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Lindsay Assistant Professors

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The Lindsay Visiting Assistant Professor program was created in 2015 to honor Professor Bruce Lindsay, a leading statistician, mentor and a long time faculty member of Penn State Statistics.

Bruce Lindsay

 

 

 

 

 

 

 

 

 

 

Current Lindsay Assistant Professors


Marzia Cremona

Marzia Cremona

Cremona joined the Department of Statistics at Penn State as a post-doctoral researcher in 2016, working with Francesca Chiaromonte and Kateryna D. Makova, and in 2017 she was appointed to a Lindsay Visiting Assistant Professorship. Marzia Cremona received her B.Sc. in 2009, and M.Sc. in 2011, in Mathematics from the Università degli Studi di Milano, Italy. She obtained in 2016 her Ph.D. in Mathematical Models and Methods in Engineering from the Politecnico di Milano, with a thesis entitled "Statistical methods for omics Data." Her doctoral research advisor was Prof. Piercesare Secchi with co-advisors Prof. Laura M. Sangalli and Prof. Simone Vantini.

Cremona’s primary research interests are in the areas of statistical learning, computational statistics, and statistical “omics”. In her research, Cremona develops statistical and computational methods for the analysis of large, high-dimensional, and complex data – in particular, functional data. An important aspect of her research is its collaborative and multidisciplinary nature; indeed, much of her work is at the interface of statistics and computational biology, and her main application areas are the biomedical and “omics” sciences.

Among her publications are articles on the clustering of ChIP-seq data using peak shape or shape indices, the genome-wide effects of non-B DNA on polymerization speed and error rate, and the influence of the genomic landscape on the integration and fixation of endogenous retroviruses. In research in other areas, she has also studied methods for predicting railway wheel wear using kriging methods.

In research that was completed recently, Cremona has announced results on high-resolution views of adaptive events and on probabilistic methods for clustering and motif discovery in functional data.

Roberto Molinari

Roberto Molinari

Roberto obtained a Master’s degree in International Affairs at the LUISS Guido Carli University in Rome. He served subsequently as a consultant for the United Nations Economic Commission for Europe (UNECE), the Organization for Economic Cooperation and Development (OECD) and the accounting firm of Ernst & Young. Molinari subsequently received his M.Sc. and Ph.D. in Statistics at the University of Geneva, Switzerland, and then became a Visiting Assistant Professor in Statistics at the University of California, Santa Barbara.

Molinari then spent a year in Senegal where he served as a statistical consultant for the United Nations International Children's Emergency Fund (UNICEF); as a researcher at the Global Research and Advocacy Group (GRAG) on marginalized communities and the non-profit organizations (GRAG), conducting research on marginalized communities in sub-Saharan Africa; and as a researcher in the field of climatology and epidemiology at the Center for International Research on Environment and Development (CIRAD).

Molinari’s research interests include robust statistics, stochastic processes, model selection in high dimensions, computational statistics and data privacy. In his position as a Lindsay Assistant Professor, Molinari is researching a variety of topics among which are the development of non-parametric bootstrap techniques for the creation of differentially-private synthetic data with Prof. Aleksandra Slavkovic and Michelle Pistner, a doctoral student; in this project, he is also researching new criteria to assess the level of privacy of statistical procedures and synthetic data. Molinari also is conducting research with co-authors at Penn State (Prof. Stephane Guerrier and Dr. Mucyo Karemera), and with colleagues at the University of Illinois (Urbana-Champaign), the University of Maine, UC-Santa Barbara, the University of Geneva, the École Polytechnique Fédérale de Lausanne (EPFL-Switzerland), and CIRAD (Senegal).

Molinari is now finalizing new algorithms for the robust estimation of a large class of time series models, to be implemented in the gmwm-R package available on GitHub, and he is extending its applicability to multivariate and non-stationary time series analysis. Molinari is also developing new approaches for gene selection problems, Granger-causality for biological problems, and computationally efficient approaches to estimate complex models in high dimensional settings, with corresponding implementation in R packages.

Danning Li

Danning Li

Danning received in 2013 her Ph.D. in Statistics from the University of Minnesota, Minneapolis, advised by Professor Tiefeng Jiang. From 2013-2015, she was a post-doctoral research associate under the supervision of Professor Richard Samworth at the University of Cambridge, England.

Li worked subsequently as an Assistant Professor of Statistics at Jilin University, China and then in 2017 she joined the Department of Statistics at Penn State as a Lindsay Visiting Assistant Professor.

Li’s research areas include random matrix theory, high-dimensional statistical inference, empirical process, and applications to biological science, network science, and social science. Her papers have been published in journals in probability and statistics, including the Journal of Theoretical Probability, the Journal of Mathematical Physics, the Institute of Electrical and Electronics Engineers (IEEE) Transactions on Information Theory, and Statistica Sinica.

Li’s publications include results on the spectra of truncated random unitary matrices; these results are germane to applications involving quantum systems with absorbing boundaries, optical and semiconductor superlattices, quantum conductance, and the distribution of resonances for open quantum maps. She has also published research on the smallest eigenvalues of random matrices, a problem which arises in electrical engineering and in multivariate statistical analysis; her research connects the limiting distributions of the smallest eigenvalues with the celebrated Tracy-Widom probability distributions. Most recently, Li has obtained results that connect Stein’s method of unbiased risk estimation with the estimation of high-dimensional covariance matrices.

In current research, Li is now working with her co-authors to develop power enhancement tests for high-dimensional means, covariance matrices, regression models, and related network models.

Former Lindsay Assistant Professors   

 

Daisy Philtron

Philtron was a Lindsay Assistant Professor from fall 2015 to spring 2017.  She obtained her Ph.D. in statistics from Penn State in 2014.  Her research interests focus on statistical approaches to the analysis of biological and genetic data and the pedagogy of teaching statistics.
As a Lindsay Fellow Philtron worked with Dr. Ben Shaby on a project combining diverse data types into a single integrated analysis.  They developed a Bayesian three-groups model to classify genetic targets as belonging to one of three groups: beneficial, deleterious, or null.  The most current application of this work includes the joint analysis of both microarray and SNP data to investigate possible genetic contributors to Parkinson's disease. 

Philtron also led an effort to revitalize Penn State's largest statistics course. During her time as a Lindsay Fellow Philtron planned and implemented an effort to change Stat 200, an intro course with an enrollment of nearly 2000 each semester, from a traditional curriculum
to a simulation-based curriculum.  She taught the pilot course, developed materials for subsequent semesters, and worked with a team of faculty to coordinate all sections of the class. After the conclusion of her Lindsay professorship, Philtron joined the Penn State statistics faculty as an Assistant Research Professor, where she is continuing her work with Dr. Shaby and developing further course material for the undergraduate program.  In 2018 Philtron and Dr. Shaby won a 5-year NSF grant to support their continued research.
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