Dr. Bennett brings over 30 years of research experience in artificial intelligence, machine learning, and their applications to problems in health, science, and industry. Her research specialty is working with people with problems and data and then developing novel machine learning and AI models and work flows to solve their problems. She serves as Associate Director of Institute of Data Exploration and Applications (IDEA). Her role is to both lead major data science research projects, develop and lead teams for new research projects, and create data science research education programs. Her work with industry includes projects with GE (PI) and Global Foundries (co-Pi). She have been PI or Co-Pi on many data science research projects funded by GE (PI), Global Foundries (co-PI), Albany Capital District Physicians Health Plan (HMO, PI), IBM (co-PI), United Health Foundation/OPTUM Labs (PI), HBI Solutions (Healthcare Data Science, PI), Albany Medical Center (Hospital, PI), Bill and Melinda Gates Foundation (co-PI), NIH (PI and co-PI) and NSF (PI and co-PI). She has worked with electronic medical records and public health data to develop solutions to problems such as Treatment Effect Estimation, Emergency Department Readmission, Critical Care Management, and High Cost Medicare Patients. She works in emerging research areas such as health equity, ML fairness, and synthetic health data. She has been program chair and area chair, PC member and/or organizer for conferences in machine learning, data mining, and operations research including KDD, AAAI, Intl. Conf. on Continuous Optimization, International Conference on Machine Learning, NIPS, IEEE Conf. on Data Mining, COLT, INFORMS, and SIAM ICDM. She has over 130 research publications. She has been a plenary speaker at major conferences including AAAI, IJCNN, and IEEE BIBM. She founded and directs the Data INCITE Lab which does novel applied data analytics research. Data INCITE fully integrates education and research. Over 250 undergrad students have done research in Lab on real problems for actual clients resulting in publications and applications. Recent awards from her group include “MortalityMinder” https://mortalityminder.idea.rpi.edu which was a winner in the AHRQ Visualization of Social Determinants of Health Contest, 2019 and Best Student paper at ACM BCB 2021.

Lirong Xia is an assistant professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). Prior to joining RPI in 2013, he was a CRCS fellow and NSF CI Fellow at the Center for Research on Computation and Society at Harvard University. He received his PhD in Computer Science and MA in Economics from Duke University. His research focuses on the intersection of computer science and microeconomics, in particular computational social choice, game theory, mechanism design, and prediction markets. He is an associate editor of Mathematical Social Sciences and is on the editorial board of Journal of Artificial Intelligence Research. He is the recipient of an NSF CAREER award, a Simons-Berkeley Research Fellowship, and was named as one of "AI's 10 to watch 2015" by IEEE Intelligent Systems.

Liu Liu has been with the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI) as an assistant professor since July 2022. He has a Ph.D. in Computer Science at University of California, Santa Barbara. His research interests reside in the intersection between computer architecture and machine learning, towards high-performance, energy-efficient, and robust machine intelligence. He leads the research on Elastic Processing & Hardware Architectures, with publications in top-tier conferences on machine learning and computer architecture (e.g., ICML, ICLR, MICRO, and ASPLOS). He earned an M.S. in Electrical and Computer Engineering from UC Santa Barbara in 2015. He is a recipient of the Peter J Frenkel Fellowship from the Institute of Energy Efficiency at UCSB.

Lydia Manikonda is an Assistant Professor in the Lally School of Management who is also affiliated with the AIRC at RPI. Her passion is to build intelligent decision-making models that are capable of learning and reasoning. These models are built to address problems in the areas of business, public health and Technology. Her multidisciplinary research aims at using alternate sources of information such as social media platforms, online discussion forums, news articles, etc. to build models for analysis and decision-making. So far, her research work has received several media mentions, a best reviewer award at ICWSM 2016 and an outstanding demonstration award at ICAPS 2014. Lydia received her PhD in Computer Science from Arizona State University in 2019. More information about her research and publications can be found here: website or Google Scholar

Dr. Magdon-Ismail has been a Professor of Computer Science since 2000. After degrees at Yale and Caltech, Dr. Magdon-Ismail was a research scholar at Caltech before joining Rensselaer as Assistant Professor of Computer Science. His interests are in decision making from data in complex systems, including machine learning, computational finance and social and communication networks. He enjoys poker, bridge, squash, tennis and badminton. For a full bio and more details, please visit his web page.

Marjorie McShane develops cognitive models of intelligent agents that can collaborate with people in task-oriented, dialog applications. While at heart a linguist, she is particularly interested in the integration of functionalities that are often treated in isolation, such as physiological simulation, emotion modeling, and the many aspects of cognition. 

One aspect of cognition to which she has devoted particular attention is natural language processing, approached from a cross-linguistic perspective and with the goal of producing machine-tractable descriptions that can support sophisticated conversational agents. She has also worked extensively on cognitive modeling in the medical domain, to support the configuration of intelligent agents playing the roles of virtual patients and tutors in training applications such as the Maryland Virtual Patient system.

McShane has (co-)authored three books: Linguistics for the Age of AI (MIT Press, 2021), A Theory of Ellipsis (Oxford University Press, 2005), and An Innovative, Practical Approach to Polish Inflection (Lincom Europa, 2003). She has published extensively on linguistics, natural language processing, cognitive modeling, and knowledge representation.

Mei Si is primarily interested in is artificial intelligence and its application in virtual and mixed realities. In particular, her research concentrates on computer-aided interactive narratives, embodied conversational agents and pervasive user interface, elements that make virtual environments more engaging and effective. Si has been using her research to develop virtual environments and intelligent conversational agents for serious games. In one example of her work, Si helped to develop the Tactical Language Training System, a large-scale (six to twelve scenes each for three languages) award-winning project funded by the U.S. military for rapid language and culture training. The system has been used by thousands of military personnel. “Computer-aided interactive narrative is a new form of media that allows the user to play a role in a story and interact with other characters controlled by an automated system. The user’s choices of actions affect the development of the story,” said Si. Narrative itself is a central part of the human experience. Its power to shape people's minds and affect people's behavior has been recognized throughout recorded history. The support for user interactivity distinguishes interactive narrative from other narrative forms. By allowing the user to interact, the experience is richer and potentially more engaging. Moreover, interactivity can promote intrinsic motivation in learning, and support learning in context and replay. Therefore, interactive narrative can be potentially a more effective media than traditional narrative.” Si regularly presents and publishes her work. She has recently presented on “Activating Narcissus: Cognitive and Affective Systems Transformed Through "Serious" Game Play” at the International Conference on the Philosophy of Computer Games; “Foreign language learning in immersive virtual environments” at the IS&T/SPIE Electronic Imaging conference, and “Modeling Rich Characters in Interactive Narrative Games” and GAMEON-ASIA. Her recently published work includes “D.V. Modeling Appraisal in Theory of Mind Reasoning” in the Journal of Agents and Multi-Agent Systems, and book chapters “ Virtual Interactive Interventions for Reducing Risky Sex: Adaptations, Integrations, and Innovations” in  Interactive Health Communication Technologies: Promising Strategies for Health Behavior, and “Modeling Theory of Mind and Cognitive Appraisal with Decision-Theoretic Agents” in Social emotions in nature and artifact: Emotions in human and human-computer interaction.

Mohammed J. Zaki is a Professor and Department Head of Computer Science at RPI. He received his Ph.D. degree in computer science from the University of Rochester in 1998. His research interests focus novel data mining and machine learning techniques, particularly for learning from graph structured and textual data, with applications in bioinformatics, personal health and financial analytics. He has around 300 publications (and 6 patents), including the Data Mining and Machine Learning textbook (2nd Edition, Cambridge University Press, 2020). He is the founding co-chair for the BIOKDD series of workshops. He is currently an associate editor for Data Mining and Knowledge Discovery, and he has also served as Area Editor for Statistical Analysis and Data Mining, and as Associate Editor for ACM Transactions on Knowledge Discovery from Data, and Social Networks and Mining. He was the program co-chair for SDM'08, SIGKDD'09, PAKDD'10, BIBM'11, CIKM'12, ICDM'12, IEEE BigData'15, and CIKM'18, and he recently co-chaired CIKM'22. He is currently serving on the Board of Directors for ACM SIGKDD. He was a recipient of the National Science Foundation CAREER Award and the Department of Energy Early Career Principal Investigator Award, as well as HP Innovation Research Award, and Google Faculty Research Award. His research is supported in part by NSF, DARPA, NIH, DOE, IBM, Google, HP, and Nvidia. He is a Fellow of the IEEE, a Fellow of the ACM, and a Fellow of the AAAS.

Oshani Seneviratne is an Assistant Professor in Computer Science. She was previously the Director of Health Data Research at the Rensselaer Institute for Data Exploration and Applications. Oshani obtained her S.M. and Ph.D. degrees in Computer Science from the Massachusetts Institute of Technology (MIT) under the supervision of Sir Tim Berners-Lee, the inventor of the World Wide Web. Before Rensselaer, Oshani worked at Oracle, specializing in knowledge representation, provenance, and healthcare-related research. Her research interests span decentralized systems (web and blockchain), knowledge graphs, artificial intelligence, and health informatics. 

Dr. Lin joined the Rensselaer faculty as an Assistant Professor of Economics in Fall 2017. Her research interests include topics in Health Economics, Industrial Organization, and Applied Econometrics.  She currently focuses on issues related to health information technology (IT) adoption, as well as how health IT impacts hospital financial and quality performance.