Ankesh Khandelwal

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Ankesh Khandelwal
Contact Info

Ankesh Khandelwal is a PhD candidate at RPI. During his training phase, first two year of PhD!, he worked towards formal analysis of the AIR language, a rule based policy language. After the training phase, he is looking into methods for representing complex knowledge, such as physical processes, in machine readable form.

Khandelwal, A. . Ankesh Thesis Defense - Front.
Khandelwal, A. . Ankesh Thesis Defense - Back.
Khandelwal, A. . Furthering the Continuous-Change Event Calculus: Providing for Efficient Description of Additive Effects and an Automated Reasoner.
Khandelwal, A., Jacobi, I., and Kagal, L. . Linked Rules: Principles for Rule Reuse on the Web. In Proceedings of Web Reasoning and Rule Systems, Fifth International Conference (August 29-30 2011).
Jacobi, I., Kagal, L., and Khandelwal, A. . Rule-Based Trust Assessment on the Semantic Web. In Proceedings of RuleML 2011 (July 19-21 2011, Barcelona, Spain).
Khandelwal, A., Bao, J., Kagal, L., Jacobi, I., Ding, L., and Hendler, J. . Analyzing the AIR Language: A Semantic Web (Production) Rules Language. In Proceedings of Web Reasoning and Rule Systems Conference 2010 (September 22-24 2010, Bressanone, Italy).


Project Collaborator

PEO Project LogoProcess and Event Ontologies (PEO)
Principal Investigator: Peter Fox
Description: The PEO project investigates methods for representation and interpretation of scientific and natural processes and events.
DCO-DS LogoTheory and Practice of Accountable Systems (TPAS)
Principal Investigator: Jim Hendler
Description: The TPAS Project investigates computational and social properties of information networks necessary to provide reliable assessments of compliance with rules and policies governing the use of information.
TAMI LogoTransparent and Accountable Datamining Initiative (TAMI)
Principal Investigator: Deborah L. McGuinness and Jim Hendler
Description: The TAMI Project is creating technical, legal, and policy foundations for transparency and accountability in large-scale aggregation and inferencing across heterogeneous information systems.