Online Master of Science in Data Science

Dr. Sheets was trained as a physicist but has worked for many years as an applied data scientist, overseeing collaborative, interdisciplinary teams to address a variety of questions. He has published scientific works in biology, entomology, forensics, paleobiology, physics, anthropology and art conservation. He co-authored a leading textbook in geometric morphometrics, a set of methods for carrying out hypothesis testing based on the shapes of non-human organisms. His current work focuses on measuring the performance characteristics of forensic firearms and handwriting examiners, as well as applying data science to the analysis of surface textures on art objects.

Randhir (Randy) Agarwal currently leads the data engineering and data science teams at Samsung Electronics. He has over 25 years in the wireless telecommunications industry, including deploying Green-field wireless networks worldwide and fine-tuning them for optimal performance. Mr. Agarwal holds an M.S. in Data Science from Northwestern with a specialization in analytics and modeling, and a Bachelor of Engineering in Telecommunications from the University of Mumbai.

Torrey is our success coach for the Data Science & Analytics program and is very important to the overall student experience. Throughout your time working towards your degree, Torrey will help address many of the questions you may have outside the classroom. In doing so, Torrey will help free more of your time to concentrate on mastering the content that is so important to your program progression.

Torrey earned her B.A. in Mathematics from the College of the Holy Cross and an M.Ed. in Higher Education from Merrimack College. You will get to engage with Torrey from the time you are admitted until you graduate, allowing you to have a consistent point of contact as you work to achieve your goals.

Michael M. Dupin, Ph.D. is a divisional director of Artificial Intelligence within the Advanced Technology group at Wolters Kluwer. With a diverse background spanning market research, banking, oncology, and physics, Michael specializes in applying data science and machine learning techniques to solve complex business problems, particularly in predictive customer insights, risk forecasting and AI product development. He has led teams of data scientists and analysts and has extensive experience in client-facing roles, collaborating closely with business stakeholders to deliver impactful, data-driven solutions.

Before joining Wolters Kluwer, Michael was the head of data science at C Space, where he founded and led the company’s AI initiatives. With over twenty years of experience in data and statistical analytics, his expertise spans macroeconomic stress testing, risk management, financial modeling, statistical model validation and blood flow modeling in tumors during his tenure as a research fellow at Harvard University.

Michael’s technical skills include proficiency in Python, R, MATLAB and various data visualization and analysis tools. He holds a Ph.D. in Computational Fluid Dynamics from Sheffield Hallam University in England, a MasterTrack in Machine Learning for Analytics from the University of Chicago, and a B.S. in Nuclear Physics from the University of Strasbourg in France. He has published more than 15 peer reviewed papers in the field of physics, medical and market research, as well as acting as a reviewer for more than 20 different academic journals.

Outside of work, he is an avid sailor, editor of a national print sailing magazine, and a full merchant marine captain.

Katherine Geist holds a Ph.D. in Biology with an emphasis in computational evolutionary genomics. Her research ranges from gene expression in social insects to migraine symptom tracking in multiple sclerosis patients, with big data at the core of her research.

Dr. Yamil Guevara is an expert in the fields of artificial intelligence, machine learning, and data science. He holds an MBA and two master’s degrees in artificial intelligence and machine learning as well as economics. He received his first Ph.D. in Organization and Management and is working on his second Ph.D. in Computer Science with an emphasis on artificial intelligence.

In addition to teaching, he has eight years of experience working as a data scientist, developing machine learning models using classical machine learning and deep learning neural network (DNN) algorithms to solve business problems. Dr. Yamil Guevara has authored “How to Increase Online Student Retention Utilizing Machine Learning” (University of Arizona Global Campus Chronicle, 2021), “Dangers of Artificial Intelligence” (Medium, 2022), “The Impact of Artificial Intelligence on Society” (Medium, 2022), and “The Utilization of Artificial Intelligence in the Classroom” (University of Arizona Global Campus Chronicle, 2021).

Chris is a Data Science Lead at Schneider Electric — a multinational power electronics, energy storage, and building management company. He leads work in industrial applications of data science for predictive maintenance, intelligent alarm management, and efficient use of devices. He is deeply interested in robust decision-making through stochastic optimization and interpretation of machine learning models. A long-time member of INFORMS, he has authored several refereed papers and been granted multiple patents.

Chris received a Ph.D. in Industrial Engineering from Georgia Tech and a B.S. in Mathematics from William and Mary. Chris teaches Data Exploration for the Data Science & Analytics program. Chris teaches Visual Data Exploration for the Data Science program.

Jeremiah is a data scientist with nine years of experience in analytics. He is currently an adjunct faculty member in the Data Science & Analytics program. His interests include computer vision, natural language processing, time series analysis, and distributed computing. Jeremiah is fluent in R, Python, VBA, and SQL, and is interested in learning new programming languages.

In his leisure, he spends time with his wife Brooke and son Magnus. Jeremiah holds a B.S. in Financial Analysis from Ball State University, an M.S. in Analytics from Dakota State University, and is pursuing an M.S. in Information Systems and Ph.D. from Dakota State University. Jeremiah teaches R and Python programming in the data science program.

Josh Orenstein is currently the NBA’s senior director of Basketball Strategy & Analytics and senior data scientist. He has over a decade of experience in the sports industry as a data scientist. He analyzed 3D radar data that transformed performance evaluation and development in Major League Baseball. At the NBA, he uses advanced analytics to improve the competitiveness of the on-court product, evaluation of referees, and evaluation of collegiate basketball players. He teachesAdvanced Sports Analytics at Merrimack College and is a Columbia University lecturer.

Dr. Peter Salemi has over 10 years of experience in quantitative research, machine learning, and statistics in both academic and industry settings. As a data scientist at The MITRE Corporation, he works with several federal agencies ranging from the United States Department of Veterans Affairs to the Centers for Medicare & Medicaid Services. Dr. Salemi received his Ph.D. in Operations Research from Northwestern University, an M.S. in Operations Research from the University of California, Berkeley, and a B.S. in Mathematics and B.B.A. in Finance from the University of Massachusetts, Amherst.

His academic research interests lie at the intersection of machine learning and stochastic simulation, and Dr. Salemi’s research has been published in Operations Research, ACM Transactions on Modeling and Computer Simulation, and the Journal of Simulation. Dr. Salemi has also served as the co-chair of the Simulation Optimization track for the 2017, 2019, and 2020 Winter Simulation Conferences, and as a reviewer for several academic journals. His teaching interests are machine learning, data management, and statistics. As an adjunct faculty member at Merrimack College, Dr. Salemi is the instructor for both the Machine Learning and Text and Image Mining courses.

Kathryn Wifvat, originally from Minnesota, earned her B.S. in Mathematical Statistics and a B.A. in Applied Mathematics before completing a Ph.D. in Applied Mathematics at Arizona State University. With experience in data analytics and full-stack web and mobile app development, she is now the founder and CEO of an edtech startup, in addition to being an adjunct faculty member.