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Nir Friedman

Research Interests: Reasoning about Uncertainty

• Inference and learning in probabilistic models: Work on representation, inference, and learning with Bayesian networks and related representations, with applications to concept learning, data mining.
• Computational Biology: Applying probabilistic models to understand biological systems and to analyze data collected from biological sources, such as protein and DNA sequences.

UAI 2002

Research

• You can find a list of my publications.
• Computational Molecular Biology Laboratory
• I have been collaborating with several people.

Research Group

• Current members are
  • Yoseph Barash
  • Gal Elidan
  • Hillel Flischer
  • Tommy Kaplan
  • Ori Mosenzon
  • Tomer Naveh
  • Iftach Nachman
  • Matan Ninio
  • Dana Pe`er


• Past members
  • Tal El Hay
  • Yuval Feinstein
  • Noam Lotner
  
  
• Dissertations
  • Matan Ninio, A Structural EM Algorithm for Phylogenetic Inference, M.Sc. dissertation. PDF, PostScript

Teaching

• Fall 01: Probabilistic Methods in AI
• Fall 00: Introduction to Machine Learning (with Yoram Singer)
• Spring 02: Introduction to Computational Molecular Biology

Computational Biology

• Computational Molecular Biology Laboratory
• Using Bayesian Networks to Analyze Whole-Genome Expression Data
• Inferring Subnetworks from Perturbed Expression Profiles
• Structural EM Phylogenetic Reconstruction

Learning Bayesian Networks

• Bayesian Networks Repository
• NIPS 2001 Tutorial on learning Bayesian networks from Data with Daphne Koller
• AAAI-98 Tutorial on learning Bayesian networks from Data with Moises Goldzsmidt
• Slides from an old talk about Structural EM for learning Bayesian networks (577Kb, gziped PostScript). [The original PPT]

Note: this is a PowerPoint output, and ghostview seems to have problems with it.

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