James Hendler

James Hendler
Director, the Rensselaer IDEA & Tetherless World Chair of Computer, Web and Cognitive Sciences

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.

 

Education

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

COVIDMINDER Web Application

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

Sibel Adali
Associate Dean of Science for Research and Graduate Studies

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.

 

Education

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 Area

Trust, Social Networks, Databases

Selected Scholarly Works

Sibel 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

Spring 2020 TWed Virtual Lightning Talks

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!"

On Campus and In Homes, New Approaches to Manufacturing Protective Equipment Being Developed

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.

Anonymous (not verified) Wed, 04/29/2020 - 20:00

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.