Dr. Mohamad Darayi, assistant professor of systems engineering, focuses his principal research and key publications on infrastructure network resilience and simulation modeling applications in health care, manufacturing, and supply chain management. He teaches courses in system simulation, risk analysis, network modeling, and data analytics.
Dr. Chelsea Hammond is an assistant clinical professor of marketing and program director of the online marketing analytics certificate. Prior to joining the Penn State Smeal College of Business, she spent more than a decade in the market research and marketing analytics industry, where she helped the world's most iconic and well-known brands leverage data to drive business success.
Dr. Ashkan Negahban is an associate professor of engineering management. Prior to joining Penn State, he was an instructor at Auburn University, where he taught courses in simulation, probability theory, and statistics. His research interests include the application of different types of simulation (discrete event, agent-based, and Monte Carlo) in design and operation of complex systems. He has developed several e-learning modules that have received worldwide publicity and are used by faculty from leading institutions around the world.
Dr. Colin Neill is a professor of software engineering and systems engineering. He teaches many courses in software and systems engineering and project management. He is the author of more than 80 articles on the development and evolution of complex software and systems and their management and governance. Dr. Neill is a senior member of the IEEE and a member of INCOSE, and he serves as associate editor-in-chief of Innovations in Systems and Software Engineering.
Dr. J. Andrew Petersen is an associate professor of marketing in the Smeal College of Business and program director of the online marketing analytics and insights master’s degree. His research interests include measuring and maximizing customer/donor lifetime value (CLV/DLV) and customer/donor equity, managing customer product return behavior, measuring the value of word-of-mouth, selling and sales management, and linking marketing metrics to financial performance. His research has been published in many top academic journals, including Journal of Marketing, Journal of Marketing Research, Harvard Business Review, MIT Sloan Management Review, and The Wall Street Journal.
Dr. Robin G. Qiu is a professor of information science at Penn State. He teaches courses on data analytics, information science, software engineering, and cyber security. Dr. Qiu's research includes smart service systems, IoT, big data, data/business analytics, information systems and integration, supply chain and industrial systems, and analytics. He served as the editor-in-chief of INFORMS Service Science. He is an associate editor of IEEE Transactions on Systems, Man, and Cybernetics and IEEE Transactions on Industrial Informatics, and has more than 160 publications.
Dr. Raghvinder S. Sangwan is a professor of software engineering. His teaching and research involve analysis, design, and development of software-intensive systems and their architecture, and automatic/semi-automatic approaches to assessment of their design and code complexity. He actively consults for Siemens Corporate Technology in Princeton, New Jersey, and holds a visiting scientist appointment at the Software Engineering Institute at Carnegie Mellon University in Pittsburgh, Pennsylvania. He is a senior member of the IEEE and ACM.
Dr. Satish Srinivasan is an associate professor of information science in the engineering division at Penn State Great Valley. He teaches courses related to database design, data mining, data collection and cleaning, design and implementation of predictive analytics system, network and web securities, and business process management. His research interests include social network analysis, data mining, machine learning, big data and predictive analytics, and bioinformatics.
Dr. Xi Zhang, assistant professor of data analytics, teaches data mining and foundations of predictive analytics.
Ready to Learn More?
Get the resources you need to make informed decisions about your education. Request information on this program and other programs of interest by completing this form.