Priya Kohli

Priya Kohli

Associate Professor of Statistics
Assistant Chair of the Mathematics and Statistics Department

Joined Connecticut College: 2012

M.S., Indian Agricultural Statistical Research Institute, New Delhi
M.S., Northern Illinois University
Ph.D., Texas A&M University


Temporal, spatial and spatio-temporal modeling

Covariance modeling

Longitudinal studies

Multivariate data analysis

Missing data

RNA-seq analysis

Financial applications

Nonparametric statistics

Priya Kohli specializes in the areas of covariance modeling, longitudinal/panel studies, multivariate modeling, missing data, time series, spatial statistics, and spatio-temporal modeling. She also works in interdisciplinary research areas including RNA-seq analysis, healthcare devices, environmental sciences, and business and finance.

Professor Kohli’s research accomplishments include publication in the time series book "Economic Time Series: Modeling and Seasonality," her research work accepted as a U.S. patent and a European patent, and her publications in some of the most distinguished international statistics and interdisciplinary journals. She has been invited as a speaker to present her research work at several prestigious international conferences, including the Joint International Chinese Statistical Association and Graybill Conference, National Bureau of Economic Research – National Science Foundation Time Series Conference, Second Conference of the International Society of Nonparametric Statistics in Cadiz, Spain, International Symposium on Business and Industrial Statistics, International Conference on Advances in Interdisciplinary Statistics and Combinatorics.

More recently, she has been working on RNA-sequencing methodology for studying the notch and other signaling pathways during taste bud development in axolotls, also known as the Mexican salamanders. RNA sequencing, or RNA-seq, is a biological tool that allows for large scale genome profiling by means of sequencing an organism's transcriptome. This next generation sequencing technology allows for large-scale genome profiling to reveal both the types of genes expressed and their levels of expression. Taste buds are derived from the oropharyngeal endoderm during embryonic development. Once specified, this epithelium will give rise autonomously to patterned taste bud progenitor cells which then differentiate into mature taste receptor cells later in embryonic development. It is hypothesized in the literature that during the patterning stage, some of the pharyngeal endoderm cells become taste bud progenitors while the remaining cells remain as non-taste epithelium. Her collaborative work provides insights into taste bud development, which plays a key role in examining gene expression levels at different development stages. The focus is on studying the pathways and target genes that play a role during taste bud patterning and differentiation using the axolotl oropharyngeal explants as a model system. Using RNA-seq analysis, the work examines expression levels during taste bud patterning and differentiation by identifying specific genes that are expressed at varying levels during these two stages. It has been found that there are key genes that play specific roles during taste bud development and that there are different genes which are up- or down-regulated at particular stages that play functional roles in these stages.

Kohli is currently working on exploring the status of men caregivers in the United States. According to AARP & NAC, 2015, forty percent of informal (unpaid) family caregivers are men, up from 34 percent in 2009. Compared to women caregivers, though men also face difficulties, they have weaker support networks and are less likely to seek out programs that can help them cope with burden and increase their caregiving capabilities. Using National Study of Caregiving (NSOC) datasets and hierarchical regressions, Kohli is working with collaborators to identify the emotional, financial, and physical burden of four different groups of male caregivers, and the need for support that reduces stress associated with caregiving based on their personal characteristics, caregiving responsibilities, and available resources. The work shows that all groups reported multiple types of burden, regardless of whether they were primary or secondary caregivers, particularly when engaging in certain tasks such as personal care. The usage of caregiver support and training is also found to be extremely low for men caregivers. To this end, it is necessary to devise, implement, and market effective policies that intentionally include gender, age, and cultural differences in order to encourage more caregiving and to relieve the stress of different groups of male caregivers in particular, and of all caregivers in general.

Kohli is also currently working on developing a unified framework for modeling dependence structure in multivariate longitudinal studies for complete and incomplete data. Her research focuses on the developing data-adaptive unconstrained parametrizations for parsimonious modeling of covariance matrices to address the two major challenges in modeling covariance, that is, high-dimensionality and positive definiteness constraint.

Kohli teaches Statistical Methods, Advanced Regression Techniques, Time Series Analysis, Statistical Computing with R, Probability, and Mathematical Statistics.

Recent publications:

  • Kohli, P., Marazzi, L. and Eastman, D. (2020). Transcriptome analysis of axolotl oropharyngeal explants during taste bud differentiation stage, Mechanisms of Development, 161, March 2020,
  • Marazzi, L., Kohli, P. and Eastman, D (2020). Transcriptome dataset for RNA-seq analysis of axolotl embryonic oropharyngeal endoderm explants, Mechanisms of Development, Data in Brief, in press.
  • Lopez-Anuarbe M. and Kohli P. (2019). Understanding Male Caregivers Emotional, Financial, and Physical Burden in the United States, Healthcare, 7(2). E72,
  • Kohli P., Siver P. A., Marsicano L., Hamer J. and Coffin A*. (2017). Assessment of Long-term Trends for Management of Candlewood Lake, Connecticut, USA, Lake and Reservoir Management, 33(3), 280-300. Here * denotes an undergraduate student.
  • Kohli P., Siver P. A., Marsicano L., Hamer J. and Co_n A*. (2017). Assessment of
    Long-term Trends for Management of Candlewood Lake, Connecticut, USA, Lake and
    Reservoir Management, in press. Here * denotes an undergraduate student.
  • Harvill J. L., Kohli P. and Ravishanker N. (2017). Clustering nonlinear, nonstationary
    time series using BSLEX, Methodology and Computing in Applied Probability, in press.
    doi 10.1007/s11009-016-9528-1.
  • Kohli, P., Garcia T. P. and Pourahmadi, M. "Modeling the Cholesky factors of covariance matrices of multivariate longitudinal data," Journal of Multivariate Analysis, 145, 87-100, 2016.
  • Kohli, P. "Fractional Bivariate Exponential estimator for long-range dependent random field," Spatial Statistics, 15, 22-38, 2016.
  • Srinivasan, S. and Kohli, P., "System and method for estimation in a multivariate, longitudinal setup," WO2013126724 A3, 2014.
  • P. Kohli and M. Pourahmadi. "Some prediction problems for stationary random fields with quarter-plane past," Journal of Multivariate Analysis, 127, 112-125, 2014.
  • Srinivasan, S. and Kohli, P. "System and method for estimation of missing data in a multivariate, longitudinal setup," US 20130226613 A1, 2013.
  • Garcia, T., Kohli, P. and Pourahmadi, M., "Regressograms and mean-covariance models for incomplete longitudinal data," The American Statistician, 66(2), 85-91, 2012.

Online work:

Kohli recently collaborated with John C Cangelosi '15 and Jill Whitney (Class of 1984) on analyzing the data collected by a survey of young adults on matters related to how parents address the issue of talking with kids about sexuality and values. The major findings based on this collaboration are reported at

Recent talks:

  • RNA-Sequencing, Recent Developments in Bioinformatics, 2017 IISA Conference, Hyderabad, India, December 2017.
  • Modeling Dependence in Multivariate Longitudinal Studies, Faculty at Work, Connecticut College, Conn., November 2016.
  • Covariance Modeling of Multivariate Longitudinal Data with Application in Clinical Trials, Celebrating Statistical Innovation and Impact in a World of Big & Small Data, IISA, December 20-24, 2015.
  • Clustering Time Series: A PSLEX-Based Approach at 24th ICSA/Graybill Joint Conference, Fort Collins, Colorado, June 14-17, 2015.
  • Technology: An Indispensable Tool for Teaching Statistics in the 21st Century, 27th International Conference on Technology in Collegiate Mathematics, Las Vegas, Nevada, March 12-15, 2015.
  • Time Series Clustering, International Conference on Advances in Interdisciplinary Statistics and Combinatorics, October 10-12, 2014.
  • Prediction of Stationary Random Fields with Quarter-Plane Past: A Time-Series Approach, International Society of Nonparametric Statistics Conference, Spain, June 12-16, 2014.
  • Prediction of Stationary Random Fields with Quarter-Plane Past: A Time-Series Approach, International Society of Nonparametric Statistics Conference, Spain, June 12-16, 2014.
  • Restricted Linear Covariance Models for Multivariate Longitudinal Data, NRC Statistics and Probability, Harvard University, July 2014.
  • Clustering Financial Time Series, International Symposium on Business and Industrial Statistics, Duke University, June 9-11, 2014.

Workshops Conducted

  • Introductory R Workshop for Applications in Life Sciences, Quantitative Life Sciences Program, Connecticut College, July 6-7, 2015.
  • Recent advances in statistical techniques for researchers in Biology, Botany and Neuro-science Quantitative Life Sciences Program, Connecticut College, January 5-7, 2014.

View my CV

Visit the department of mathematics and statistics website.

Majoring in Mathematics.

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Mailing Address

Priya Kohli
Connecticut College
270 Mohegan Ave.
New London, CT 06320


312 Fanning Hall