Malik Magdon-Ismail

Malik Magdon-Ismail

After degrees at Yale and Caltech, Dr. Magdon-Ismail was a research scholar at Caltech before joining Rensselaer as Assistant Professor of Computer Science in 2000. 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.


B.S., Physics, Yale University, 1993. M.S., Physics California Institute of Technology, 1995. PhD., EE/Physics, California Institute of Technology, 1998.

Focus Area

Learning from Data; theory and applications., Computational Finance, Social and Communication Networks; Hidden Groups., Inference and Search on Volunteer Computing Platforms, Collective Wisdom in Multi-agent Systems; Prediction Markets.

Selected Scholarly Works

Malik Magdon-Ismail, "Permutation Complexity Bound on Out-Sample Error", Proc. 24th Annual Conference on Neural Information Processing Systems (NIPS), 2010.

Costas Busch, Malik Magdon-Ismail "Atomic Routing Games on Maximum Congestion", Theoretical Computer Science, Volume 410, Issue 36, Pages 3337-3347, 2009.

Malik Magdon-Ismail, Konstantin Mertsalov, "A Permutation Approach to Validation", Proc. 10th SIAM International Conference on Data Mining (SDM), pages 882-983, Columbus Ohio, April 29-May 1, 2010.

Sanmay Das, Malik Magdon-Ismail, "Collective Wisdom: Information Growth in Wikis and Blogs", ACM Conference on E-Commerce (EC 2010), pages 231-240, June 7-8 , Cambridge Massachusetts, 2010.

Costas Busch, Malik Magdon-Ismail, Marios Mavronicolas, "Universal Bufferless Packet Switching", Siam Journal on Computing, Volume 37, Issue 4, pages 1139-1162, 2007.

Malik Magdon-Ismail, and Joseph Sill "A Linear Fit Gets the Correct Monotonicity Directions", Machine Learning, Volume 70, Number 1 / January, 2008, pages 21-43.

Volkan Isler, Malik Magdon-Ismail "Sensor Selection in Arbitrary Dimension", IEEE Transactions on Automation Science and Engineering (TASE), Vol. 5, No. 4, pages 651-660, 2008.

Ali Civril, Malik Magdon-Ismail "On Selecting a Maximum Volume Sub-Matrix of a Matrix and Related Problems", Theoretical Computer Science, 2009.

Nathan Cole, Heidi Joe Newberg, Malik Magdon-Ismail, Travis Desell, Kristopher Dawsey, Warren Hayashi, Xinyang (Fred) Liu, Jonathan Purnell, Boleslaw Szymanski, Carlos Varela, James Wisniewski, "Maximum Likelihood Fitting of Tidal Streams with application to the Sagittarius Dwarf Tidal Tails", the Astrophysical Journal, Vol 683, pages 750-766 (2008).

Jeffery Baumes, Mark Goldberg, Mykola Hayvonovych, Malik Magdon-Ismail, William Wallace, Mohammed Zaki, "Finding Hidden Group Structure in a Stream of Communications", <strong>[Top 3 Paper Award]</strong>, Proceedings of the 4th Symposium on Intelligence and Security Informatics (ISI 06), San Diego, CA, May 23-24 2006.
Machine Learning Models Predict COVID-19 Impact in Smaller Cities

Rensselaer Polytechnic Institute researcher Malik Magdon-Ismail tailored the robust machine learning models he is developing for the COVID pandemic to work with sparse data points, like those available during the early phase in a pandemic or in smaller cities, which ordinarily make trend-spotting difficult.

Anonymous (not verified) Thu, 04/16/2020 - 20:00
Data Visualization Tool Examines Community Factors Underlying COVID-19 Outcomes

Using daily updated data, COVIDMinder, a new data visualization tool, examines how and why COVID-19 impacts regions differently by comparing community risks, mediation tools, and outcomes related to COVID-19 by state across the United States, and by county within New York state.

Anonymous (not verified) Sun, 04/12/2020 - 20:00

First Annual Cognitive and Immersive Data Insights Application Challenge (June 5–6, 2018)

On June 5-6, 2018 The Lally School of Management, the Rensselaer Institute for Data Exploration and Applications (IDEA) / Health INCITE, the Center for Global Communication+Design (Comm+D), and the Cognitive and Immersive Systems Lab (CISL) presented the 2018 Rensselaer Cognitive and Immersive Data Insights Application Challenge. More than 40 RPI undergrad and grad students competed in this inaugural challenge to create immersive, multimodal, collaborative applications using health and business datasets.

Marjorie McShane

Marjorie McShane

Marge McShane is a cognitive scientist, computational linguist and knowledge engineer who develops cognitive models of intelligent agents that can collaborate with people in task-oriented, dialog applications. 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. McShane was a central contributor to the Boas system, a proof-of-concept system that elicited knowledge about any of the world’s languages from linguistically untrained native speakers. Boas used a mixed-initiative strategy, by which the system guided certain aspects of the knowledge compilation process and the user took the lead in others. Among the key requirements were that the system accommodate descriptions of not only anticipated, but also unanticipated, phenomena; that the descriptions be sufficiently formal to directly provide support to a generic machine translation engine; and that the system be usable by informants without the support of developers.

McShane 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. Guided by the functional needs of such agents, McShane has recently begun to pursue the modeling of “mindreading” (otherwise known as mental model ascription), defined as inferring features of another human or artificial agent that cannot be directly observed, such as that agent's beliefs, plans, goals, intentions, personality traits, mental and emotional states, and knowledge about the world. This capability is an essential functionality of intelligent agents if they are to engage in sophisticated collaborations with people.

McShane has authored two books, A Theory of Ellipsis (Oxford University Press, 2005) and An Innovative, Practical Approach to Polish Inflection (Lincom Europa, 2003), and has published extensively in the areas of linguistics, natural language processing, cognitive modeling and knowledge representation.


PhD, Slavic Languages and Literatures, Princeton University

Focus Area

linguistics, cognitive modeling, natural language processing

James Hendler

James Hendler
Director of the Institute for Data Exploration and Applications and the Tetherless World Senior Constellation Professor of Computer and Cognitive Science

James Hendler is the Director of the Institute for Data Exploration and Applications and the Tetherless World Professor of Computer, Web and Cognitive Sciences at RPI.  He also heads the RPI-IBM Center for Health Empowerment by Analytics, Learning and Semantics (HEALS) and serves as a Chair of the Board of the UK’s charitable Web Science Trust. 

One of the originators of the “Semantic Web,” Hendler was the recipient of a 1995 Fulbright Foundation Fellowship, is a former member of the US Air Force Science Advisory Board, and is a Fellow of the AAAI, BCS, the IEEE, the AAAS and the ACM. He is also the former Chief Scientist of the Information Systems Office at the US Defense Advanced Research Projects Agency (DARPA) and was awarded a US Air Force Exceptional Civilian Service Medal in 2002. He is also the first computer scientist to serve on the Board of Reviewing editors for Science. 

In 2010, Hendler was named one of the 20 most innovative professors in America by Playboy magazine and was selected as an “Internet Web Expert” by the US government. In 2013, he was appointed as the Open Data Advisor to New York State and in 2015 appointed a member of the US Homeland Security Science and Technology Advisory Committee. In 2016, became a member of the National Academies Board on Research Data and Information and in 2018 became chair of the ACM’s US technology policy committee and was elected a Fellow of the National Academy of Public Administration.



Ph.D., Computer Science, Artificial Intelligence, Brown University

Sc.M., Computer Science, Artificial Intelligence, Brown University

M.S., Cognitive Psychology, Human Factors Engineering, Southern Methodist University

B.S., Computer Science, Artificial Intelligence, Yale University

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COVID-19 Modeling and Data Resources Recommendations Needed johnsa17 Mon, 03/23/2020 - 18:07
Letter from Richie C. Hunter, Vice President, Strategic Communications and External Relations, soliciting suggestions for COVID-19 modeling and data resources for IDEA and the Rensselaer Libraries

COVID-19 Modelling Resources, Data and Challenges (updated: 05 July 2020)

Rensselaer data analytics students, researchers and colleagues are analyzing the data emerging from the 2019-2020 COVID-19 outbreak/pandemic. In early March 2020 The Rensselaer IDEA started gathering a set of resources on exploring and modelling current global data. This list will be updated periodically as new resources become available. Many thanks to our friends and colleagues near and for for contributing to this list!