Roni Khardon

Associate Professor
Department of Computer Science
Tufts University

Research Interests:
Machine Learning and Data Mining
Artificial Intelligence
Theoretical Computer Science and Efficient Algorithms

RA positions available in AI and in ML:

AI: We are looking for strong PhD students with background in logic, probability, AI planning, and ideally statistical relational models. Ideal candidates will have strong theoretical interest and skills and excellent programming and system building skills.
ML: We are looking for strong PhD students with background in probability and statistics, algorithms, and ideally some exposure to pattern recognition, data mining, machine learning or AI. Ideal candidates will have strong theoretical interest and skills and excellent programming and system building skills.
Applying: If you have such background please apply using the normal procedures for Tufts. See this link for department information and/or email me with questions.

Recent Projects

For Research, Publications, Datasets, Software, CV ... please see the Machine Learning Group Web Site .
See also the AI pages (under construction)

Machine Learning: Research Highlight: Time Series for Astronomy Learning Planning and Acting in Complex Domains: Research Highlight: FODDs

Various projects are exploring foundations and interdisciplinary applications of machine learning, for astronomy, remote sensing, environmental engineering, and more. For astronomy we have investigated several approaches to signal processing, analysis, and classification of time series capturing the of luminosity of stars, with the goal of automating the categorization of light sources in astronomy sky surveys. To date we have focused on identifying variable periodic stars and on identifying quasars. In this line of research our goal is to develop theories and systems that will enable building agents that can act intelligently in complex structured non-deterministic environments. Our main effort in recent work has been to develop a knowledge representation (FODD) and algorithms for it that serve as the engine to build such agents. The work combines logical analysis, probabilistic inference, and algorithmic ideas, and solves planning problems at an abstract level. Recent application shows that our approach can solve difficult open world robot planning and be deployed on physical robots.

Publications: ECML 2009 / IJCAI 2009 Workshop / ECML 2010 / ApJ 2011 / ApJ 2012 Publications: IJCAI07 / UAI 2007 / JAIR 2008 / ICAPS 2008 / IJCAI 2009 / ICAPS 2010 / AAAI 2010 / JAIR 2011 / AIJ 2011 / ICRA 2012

To start exploring see this article: Astronomy Time Series To start exploring see this article: FODDs for Relational MDPs
See also videos from ICRA paper: simulation showing the communication between abstract planner and robot architecture and demo on simple physical robot.

Partly supported by NSF grant IIS-0803409. Partly supported by NSF grants IIS-0936687 and IIS 0964457.

For More Information:

For Research, Publications, Datasets, Software, CV ... please see the Machine Learning Group Web Site
Teaching
Link for Barbara Hillers.

Recent Talks

First Order Decision Diagrams for Relational Markov Decision Processes UIUC, Artificial Intelligence Seminar, March 2011 , University of Massachusetts, Amherst, Planning Seminar, November 2010, Rutgers University, April 2010, PLL Summit, KU Leuven, Belgium, July 2009, Algorithmic Decision Theory, April 2009.
Classifying Stars: Machine Learning for Astrophysics IBM Haifa Research Lab, Machine Learning Group, December 2010
Stochastic Planning and Lifted Inference: AAAI10 workshop on Statistical Relational AI (starAI-10).
Learning to Classify Graphs and Hypergraphs Boston University , November 2008, Toronto University, December 2007

Recent Conference and Editorial Activities

Associate Editor (action editor) for the Machine Learning Journal (ML)
Associate Editor (action editor) for the Journal of Artificial Intelligence Research (JAIR)

Senior Program committee member for The National Conference on Artificial Intelligence (AAAI 2012)
Program Committee member for ICML 2012 workshop on Statistical Relational Learning (SRL-12)
Program Committee member for UAI 2012 workshop on Statistical Relational AI (StaRAI-12)
Program committee member for The International Conference on Principles of Knowledge Representation and Reasoning (KR 2012)
Program committee member for The International Conference on Automated Planning and Scheduling (ICAPS 2012)
Program committee member for International Symposium on Artificial Intelligence and Mathematics (ISAIM 2012)
Area Chair for The National Conference on Artificial Intelligence (AAAI 2011)
Senior Program committee member for The International Joint Conference on Artificial Intelligence (IJCAI 2011)
Program committee member for The International Conference on Automated Planning and Scheduling (ICAPS 2011)
Program committee member for The 3rd workshop on learning and planning held with ICAPS 2011
Program committee member for The workshop on generalized planning held with AAAI 2011
Program committee member for The International Conference on Machine Learning (ICML 2011)
Program committee member for The International Conference on Inductive Logic Programming (ILP2011)
Area Chair for The National Conference on Artificial Intelligence (AAAI 2010)
Program Committee member for The International Conference on Machine Learning (ICML 2010)
Program Committee member for The European Conference on Machine Learning (ECML/PKDD 2010)
Program Committee member for AAAI10 workshop on Statistical Relational AI (starAI-10)
Program committee member for The Conference on Algorithmic Learning Theory (ALT2009)
Program Committee member for The International Conference on Machine Learning (ICML 2009)
Program committee member for The International Joint Conference on Artificial Intelligence (IJCAI 2009)
I am co-organizing the Learning Track in the 2008 International Planning Competition together with Alan Fern and Prasad Tadepalli
Program Committee member for The International Conference on Machine Learning (ICML 2008)
Program committee member for Workshop on Algorithms for Large-Scale Information Processing in Knowledge Discovery held in conjunction with PAKDD2008
Program committee member for International Symposium on Artificial Intelligence and Mathematics (ISAIM 2008)
Program committee member for The Conference on Learning Theory (COLT2007)
Program Committee member for The International Conference on Machine Learning (ICML 2007)
Program committee member for The National Conference on Artificial Intelligence (AAAI 2007)

Contact Information:

Roni Khardon
Department of Computer Science
Tufts University
161 College Ave.
Medford, MA 02155
USA

Office: Halligan 230
Phone: 1-617-627-5290
Fax: 1-617-627-2227
Dept.: 1-617-627-2225
Email: roni@cs.tufts.edu

Machine Learning: Research Highlight: Time Series for Astronomy	Learning Planning and Acting in Complex Domains: Research Highlight: FODDs
Various projects are exploring foundations and interdisciplinary applications of machine learning, for astronomy, remote sensing, environmental engineering, and more. For astronomy we have investigated several approaches to signal processing, analysis, and classification of time series capturing the of luminosity of stars, with the goal of automating the categorization of light sources in astronomy sky surveys. To date we have focused on identifying variable periodic stars and on identifying quasars.	In this line of research our goal is to develop theories and systems that will enable building agents that can act intelligently in complex structured non-deterministic environments. Our main effort in recent work has been to develop a knowledge representation (FODD) and algorithms for it that serve as the engine to build such agents. The work combines logical analysis, probabilistic inference, and algorithmic ideas, and solves planning problems at an abstract level. Recent application shows that our approach can solve difficult open world robot planning and be deployed on physical robots.

Publications: ECML 2009 / IJCAI 2009 Workshop / ECML 2010 / ApJ 2011 / ApJ 2012	Publications: IJCAI07 / UAI 2007 / JAIR 2008 / ICAPS 2008 / IJCAI 2009 / ICAPS 2010 / AAAI 2010 / JAIR 2011 / AIJ 2011 / ICRA 2012
To start exploring see this article: Astronomy Time Series	To start exploring see this article: FODDs for Relational MDPs See also videos from ICRA paper: simulation showing the communication between abstract planner and robot architecture and demo on simple physical robot.
Partly supported by NSF grant IIS-0803409.	Partly supported by NSF grants IIS-0936687 and IIS 0964457.