Address: Room 124, Gates Bldg. 1A
Computer Science Department
Stanford University
Stanford, CA 94305-9010
E-mail:galel@cs.stanford.edu
Research Interests:
Probabilistic Graphical Models. Fundamental
representations and methods for inference and learning in large scale
domains, with an emphasis on high-level elements such as the discovery
of hidden variables and classes, transfer of knowledge between
related classes/tasks, and the combination of discriminative and
generative elements within a unified model.
Real-life Applications. Applying fundamental techniques to
challenging domains such as computational biology and machine vision.
Recently, I have started looking into the development of principled
techniques based on probabilistic knowledge for diagnosis in the field
of medical informatics. Using challenges in these fields as a feedback
trigger for fundamental research.
Submitted papers
Learning Bounded Treewidth Bayesian Networks
(pdf).
with Stephen Gould.
Submitted to the Journal of Machine Learning Research, 2008.
LOOPS: Localizing Object Outlines using Probabilistic Shape
(pdf).
with Geremy Heitz, Ben Packer and Daphne Koller.
Submitted to the International Journal of Computer Vision, 2008.
Journal Publications
Multi-Class Segmentation with Relative Location Prior
(pdf).
with Stephen Gould, Jim Rodgers, David Cohen and Daphne Koller.
To appear in the International Journal of Computer Vision, 2008.
Markov random field based automatic image alignment for electron tomography
(pdf).
with Fernando Amat, Farshid Moussavi, Louis Comolli, Kenneth Downing and Mark Horowits.
Journal of Structural Biology, In Press, 2007.
"Ideal Parent" Structure Learning for Continuous Variable Networks
(pdf).
with Iftach Nachman and Nir Friedman.
Journal of Machine Learning Research (JMLR), Vol. 8, p. 1799-1833, 2007.
Towards an Integrated Protein-Protein Interaction Network: A Relational Markov Network Approach
(pdf).
with Ariel Jaimovich, Hanah Margalit and Nir Friedman Journal of Computational Biology (JCB), Mar 2006, Vol. 13, No. 2: 145-164.
Learning Hidden Variable Networks: The Information Bottleneck Approach
(pdf)
with Nir Friedman.
Journal of Machine Learning Research (JMLR), Vol. 6, p. 81-127, 2005.
CIS: Compound Importance Sampling Method for Transcription Factor Binding Site p-value Estimation
(pdf)
with Yoseph Barash, Nir Friedman and Tommy Kaplan.
BioInformatics, Vol. 20(6), p. 839-46, 2004.
Inferring Subnetworks from Preturbed Expression Profiles
(pdf)
with Dana Pe'er, Aviv Regev and Nir Friedman
Bioinformatics, Vol. 17:S, p. 215-224, 2001.
Peer-Reviewed Conference Publications
Convex Point Estimation using Undirected Bayesian Transfer Hierarchies
(pdf)
with
Ben Packer,
Geremy Heitz,
and Daphne Koller To appear in the Twenty Fourth Conference on Uncertainty in Artificial Intelligence (UAI), 2008.
Using Combinatorial Optimization within Max-Product Belief Propagation
(pdf)
with
John Duchi,
Danny Tarlow,
and
Daphne Koller Neural Information Processing Systems (NIPS) conference, 2006.
Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing
(pdf)
with Ian McGraw and Daphne Koller Twenty Second Conference on Uncertainty in Artificial Intelligence (UAI), 2006.
Learning Object Shape: From Drawings to Images
(pdf)
with Geremy Heitz and Daphne Koller IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2006.
Toward an Integrated Protein-Protein Interaction Network
(pdf)
with Ariel Jaimovich and Nir Friedman.
The Ninth Annual International Conference on Research in Computational Molecular Biology (RECOMB), 2005 (also presented in the KEYSTONE 2005 workshop).
"Ideal Parent" Structure Learning for Continuous Variable Networks
(pdf)
with Iftach Nachman and Nir Friedman.
Twentieth Conference on Uncertainty in Artificial Intelligence (UAI), 2004.
Runner-up for Best Student paper award.
CIS: Compound Importance Sampling Method for Transcription Factor Binding Site p-value Estimation
(pdf)
with Yoseph Barash, Nir Friedman and Tommy Kaplan.
International Conference on Intelligent Systems for Molecular Biology (ISMB), 2004.
The Information Bottleneck Expectation Maximization Algorithm
(pdf)
with Nir Friedman,
Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI), 2003.
Modeling Dependencies in Protein-DNA Binding Sites
(pdf)
(web supplement)
with Yoseph Barash, Nir Friedman and Tommy Kaplan.
The Seventh Annual International Conference on Research in Computational Molecular Biology (RECOMB), 2003.
Data Perturbation for Escaping Local Maxima in Learning
(pdf)
(poster)
with Matan Ninio, Nir Friedman and Dale Schuurmans.
The Eighteenth National Conference on Artificial Intelligence, 2002.
Inferring Subnetworks from Perturbed Expression Profiles
(pdf)
with Dana Pe'er, Aviv Regev and Nir Friedman.
International Conference on Intelligent Systems for Molecular Biology (ISMB), 2001.
Best paper award.
Learning the Dimensionality of Hidden Variables
(pdf)
(poster)
with Nir Friedman.
The Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 2001.
Gal Elidan
