IT Analytics and Operations Seminars

Rohit Aggarwal's research explores how AI technologies and human expertise can mutually enhance each other in organizational settings. Professor Aggarwal's work can be categorized under two main themes:

Theme 1: AI Augmenting Human Decision-Making

In this theme, Professor Aggarwal's research explores AI’s role in enhancing learning, skill development, and productivity within organizations. Through field experiments, he examines how AI tools improve productivity and support skill acquisition for new learners across tasks of varying complexity. He also studies the differential effects of AI usage patterns on experienced and inexperienced users, revealing how AI enhances decision-making while highlighting risks such as over-reliance.

By employing a Partially Observable Markov Decision Process (POMDP) model, Professor Aggarwal gains deeper insights into how prolonged AI use impacts skill development and potential inertia against AI adoption, aiming to balance immediate productivity gains with sustainable skill enhancement. Additionally, to address global challenges, his work explores AI’s potential to bridge linguistic divides by proposing new approaches for non-native English speakers using AI-powered coding tools.

Theme 2: Humans Augmenting AI Decision-Making

This theme focuses on improving AI systems by embedding human insights to enhance their performance and transparency. A core aspect is integrating tacit knowledge and inferred latent themes into AI models, which enhances AI decision-making by aligning models more closely with real-world complexities. By employing Bayesian modeling and AI-enabled extraction & aggregation techniques, Professor Aggarwal's research aims to make AI systems more robust and contextually aware, as validated in various field experiments.

Another key component involves creating explainable AI models, particularly for recruitment, where transparent decision-making is crucial. By utilizing a Hierarchical Attention Mechanism, he develops systems that highlight human-relevant qualifications, fostering trust in AI’s role in decision processes.

Furthermore, Professor Aggarwal addresses the challenges of Generative AI (GenAI) by proposing hybrid frameworks that incorporate human guidance to enhance AI system planning, adaptability, and effectiveness. This theme ultimately emphasizes how human expertise can shape and augment AI, making systems more reliable while preserving necessary oversight and human control.

Panos Ipeirotis is a Professor and George A. Kellner Faculty Fellow at the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business of New York University. His research focuses in the areas of crowdsourcing, machine learning, web data management, and social media analytics. He is widely regarded as the world's leading expert in building human-machine loop systems, that integrate human and machine intelligence to generate outcomes that are better than what humans alone or machines alone can achieve. He has also received more than ten “Best Paper” awards and nominations, and a CAREER award from the National Science Foundation. For his contributions in the field of social media, user-generated content, and crowdsourcing, he received the 2015 Lagrange Prize in Complex Systems.

Blake McShane joined the marketing faculty at Northwestern's Kellogg School of Management in 2010 as a Donald P. Jacobs Scholar. He has developed and applied statistical methodology to topics ranging from optimizing internet ad-serving algorithms to forecasting home runs in baseball. His specific research interests include hierarchical/multilevel modeling, statistical learning, and generalized Markov models. More generally, he seeks to develop statistical methods to accommodate the rich and varied data structures encountered in business problems and to use these methods to glean insight about individual behavior so as to test and supplement existing theories. Blake earned his PhD and MA in Statistics, MA and BA in Mathematics, and BS in Economics from the University of Pennsylvania.

Tinglong Dai is the Bernard T. Ferrari Professor at the Johns Hopkins Carey Business School, specializing in Operations Management and Business Analytics. He holds a joint faculty appointment at the Johns Hopkins School of Nursing. He is a member of the Johns Hopkins University Council and serves on the leadership team of the Hopkins Business of Health Initiative. He also co-leads the University’s Bloomberg Distinguished Professorship Cluster on Global Advances in Medical Artificial Intelligence. As a co-chair of the Johns Hopkins Workgroup on AI and Healthcare, his current work focuses on integrating AI into clinical workflows and improving productivity, access, and equity in healthcare delivery. He joined Carey in 2013 after receiving a PhD in Operations Management/Robotics from Carnegie Mellon University. As a renowned expert in healthcare analytics and global supply chains, Professor Dai has been quoted hundreds of times in the media, including Associated Press, Bloomberg, CNN, Fortune, New York Times, NPR, USA Today, Wall Street Journal, and Washington Post, and has appeared on national and international TV such as BBC News, CNBC, PBS NewsHour, Sky News, and ZDF. In 2021, he was named as one of the World's Best 40 Under 40 Business School Professors by Poets & Quants.

Professor Dai's research interests span across healthcare, human-AI interaction, global supply chains, and marketing-operations interfaces. His work has been published in leading journals such as Management Science, M&SOM, Marketing Science, Operations Research, Journal of Marketing Research, NEJM AI, and npj Digital Medicine, and has been recognized by Johns Hopkins Discovery Award, INFORMS Public Sector Operations Research Best Paper Award, POMS Best Healthcare Paper Award, and Wickham Skinner Early Career Award (Runner-Up). He is an Associate Editor of Management Science, M&SOM, npj Digital Medicine, Service Science, Health Care Management Science, and Naval Research Logistics, a member of the Editorial Board of Marketing Science, and a Senior Editor of INFORMS Journal on Data Science and Production and Operations Management. In 2023, he was elected as Vice President of Marketing, Communications, and Outreach for INFORMS, the world's largest professional association for decision and data sciences. He co-chairs the Johns Hopkins Symposium on Healthcare Operations and co-edits the Handbook of Healthcare Analytics: Theoretical Minimum for Conducting 21st Century Research on Healthcare Operations, published by John Wiley & Sons in 2018.

John R. Birge studies mathematical modeling of systems under uncertainty, especially for maximizing operational and financial goals using the methodologies of stochastic programming and large-scale optimization. He was first drawn to this area by a need to use mathematics in a useful and practical way. "My research has shown how special problem structure can allow for efficient solution of complex problems of decision making under uncertainty," Birge explains. This research has been supported by the National Science Foundation, the Ford Motor Company, General Motors Corporation, the National Institute of Justice, the Office of Naval Research, the Electric Power Research Institute, and Volkswagen of America. He has published widely and is the recipient of the Best Paper Award from the Japan Society for Industrial and Applied Mathematics, the Institute for Operations Research and the Management Sciences Fellows Award, the Institute of Industrial Engineers Medallion Award and was elected to the National Academy of Engineering.

A former dean of the Robert R. McCormick School of Engineering and Applied Sciences at Northwestern University, he has worked as a consultant for a variety of firms including the University of Michigan Hospitals, Deutsche Bank, Allstate Insurance Company, and Morgan Stanley, and he uses cases from these experiences in his teaching.

Birge earned a bachelor's degree in mathematics from Princeton University in 1977 and a master's degree and a PhD in operations research from Stanford University in 1979 and 1980, respectively. He joined the Chicago Booth faculty in 2004.

He is a member of the Institute for Operations Research and the Management Sciences, the Mathematical Programming Society, the Mathematical Association of America, and Sigma Xi. He also speaks French, Russian, German, and English.

Dr. Brian T. Tomlin is the William and Josephine Buchanan Professor of Management and Professor of Business Administration at the Tuck School of Business at Dartmouth. He previously served as Senior Associate Dean for Faculty and Research and currently serves as Area Coordinator for Operations and Management Science.

Tomlin's research interests center on operations strategy, supply chain management, and resilience in operations. His work has appeared in top-tier journals such as Management Science, Operations Research, Manufacturing & Service Operations Management, and Production and Operations Management. He has served as Department Editor for Manufacturing & Service Operations Management and on the editorial boards of leading academic journals in the field. His research has been recognized with multiple awards and supported by the National Science Foundation. He earned his PhD in Operations Management from the MIT Sloan School of Management.

Material Boundaries, Scalability, and Multifinality in Digital Sustainability: Field Lessons and Future Pathways

This talk reflects on insights from a longstanding research program exploring the role of
digital technologies in addressing human-induced climate change. Drawing on studies of
organizational sustainability transformations, digitally enabled behavioral change, and
efforts to make technology use itself more sustainable, I will synthesize key conceptual
challenges in advancing insights on digital sustainability. I will also outline possible pathways
toward a more nuanced understanding of how digital solutions can support sustainable
behavior and development in practice.

Learn More About Dr. Jan Recker

Dr. Jan Recker is the Nucleus Professor for Information Systems and Digital Innovation at the University of Hamburg. He is also Alexander-von-Humboldt Fellow, AIS Fellow, and Adjunct Professor at the QUT Business School, Australia. He previously held positions including the Woolworths Chair in Retail Innovation at Queensland University of Technology and Chaired Professor for Information Systems at the University of Cologne.

His research explores how organizations navigate the opportunities and challenges of digitalization, artificial intelligence, and sustainability, working with firms such as Apple, SAP, Volkswagen, and Lufthansa, and others. He has published extensively in leading journals including MIS Quarterly, Academy of Management Review, Entrepreneurship Theory and Practice, and Organization Science, and his work has been featured in Harvard Business Review, MIT Sloan Management Review, and other outlets. He is also the author of widely used textbooks on scientific research and data analysis, used in more than 600 institutions worldwide. Dr. Recker currently serves as Senior Editor for the MIS Quarterly, the Journal of the Association for Information Systems, and the European Journal of Information Systems. Previously, he was Editor-in-Chief of the Communications of the Association for Information Systems.

Dr. Recker is recognized among the top 2% of scientists globally and is one of only three scholars listed in Germany’s top 100 researcher rankings for both business and management as well as computer science. In 2019, he was named Germany’s #1 business researcher under 40 by Wirtschaftswoche. In his spare time, he co-hosts the “this IS research” podcast with Dr. Nick Berente of the University of Notre Dame.