Faculty

Dr. Adrian S. Barb, associate professor of information science, teaches databases, data mining, and big data courses. He has worked as a database programmer analyst as well as a web developer at University of Missouri. His research interests include data mining, knowledge discovery in databases, knowledge representation and exchange in content-based retrieval systems, semantic modeling and retrieval, conceptual change, ontology integration, and expert-in-the-loop knowledge generation and exchange.

Dr. Nathaniel Bastian is an instructor of supply chain and information systems. His expertise lies in the discovery and translation of data-driven, actionable insights into effective decisions using mathematics, statistics, engineering, economics, and computational science to develop decision-support models for descriptive, predictive, and prescriptive analytics. His teaching interests lie in the areas of decision analytics, data science, and applied econometrics. His research interests lie in the areas of multiple objective optimization and sequential decision-making under uncertainty.

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. Terry P. Harrison, professor of supply chain and information systems and Earl P. Strong Executive Education Professor in Business, teaches Prescriptive Analytics for Business. He is a Fellow and past president of the Institute for Operations Research and the Management Sciences (INFORMS) and has served as a member of the INFORMS Analytics Certification Board. He has teaching and research interests in supply chain management and modeling, large-scale production and distribution systems, decision support systems, applied optimization, and the management of renewable natural resources.

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. 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. Dusan Ramljak, assistant teaching professor of information science, teaches courses on information science, data science, storage systems, and emerging technologies. He has been applying data science on storage systems in NSF IUCRC projects with HPE, Dell, Huawei, and other companies and has more than 20 years of system administration experience facilitating business and research in the U.S., Portugal, and Serbia. His research interests include solving challenging storage systems, provenance, and caching problems, and developing and integrating distributed and parallel data mining and statistical learning technology for an efficient knowledge discovery at large sequence and temporal databases.

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. Rashmi Sharma, assistant clinical professor of supply chain and information systems and managing director of the Laboratory for Economics, Management, and Auctions, will teach Predictive Analytics (BAN 840). Before coming to Penn State, she worked in the software industry and managed the development of business intelligence solutions for manufacturing. She has teaching and research interests in supply chain management, management science, business analytics, incentive design, and workforce management.

Dr. Chris Solo, clinical assistant professor of supply chain and analytics, teaches Business Strategies for Data Analytics (BAN 530). Dr. Solo previously served more than 21 years in the U.S. Air Force, where he held leadership positions in the intelligence, acquisition program management, and operations research analyst career fields. During this time, he also served on the faculty at the U.S. Air Force Academy, teaching a variety of quantitative analysis courses. He is an active member of the Institute for Operations Research and the Management Sciences (INFORMS).

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. Serkan Yilmaz works for Royal Philips HealthCare as a senior manager of global risk management, data analytics, and trending for the Philips corporate global risk management and post-market surveillance analytics team, which drives and executes the Philips global business transformation journey in the risk management, complaint handling, and post-market surveillance processes. He reports Philips Key Performance Indicators (KPIs) to the senior executive team and provides insights using data analytics and problem solution techniques. He leads and provides coaching to business teams on medical device risk profiles, risk management, post-market surveillance, data analytics, statistical and trending methods, data insights, and problem solutions. Dr. Yilmaz brings 17 years of industry, engineering, research, and teaching experience with a diverse background in the following areas: manufacturing engineering, medical device industry, quality engineering, regulatory and compliance, medical device risk management, probabilistic risk assessment and reliability, Lean Six Sigma (LSS) and Design for Six Sigma (DFSS), Design for Reliability (DFR), data analytics and trending, risk quantification, Measurement System Analysis (MSA), kaizen, uncertainty propagation, crystal ball forecasting, manufacturing process capabilities, Statistical Process Control (SPC), Design of Experiment (DOE), new product introduction, software and method development and validation, optimization methods, adjunct faculty six sigma, and nuclear engineering. Dr. Yilmaz teaches BAN 830: Descriptive Analytics for Business.