in Minneapolis, August 2005.

Invited conference and workshop talks

  • Mathematical Methods of Modern Statistics, Marseille, June 2020 (postponed).
  • Mathematical Statistics and Learning, Barcelona, June 2020 (postponed).
  • Statistics in the Big Data Era, the Simons Institute, May 2020 (postponed).
  • Missing Data Challenges, Institute for Advanced Studies, Princeton University, March 2020.(postponed)
  • Fourth Workshop on High Dimensional Asymptotics and Post-Selection Inference, St. Louis, August, 2019.
  • Machine Learning and Data Science, Columbia University, June 2019.
  • Oberwolfach meeting on Statistical and Computational Aspects of Learning with ComplexStructure, May 2019
  • Joint Statistical Meeting, Vancouver, 2018.
  • Isaac Newton Institute workshop: Future challenges in statistical scalability, Cambridge, UK, June 2018.
  • Oberwolfach workshop Matrix estimation meets network analysis, Germany, June 2018.
  • International workshop on New Aspects of Statistics, Financial Econometrics and Data Sci- ence, University of Chicago, May 2018.
  • Oberwolfach workshop on Statistical Inference for Structured High-dimensional Models, Ger- many, March 2018.
  • Meeting in Mathematical Statistics, Marseilles, France, 2017.
  • Second Workshop on High Dimensional Asymptotics and Post-Selection Inference, St. Louis,2017.
  • Joint Statistical Meeting, Baltimore, 2017.
  • Conference on Foundations of Computational Mathematics, Barcelona, Spain, 2017.
  • Meeting in Mathematical Statistics: Advances in nonparametric and high-dimensional Statis- tics, Fr ́ejus, France, 2016.
  • Workshop on High Dimensional Asymptotics and Post-Selection Inference, St. Louis, 2016.
  • New Researchers Pre-meeting of the IMS/Bernoulli World Congress for Probability and Statistics, Toronto, 2016, Plenary speaker.
  • Theory of Big Data 2, London, January 2016, Plenary speaker.
  • JSM, Seattle, August 2015.
  • Oberwolfach Workshop, Probabilistic Techniques in Modern Statistics, Oberwolfach, Ger- many, May 2015.
  • American Institute of Mathematics (AIM), Palo Alto, CA, Workshop on Inference in high dimensional regression, 2015.
  • Joint Statistical Meeting, Boston, August 2014.
  • Dimension Reduction and High Dimensional Inference Workshop, University of Florida, 2014.
  • International Symposium on Business and Industrial Statistics/Conference of the ASA Section on Statistical Learning and Data Mining, Durham, June 2014.
  • Oberwolfach Workshop, Adaptive Statistical Inference, Oberwolfach, Germany, March 2014.
  • 3rd Princeton Day of Statistics Workshop, Oct. 19, 2012.
  • International Chinese Statistics Association Applied Statistics Symposium, Boston, June 2012, Simultaneous variable and rank selection for optimal estimation of high dimensional matrices.
  • High-dimensional problems in statistics workshop, ETH, Zurich, Switzerland, September 2011, Simultaneous variable and rank selection for optimal estimation of high dimensional matrices.
  • Instantaneous Frequencies and Trends for Nonstationary Nonlinear Data, IMA, Minneapolis, September 2011, Simultaneous variable and rank selection for optimal estimation of high dimensional matrices.
  • Sparse Statistics, Optimization and Machine Learning BIRS Workshop, Banff, Canada, Jan- uary 2011, Optimal selection of reduced rank estimators of high dimensional matrices.
  • The Applied Statistics Symposium of the International Chinese Statistical Association, Indi- anapolis, June 2010, Adaptive Rank Penalized Estimators in Multivariate Regression.
  • Conference on Resampling Methods and High Dimensional Data, College Station, Texas, March 2010. Optimal dimension reduction in high dimensional matrix models via the Rank Selection Criterion.
  • Sparsity in high dimensions conference, The Mathematical Institute at Oberwolfach, Ger- many, March 2009, Model selection and sparsity in case control studies.
  • Understanding the New Statistics: Expanding Core Statistical Theory, Banff, Canada, Septem- ber 2008. Honest variable selection.
  • International Workshop on Sparsity in High Dimensional Statistics and Learning Theory, March 2008, Georgia Institute of Technology, Atlanta. On l1 regularization in functional data and binary response regression models.
  • The Isaac Newton Institute, Cambridge University, UK, Programme on Statistical Theory and Methods for Complex, High-Dimensional Data, May 2008. Non-asymptotic variable selection.
  • The Joint Statistical Meeting, the Institute of Mathematical Statistics section, Salt Lake City, August 2007. Sparse oracle inequalities for the Lasso.
  • The Annual Conference on Learning Theory (COLT), San Diego, June 2007. Identifying components in sparse mixtures: the Spades estimator.
  • Statistical and Probabilistic Methods in Model Selection, The Mathematical Institute at Oberwolfach, Germany, October 2005. On-line prediction algorithms in aggregation.
  • Kickoff Workshop of the SAMSI program on High Dimensional Inference and Random Ma- trices, September 2006.
  • The Joint Statistical Meeting, the Institute of Mathematical Statistics Section, Seattle, Au- gust 2006.
  • The Joint Statistical Meeting, the Institute of Mathematical Statistics section, Minneapolis, August 2005. Aggregation for regression learning.
  • SRCOS/ASA Summer Research Conference, Virginia Tech, June 2004. Oracle inequalities in aggregation.
  • International Workshop on Applied Probability, Greece, March 2004. One and multiple- sample aggregation in nonparametric regression models.
  • New Researchers Conference, Davis, CA, July 2003. Consistent model selection via the FDR procedure.
  • Nonparametrics Research Conference, Tallahassee, FL, January 2003. The consistency of the FDR procedure with applications to semiparametric models.
  • International conference on current advances and trends in nonparametric statistics, Crete, July 2002. Post model selection inference in semiparametric regression models.
  • IMS meeting in Guanajuato, Mexico, 2000.Partially linear regression.
  • SIAM Annual Meeting, Atlanta, 1999. MCMC for multi-dimensional contingency tables.

Invited Individual Seminars

  • Canada: University of Toronto (2002), University of British Columbia (2000).
  • France: Universit ́e de Paris VI and CREST (Paris, 2009), Universit ́e de Paris VI (Paris, 2003), ENSAI (Rennes, 2001), L’Institut Henri Poincar ́e (Paris, 2001).
  • United Kingdom: Bath University (2001), Newton Institute, Programme on Statistical Theory and Methods for Complex High-dimensional Data, Cambridge (2008).
  • Switzerland: The Swiss Institute of Technology (ETH), Zurich (2008).
  • USA
      • 2020: NYU, Currant Institute.
      • 2019: University of Washington, Temple University, Harvard University.
      • 2017: Columbia
      • 2016: Princeton
      • 2014: Brown
      • 2010, 2012: Cornell University
      • 2008: University of Chicago.
      • 2007: Carnegie Mellon University, Johns Hopkins University, University of Minnesota, Rutgers University.
      • 2006: Texas A & M University, North Carolina State University.
      • 2005: University of Georgia, University of Illinois at Urbana.
      • 2004: The Pennsylvania State University, Duke University.
      • 2002: University of Florida.
      • 2000/2001: Yale University, University of Pennsylvania, Stanford University.

Courses (Sample)

  • Statistics through examples
  • Introduction to Probability
  • Mathematical Statistics
  • Probability Models and Inference
  • Probability and Measure
  • Probability Theory
  • Topics in Stochastic Processes
  • Advanced Topics in Probability and Statistics: Model Selection
  • Advanced Topics in Probability and Statistics: Semiparametric models and empirical processes
  • Advanced Topics in Probability and Statistics: Aggregation in non-parametric models
  • Advanced Topics in Probability and Statistics: Model selection and aggregation in high dimensions
  • Advanced Topics in Probability and Statistics: Inference in large scale problems: vector and matrix models.

Ph.D. Student supervision

Former Ph. D. students: Anna Auguste, Andrada Ivanescu, Shuva Gupta, Jennifer Geis, Claudiu Dinicu, Martin Royer. Current Ph.D students: Mike Bing, Seth Strimas-Mackey, Tejas Ramdas.

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