IDEA Health INCITE Undergraduate Research Opportunities (Summer 2020)

The Rensselaer IDEA Health INCITE program is excited to announce immediate openings for six- and twelve-week undergraduate research opportunities for the Summer 2020 Term.  All work will be done remotely.   We are seeking students to work on data analytics research related to the COVID-19 pandemic.  This work will include data analytics, web app development, and creation of social media content.  

Privacy Preserving Synthetic Health Data Generation and Evaluation

Attempting to use real medical data in a classroom setting is hard to do without limiting yourself to specific datasets. Through the research being presented we work to create an end-to-end workflow for generating synthetic health data and testing the synthetic data for privacy, resemblance, and utility. This includes creating a novel generation method called HealthGAN and defining metrics for measuring the privacy and resemblance of the generated data. The utility of the data is then measured in the context of the analysis task the dataset was designed to accomplish.

Malik Magdon-Ismail

Malik Magdon-Ismail

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.

Education

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.

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.

Education

PhD, Slavic Languages and Literatures, Princeton University

Focus Area

linguistics, cognitive modeling, natural language processing

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