Dr. Kylie Anglin is an Assistant Professor of Research Methods, Measurement, and Evaluation in the Neag School of Education. Dr. Anglin's research develops methods for using natural language processing (NLP) to understand educational and social processes. She also develops methods for improving causal inference and replication in the social sciences. Her teaching focuses on the validity of inferences drawn from statistics and machine learning.

Coming soon....

Dr. Kun Chen is an Associate Professor in the Department of Statistics at the University of Connecticut (UConn) and a Research Fellow at the Center for Population Health at the UConn Health Center. Kun’s research focuses on multivariate statistical learning, statistical machine learning, and healthcare analytics with large-scale data. He has extensive interdisciplinary research experience in a variety of fields including ecology, biology, agriculture, and public health. Currently, Kun and his team are funded by National Institutes of Health for improving suicide risk identification and prediction in diverse clinical settings using data fusion. He has also been funded by NSF on integrative statistical learning and data heterogeneity. Kun has developed and taught a number of data science related course subjects, including multivariate statistical learning, high-dimensional statistics, data science in action, and statistical computing.

Dr. Jose Cruz is an Associate Professor of Operations and Information Management and Associate Dean for Graduate Programs within UConn’s School of Business. Dr. Cruz’s teaching interests are in Operations Management, Operations Research, Business Analytics, and Project Management and digs deep into business-related aspects such as supply chain management, corporate social responsibility, sustainability, relationships and risk management. With expertise and a fascination for variational inequalities, dynamical systems, network theory, multicriteria decision-making, and optimization, Dr. Cruz specializes in the teaching of predictive modeling and data mining through a business-minded lens.

Dr. Nathan Fiala is an Associate Professor within UConn’s Department of Agriculture and Resource Economics. Focussing on Data Science in terms of food security, the environment and the political economy are just a few areas of interest that drive this ethically-minded instructor and researcher. Dr. Fiala’s research endeavors include dozens of randomized control trials (RCTs) in Africa, Asia and the US concentrating on topics having to do with international development, homelessness, and poverty related issues.

Mia Hawlk has worked in higher education for 15 years. Eight of those years have been in Graduate Business Education. She joined UCONN in 2014. Currently serves as the Executive Director of MBA Programs. In this role she oversees and provides leadership for the Online, Part-time, Full-time and Executive MBA degree programs. Mia enjoys connecting with her students and helping them to find balance in their work, school and home lives.

Mia is actively engaged in the national PMBA community and was appointed to the national PMBA/OMBA advisory board in 2020. In 2021 along with a colleague from Villanova she co-founded the Big East Part-time MBA consortium in an effort to increase knowledge share among peer institutions. She is a member of many organizations that promote female leadership, and recently has been speaking about the challenge of balancing motherhood during COVID.

Mia holds a Doctorate in Educational Leadership and also teaches technical communications for the MS in Business Analytics and Project Management program at UCONN.

Dr. Joseph Johnson is an Associate Professor In Residence and Associate Program Director in Computing within UConn’s Computer Science and Engineering Department. Dr. Johnson’s research passions focus on artificial intelligence (AI), machine learning (ML), data science, and natural language processing. A graduate of UConn and Rensselaer Polytechnic Institute, Joe has been teaching data mining and machine learning courses for over two decades, covering topics such as regression techniques (linear, non-linear methods), classification techniques (logistic regression, decision trees, boosting, bagging, random forests, gradient boosting, xgboost, gradient descent (batch and stochastic), k-nearest neighbors, support vector machines), unsupervised learning techniques (k-means, dbscan), and anomaly detection techniques.

Coming soon....

Prasanthi Lingamallu is an Analytics thought leader experienced in driving business strategy through data driven insights. She has worked in Data and Analytics for over 19 years in the Banking, Finance and Insurance domains with an expertise in customer segmentation and marketing strategies. Prasanthi is passionate about creating a customer centric data culture, innovation, and visual storytelling. She is a FIRST Robotics coach and an avid champion of Diversity, Equity & Inclusion. Prasanthi holds a Masters in ECE from University of Arizona and a Masters in Business Analytics and Project Management from UCONN.

Betsy McCoach is a professor in the Neag School’s RMME program. She has extensive experience in structural equation modeling, longitudinal data analysis, hierarchical linear modeling, instrument design, and factor analysis. In addition, she is the current Director of the Data Analysis Training Institute of Connecticut (DATIC), where she teaches summer workshops in longitudinal modeling, structural equation modeling, multilevel modeling, and she is the founder and conference chair of the Modern Modeling Methods conference, held at UConn every May.

UConn’s Alexandra Paxton is an Associate Professor within the Department of Psychological Sciences. Focused on communication and interaction within data-rich environments, Dr. Paxton is further intrigued by the development of methods to quantify variables surrounding social interaction. With an emphasis on data science and big data, Paxton is an expert in the analysis of naturally occurring data with an underpinning emphasis on data ethics. Via a big data perspective, analysis of social dynamics is a key component within Alex’s many competencies, research and publications.

Dr. Sanguthevar Rajasekaran a professor and heads the UConn’s Computer Science and Engineering Department. With a focus on algorithms, big data analytics, bioinformatics, materials genomics and high performance computing, Dr. Rajasekaran is passionate about teaching and making the world a better place through Big Data Science, Artificial Intelligence (AI), Machine Learning (ML), Bioinfomatics, Computational Biology and Computational Science. Sanguthevar has lead and participated in dozens of research endeavors and publications, including funded research initiatives focused on Statistical Computing Approaches for the Analysis of Multiple Time Course Data, as well as, Big DataTools: From Bioinformatics to Materials Genomics.

Dr. Nalini Ravishanker is a professor in the University of Connecticut’s Department of Statistics. With a passion for teaching and developing novel statistical methods, Dr. Ravishanker’s research interests include time series analysis, times-to-events analysis, Bayesian dynamic modeling, signal processing, and predictive inference. With a long track record of research and publications concerning statistics and their application to our world, Dr. Ravishanker has focused much of her interdisciplinary research on applications in the domains of actuarial science, finance, marine sciences, marketing, and transportation engineering. Her recent funded research initiatives are focused on Statistical Computing Approaches for the Analysis of Multiple Time Course Data and Safety Issues in Civil and Transportation Engineering. She has served as the undergraduate program direction in her department, as President of the International Society for Business and Industrial Statistics (ISBIS) and co-editor-in-chief of the International Statistical Review.

Matt Samelson is a data science leader with several decades of experience in predictive modeling, quantitative research, consulting, and management. He is based in Stamford, Connecticut, and possesses deep domain knowledge in private equity, banking, brokerage, and asset management. Matthew is passionate about "hands-on" problem-solving using predictive modeling and data mining. He is skilled in Python, R, and SQL.

As Senior Director, Conferences Business Analytics at Gartner Inc., Matt leads a team that drives business growth by extracting value from data. He uses predictive modeling and data analytics to improve operational efficiencies, extract valuable insights from conference data, and spearhead a focus on identifying key business drivers.

As an Adjunct Lecturer at the University of Connecticut, Matt teaches and develops curricula for the MS in Business Analytics and Project Management (BAPM) and the MS in Data Science programs. He teaches courses on predictive modeling with Python, business process modeling, data management, SQL, and the MS in Data Science capstone project.

In previous positions, including as a Principal Data Scientist & Senior Machine Learning Engineer at venture capital firm OurCrowd, Director of Quantitative Analytics, Data Science, and Development at Outvest Capital, and Director of Quantitative Analytics and Business Intelligence at Woodbine Associates, Matt has demonstrated an ability to develop and implement strategies that have driven productivity, profitability, and growth.

Matt holds an MBA in Finance from the University of Chicago and a Bachelor of Arts in Economics from Columbia University. He is a former Lieutenant, US Naval Reserve where he held a Top Secret Clearance.

Matt lives in Stamford, Connecticut with his wife Diane, son Nate, and daughter Julia.

He enjoys sailing, biking, and geocaching.

UConn’s Ramesh Shankar is an Associate Professor of Operations and Information Management within the School of Business. Dr. Shankar’s research interests include Big Data analytics, social media analytics, and the strategic analysis of durable goods, digital goods, music and video games. With a passion for leading and developing courses in IT Strategy, Big Data Analytics, Business Information Systems and Database Management, Ramesh is also a renowned researcher and practitioner within the field.

Dr. Neil Spencer is an Assistant Professor in the Department of Statistics. His research focuses on the development of robust, principled, and computationally efficient techniques for learning from structured data. He has expertise working with networks, relational data, images, spatial data, and temporal data in applications that include forensic science, social science, neuroscience, and medicine. He teaches the course Computing for Statistical Data Science for the MS in Data Science program.

Jeremy Teitelbaum, the Director of the MS in Data Science,  is a Professor of Mathematics at UConn.  He has carried out research in algebraic number theory, on computational problems in pure mathematics,  and, more recently, on connections between latent class models and algebraic geometry.  Over the past several years he has developed curricula on the Mathematics of Machine Learning at the graduate and undergraduate level.  From 2008 to 2017 he served as Dean of the College of Liberal Arts and Sciences at UConn, and in 2017-2018 as Interim Provost of the University.

Dr. Charles Towe is an Associate Professor within UConn’s Department of Agricultural and Resource Economics. With a lifelong commitment to the study of environmental economics, land use and policy evaluation, to say Dr Towe is passionate about the environment and its preservation would be an understatement. As an Applied Econometrician and Environmental Economist, Charles has focused much of his career on data collection, forecast programming and data analysis in terms of agriculture, the environment and policy analysis. Charles has participated in many research projects and publications, including submission and contribution to articles such as Not my problem: Growth spillovers from uncoordinated land use policy and Improving researcher access to USDA’s Agricultural Resource Management Survey.

Dr. David Wanik is an Assistant Professor in Residence at UConn’s Department of Operations and Information Management within the School of Business. At the intersection of natural hazards, business analytics, remote sensing and big data analytics, Dr. Wanik is an expert in the application of data analysis tools towards the understanding of risk for large businesses and the population. David teaches graduate courses in statistics, optimization, data science and deep learning and his career has been deeply rooted in predictive analytics and geospatial data processing within a natural hazards - risk assessment framework. David’s research interests include the meshing of business analytics paired with sustainability and resilience.

Dr. Jill Wegrzyn is a Plant Computational Genomics Associate Professor within UConn’s Department of Ecology and Evolutionary Biology. With skills and expertise in bioinformatics, computational biology, genomics, next generation sequencing, gene expression, molecular biology, genetics, genotyping and genetic analysis, Dr. Wegrzyn’s knowledge is vastly applicable to her active role within the University’s Plant Computational Genomics Laboratory. Having taught, designed and launched a number of courses within the field, Jill is also a researcher and practitioner studying breakthrough data science findings within related spheres of practice, such as Forest Tree Genomics.

Dr. Chandi Witharana is an Assistant Professor in Residence in the Department of Natural Resources and the Environment at UConn’s College of Agriculture, Health and Natural Resources (CAHNR). He is the Director of UConn’s Remote Sensing and Geospatial Data Analytics Online Graduate Certificate. Dr. Witharana is also an affiliated faculty of Eversource Energy Center and the Institute for the Brain and Cognitive Sciences at UConn. He is a member of American Society for Photogrammetry and Remote Sensing and the American Geophysical Union. Dr. Witharana is the Director of ConnecticutView at UConn. He earned a PhD in Remote Sensing and an MS in GIScience at the University of Connecticut, and a BSc in Geology at the University of Peradeniya, Sri Lanka. Prior to joining the UConn faculty, he was a Post-doctoral Research Associate at the SUNY Stony Brook. Dr. Witharana was a Geospatial Analyst at the United Nations Office for the Coordination of Humanitarian Affairs. He conducts interdisciplinary remote sensing research speaking to the transformational uses of earth observation technology in environmental, industrial, and humanitarian applications.