Interdisciplinary work is at the core of Susan Smith’s research and educational interests.  Her undergraduate work in Biology serves as a basis for her research in Philosophy of Biology, Philosophy of Race and Biomedical Ethics. Her master’s work at the University of Guelph was completed under the direction of Michael Ruse and focused on the nature of human action with respect to free will and determinism.  At the University at Buffalo, working with Jorge J.E. Garcia, she explored the metaphysical basis of race with a focus on its intersection with healthcare.Dr. Smith’s current work explores the ethical issues related to genetic testing and, specifically, informed consent.  She is also actively investigating the perpetuation of racial disparities in medicine and medical research and solutions for their elimination. Additionally, she continues to explore ethical issues with data privacy and algorithmic bias. Before coming to RPI, Dr. Smith taught at Mercyhurst University, Canisius College, and the University at Buffalo.  She has taught courses in Biomedical Ethics, Research Ethics, Philosophy of Human Nature and Science, Technology and Human Values.  Teaching has been a passion for her since she received her undergraduate degree in Education from the University of Windsor.  Dr. Smith encourages students to critically examine their own beliefs and to attempt to create rational defenses for those beliefs.  Dr. Smith was selected by the graduating class of 2021 as one of four professors at Rensselaer to present a "Last Lecture" as somemone who had a last positive impact on their undergraduate experience. She was the recipient of the 2022 Teaching Excellence Award for the School of Humanities, Arts and Social Sciences at Rensselaer.Prior to her arrival at Rensselaer, Smith was the Director of the Social Science Interdisciplinary Degree Programs at the University at Buffalo and served on the advisory board of the University at Buffalo Genomics, Education and the Microbiome (GEM) Community of Excellence. 

Professor Ravichandran is an associated faculty member in the School of Engineering and a faculty for the IT program in the School of Science. He teaches course in the graduate and doctoral programs at Rensselaer. He periodically teaches some of these courses in top business schools in Asia and Europe and brings a global perspective to his teaching. His long term research interests focuses on digital strategies of firms and the mechanisms through which digitization is transforming firms, markets, supply networks and industries. His research has been funded by grants from the U.S. National Science Foundation and the Ministry of Education, Singapore. He has published extensively in leading scholarly journals in Information Systems (Information Systems Research, Journal of Management Information Systems, MIS Quarterly; European Journal of Information Systems, Information Technology Management), Decision Sciences (Decision Sciences; Logistics Information Systems) Strategic Management (Organization Science), Technology Management (IEEE Transaction on Engineering Management, Journal of High Technology Management Research,) as well as in leading practitioner journals (Communications of the ACM). His research has won several awards including the 1) Best Paper, IT and Healthcare Track, International Conference in Information Systems, 2019; 2) Best Information Systems Publication in 2010 (Association of Information System); 3) Best Published Paper Award, 2010 (Information Systems Research); 4) Best Paper Award, Software Technology Track (HICSS, 2010); 5) Best Paper Award Honorable Mention (IEEE Transactions on Engineering Management, 2007); 6) Best Academic Paper Award (Second Supply Chain Management Symposium, McMaster University, 2004); 7) Best Paper Award (OCIS Division, Academy of Management, 2001). He has served in editorial roles in premier academic journals: as a Senior Editor of MIS Quarterly and as a Department Editor for IEEE Transactions on Engineering Management, as an Associate Editor of both MIS Quarterly and Information Systems Research. Prior to joining Rensselaer, Dr. Ravichandran had extensive business experience having served as a Consultant to the Reliance Group, as the Assistant Director of National Productivity Council, India and as a Production Manager in Flakt AB (now Asea Brown Boweri). He has also been a successful entrepreneur; he started, built and ran an IT services firm.

Tianyi Chen has been with Rensselaer Polytechnic Institute (RPI) as an assistant professor since August 2019. Dr. Chen is the inaugural recipient of the IEEE Signal Processing Society Best PhD Dissertation Award in 2020, a recipient of the NSF CAREER Award in 2021, and a recipient of the Amazon Research Award in 2022. He is also a co-author of the Best Student Paper Award at the NeurIPS Federated Learning Workshop in 2020, the IEEE Signal Processing Society Flagship Conference ICASSP in 2021, and the IEEE Signal Processing Society Young Author Best Paper Award in 2024. Dr. Chen's current research addresses the theoretical and algorithmic aspects of bilevel optimization and multi-objective optimization and their applications to AI and learning problems: meta-learning (now ICL), LLMs fine-tuning and alignment, and AI-integrated NextG wireless systems.

Shekhar Garde is the Thomas R. Farino Jr. ’67 and Patricia E. Farino Dean of the School of Engineering and the Elaine S. and Jack S. Parker Chaired Professor of Chemical and Biological Engineering at Rensselaer Polytechnic Institute.  He received his bachelor's (University of Bombay, 1992) and PhD (University of Delaware, 1997) degrees in chemical engineering and was a director's fellow at Los Alamos National Labs (1997-1999), before joining Rensselaer in 1999. At RPI he was promoted to full Professor and named Elaine and Jack Parker Endowed Chair in Engineering in 2006, appointed Department Head of the Howard P. Isermann Department of Chemical and Biological Engineering in 2007, and named Dean of Engineering in 2014. His research focuses on understanding the role of water in biological interactions.  He has published over 100 papers (cited 12,500+ times) and presented 150 invited talks at leading universities and conferences. He received the National Science Foundation NSF CAREER Award, Rensselaer Early Career Award, and was the 2011 Robert W. Vaughan Lecturer at CalTech. He is a Fellow of the American Institute of Medical and Biological Engineers and of the American Association for the Advancement of Science. Garde co-leads the award-winning Molecularium Project, which has produced digital dome and IMAX movies and a web-based gaming portal for children. In 2011, Garde was honored with the Explore-Discover-Imagine Award by the Children's Museum of Science and Technology in the Capital District (Albany), NY.

Prof. Tomek Strzalkowski research interests span a wide spectrum of human language technology including computational linguistics and sociolinguistics, socio-behavioral computing, interactive information retrieval, question-answering, human-computer dialogue, serious games, social media analytics, formal semantics, and reversible grammars. He has directed research sponsored by IARPA, DARPA, ARL, AFRL, NSF, the European Commission, NSERC, as well as a number of industry-funded projects. He was involved in IBM’s Jeopardy! Challenge in advanced question answering. Dr. Strzalkowski has published over a hundred and fifty scientific papers, and is the editor of several books, including Advances in Open Domain Question Answering. He serves on the Editorial Board of the journal of Natural Language Engineering.   Prior to joining RPI, Dr. Strzalkowski was Professor of Computer Science at SUNY Albany. At SUNY, he was the founding Director of the Institute for Informatics, Logics, and Security Studies with research budget of more than $35 million. He came to SUNY from GE CRD where he was a Natural Language Group Leader and Principal Scientist. At GE, Dr. Strzalkowski directed projects on automated technical manuals, medical informatics, speech recognition, automated summarization, as well as multimedia processing including language and video. Before coming to GE, he was a research faculty at the Courant Institute of New York University, where he worked on applications of natural language processing to information retrieval.   Current projects include research into social dimensions of information spread online, internet ethnography, and building effective AI defenses against disinformation and exploitation of human socio-cognitive vulnerabilities online, including social engineering attacks. Some example projects include: GATOR: The Goal-oriented Autonomous Dialogue System. We develop a new type of human-machine dialogue system that uses deep learning technologies (such as transformers) to learn how to recognize and generate dialogue plans, i.e., semantic and pragmatic structures that represent one party’s goals and intentions, as well as the impact these are having on the other party. Unlike the current transformer-driven chatbots, the core learning is not to transform one language expression (input) into another language expression (response) but instead to construct a response plan that would properly address the plan in the input and the history of interaction. Consequently, the learning process takes three types of information: (1) the input utterance; (2) its semantic-pragmatic plan, i.e., the plan that was used to produce the utterance, and (3) the history of interaction up to this point. Furthermore, the cumulative history of the dialogue is not merely the memory of the utterances exchanged earlier, but it captures, in a condensed semantic form, the evolving state of the parties’ objectives as well as the emerging sociolinguistic behavioral patterns of both (all) parties. Personalized AutoNomous Agents Countering Social Engineering Attacks (PANACEA) protects online users against current and future forms of social engineering. PANACEA serves as an intermediary between attackers (human, automated, hybrid, coordinated) and the potential victim(s) they target. Depending upon the nature and source of communication, PANACEA either handles it autonomously, or allows the user to proceed with an exchange while monitoring the conversation and intervening as needed by (1) inserting or modifying users’ messages, (2) instructing the user how best to respond, while at the same time (3) initiating an investigation to identify the attacker. (DARPA ASED Program) COMETH (Computational Ethnography from Metaphors and Polarized Language). The objective of this project is to develop a methodology and accompanying software tools for constructing dynamic socio-behavioral models of communities based on online content that their members produce. A community can be defined by the set of salient concepts that its members recognize, along with the values they assign to them. The resulting causal models are then applied to derive culturally biased interpretations of novel information by prototyping the process by which such new information is adapted to fit into the community current model. (DARPA UGB) Social Convos: A New Approach to Modeling Information Diffusion in Social Media. In this project, we recast our understanding of all social media as a landscape of collectives, or “convos”: sets of users connected by a common interest in an (possibly evolving) information artifact, such as a repository in GitHub, a subreddit in Reddit or a group of hashtags in Twitter. Convos are represented by the collections of features that capture their internal social dynamics. Furthermore, convos are basis for modeling large and small internet-based communities as “hybrid organisms” that interact in various ways with one another and react collectively to external stimuli, including information and disinformation campaigns. (DARPA SocialSim)

Trevor David Rhone received a liberal arts education from Macalester College in Saint Paul. He went on to pursue his doctoral studies at Columbia University in the city of New York where he did experimental studies of two-dimensional electron systems in the extreme quantum limit. Trevor David spent several years at NTT Basic research laboratories in Japan.  During a research stint at the National Institute of Materials Science in Japan, he transitioned to materials informatics research - exploiting machine learning tools to perform materials research. He continued this work at Harvard University where he used machine learning tools to search for new 2D magnetic materials. Trevor David Rhone's research interests involve using machine learning tools for materials discovery and knowledge discovery. Materials discovery could manifest in the search new 2D materials with exotic properties, the prediction of the outcome of industrially relevant catalytic reactions or for other compelling research problems. In addition, data analytics tools will be used to aid in developing a better understanding of physical systems.

Chris Carothers is a Professor in the Computer Science Department at Rensselaer Polytechnic Institute. His research interest are in massively parallel systems focusing on modeling and simulation systems of all sorts. Prof. Carothers is an NSF CAREER award winner and is currently active in the DOE Exascale Co-Design Program associated with designs for next generation exascale storage systems as well as the NSF PetaApps Program, and the Army Research Center's Mobile Network Modeling Institute

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.

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 networked systems and quantum computing. He enjoys poker, bridge, squash, tennis and badminton. For more details, please visit his web page.