Dan Geiger

Research Interests

My main research is focused on the study of probabilistic models for intelligent systems, in particular, the study of Bayesian networks and their applications in Bioinformatics and in other domains. I have focused my attention on several aspects of Bayesian networks, including, (1) Foundation – which independence assumptions are encoded in a Bayesian network, (2) Exact Inference – how to efficiently answer queries using a Bayesian network, (3) Learning – how to learn Bayesian networks from data, and (4) Applications – building effective intelligent systems based on Bayesian networks. Currently, I am mostly interested in using probabilistic models effectively for mapping disease genes and for other Bioinformatics tasks. I am interested in building state of the art software packages that help geneticists map genes for complex diseases either by linkage studies, association studies, or other methods. The experience with graphical models has lead me to construct with my students an efficient program for linkage analysis called Superlink to which I devote a sizeable portion of my time.

Ph.D Students

  • Anna Becker. Studied combinatorial problems related to exact inference and their applications to genetic linkage analysis (2000).
  •  Ma’ayan Fishelson. Studied genetic linkage analysis (2004).
  •  Dmitry Rusakov. Studied asymptotic Bayesian model selection criteria (2004).
  •  Gideon Greenspan. Studied blocks of SNPs and their usage in association analysis (2005).
  •  Ydo Wexler. Studied approximate inference with applications to genetic linkage analysis (2007).
  • Ron Zohar. Studied group tracking methodologies using probabilistic models (2007).
  • Mark Silberstein. Studied mechanisms for building supercomputing systems for genetic linkage analysis using large-scale distributed resources (2010).
  • Sivan Bercovici. Studied gene mapping by admixture linkage disequilibrium (2010).

Selected Publications

  • Dan Geiger, Tom Verma, and Judea Pearl. Identifying independence in Bayesian networks. Networks, 20(5):507-534, 1990. [ bib | http | .pdf ]
  •  David Heckerman, Dan Geiger, and David M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine learning, 20(3):197-243, 1995. [ bib | http | .pdf ]
  • Nir Friedman, Dan Geiger, and Moises Goldszmidt. Bayesian network classifiers. Machine learning, 29(2):131-163, 1997. [ bib | http | .pdf ]
  • Ma’ayan Fishelson and Dan Geiger. Exact genetic linkage computations for general pedigrees. Bioinformatics, 18(suppl 1):S189-S198, 2002. [ bib | http | .pdf ]
  • Mark Silberstein, Anna Tzemach, Nickolay Dovgolevsky, Ma’ayan Fishelson, Assaf. Schuster, and Dan Geiger. Online system for faster multipoint linkage analysis via parallel execution on thousands of personal computers. The American Journal of Human Genetics, 78(6):922-935, 2006. [ bib | http | .pdf ]
  • Sivan Bercovici and Dan Geiger. Inferring ancestries efficiently in admixed populations with linkage disequilibrium. Journal of Computational Biology, 16(8):1141-1150, 2009. [ bib | http | .pdf ]


  • ATRHunter – finds approximate tandem repeats in a genomic sequences. Developed by Ydo Wexler.
  • HaploBlock – finds blocks of SNPs and haplotypes from genotypes. Developed by Gideon Greenspan.
  • Superlink – finds suspected location of disease genes from pedigree information. Developed by Ma’ayan Fishelson.
  • Superlink Online – finds suspected location of disease genes from pedigree information. Developed by Mark Silberstein et al.
  • Superlink Online SNP – finds suspected location of disease genes from pedigree information. Developed by Mark Silberstein et al.

Some Favorite Biking Links

Contact Information

Electronic mail: dang@cs.technion.ac.il
Office phone number: +972 4 829 4339
Address: Technion – Israel Institute of Technology, Computer Science Department, Taub 616, Haifa, 36000, Israel
Link to the Laboratory of Computational Biology