John Erickson, of the Institute for Data Exploration and Applications (IDEA) at Rensselear Polytechnic Institute (RPI), is the lead engineer of COVIDMINDER--a graph and map-based visualization tool for COVID-19 statistics across the nation, by state and by county, with daily updates. COVIDMINDER is an outgrowth from MORTALITYMINDER which graphs and maps the demographics of mortality disparities. These projects are under the Health INCITE program at RPI with Dr. Kristen Bennett, and is funded by the United Health Foundation.
Sibel Adali is a professor at Rensselaer Polytechnic Institute, which she joined in 1996 after obtaining her PhD from the University of Maryland. Her work concentrates on cross-cutting problems related to trust, information processing and retrieval, and social networks. She has worked as the ARL-lead Collaborative Technology Alliance (CTA) wide Trust Coordinator and the Social and Cognitive Networks Academic Research Center (SCNARC) Associate Director. She is the author of the book "Modeling Trust Context in Networks", which was published by Springer in 2013. At Rensselaer, Adali served as the Associate Head and Graduate Program Director of the Computer Science Department 2015-2018. She currently serves as the Associate Dean of Science for Research and Graduate Studies. She teaches the introductory problem solving course in Computer Science as well as courses in databases. In 2015, Adali received the Trustees' Outstanding Teacher Award, the highest teaching award given by Rensselaer Polytechnic Institute.
Ph.D. 1996, Computer Science Department, University of Maryland at College Park, USA MS. 1994, Computer Science Department, University of Maryland at College Park, USA B.S. 1991, Computer Engineering and Information Science Department , Bilkent University, Ankara, Turkey
Focus AreaTrust, Social Networks, Databases
Selected Scholarly WorksSibel Adali, "Trust Context in Networks", Springer 2013.
Jin-Hee Cho, Kevin Chan and Sibel Adali, "A Survey on Trust Modeling", Computing Surveys, 48(2), 2015.
Ben Horne, Will Dron, Sara Khedr and Sibel Adali, "Assessing the News Landscape: A Multi-Module Toolkit for Evaluating the Credibility of News", WWW 2018 Conference.
Sujoy Kumar Sikdar, Byungkyu Kang, John O'Donovan, Tobias Hollerer and Sibel Adali, "Understanding Information Credibility on Twitter", in Proceedings of SocialCom 2013, Best paper award.
Sibel Adali, Fred Sisenda and Malik Magdon-Ismail, "Actions speak as loud as words: Predicting relationships from social behavior data", Proceedings of the WWW 2012 Conference
Students and staff of The Rensselaer IDEA's Health INCITE program are developing a new data visualization tool that examines how and why COVID-19 impacts regions differently.
Plan to join us for a very, VERY special TWed as the Tetherless World Constellation holds a "virtual" version of our end-of-term Graduate Research "Lightning Talks." TWed Lightning Talks are a great way for the TWC community and friends to learn of the wide range of amazing research happening in the Tetherless World, and "a good time is had by all!"
From a variety of locations in the Capital Region, and throughout the country, Rensselaer Polytechnic Institute faculty, students, and staff are pressing their knowledge and machinery to work making personal protective equipment for those on the front lines of the pandemic.
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.
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.
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.
B.S., Physics, Yale University, 1993. M.S., Physics California Institute of Technology, 1995. PhD., EE/Physics, California Institute of Technology, 1998.
Focus AreaLearning 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 WorksMalik 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.
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.