Source: http://ai.stanford.edu/~vjojic/
Timestamp: 2019-04-19 07:03:08+00:00

Document:
During my PhD studies at the University of Toronto, I developed models and inference techniques for a variety of computational biology problems: phylogeny reconstruction under intractable evolutionary models, simultaneous phasing and haplotype block recovery, and denoising of protein interaction networks. In my thesis work, I addressed the problem of HIV vaccine design, a challenging problem because of the great diversity of the infecting virus. I formulated a combinatorial optimization problem that took into account both immune system operation and viral diversity. For this problem, I developed a number of techniques, both exact and approximate.
As a postdoc in Daphne Koller’s lab at Stanford, I have concentrated on three research areas: population sequence assembly, gene expression modeling and convex optimization methods. In the area of population sequencing I have tackled sequencing HIV and metagenomic samples by formulating probabilistic models of sequence generation and developing inference methods for these models. The focus of my efforts in gene expression analysis has been on devising models of the differential expression that results from differential regulation in immune cell development. Finally, to facilitate the fitting of the gene expression models, I have developed a number of convex optimization techniques.
V. Jojic, N. Jojic, C. Kadie, C. Meek, D. Heckerman, C. Moore, M. John, and S. Mallal. HLA-driven optimization of HIV immunogens using epitome. Conference on Retroviruses and Opportunistic Infection, 2005.
V. Jojic, T. Shay, ImmGen Consortium, A. Regev, D. Koller. A model of regulatory program differentiation in immune cell development. Biology of Genomes, CSHL, 2010.
T. Shay, V. Jojic, ImmGen Consortium, D. Koller, A. Regev. Comparing the transcriptional circuits controlling human and mouse hematopoiesis. Biology of Genomes, CSHL, 2010.
J. Laserson, V. Jojic, and D. Koller. Bayesian population sequencing via the Dirichlet process. Biomedical Computation at Stanford, 2008.
V. Jojic and Q. Morris Untangling protein-protein interaction networks in yeast. Yeast, 2006.
Q. Morris, V. Jojic, and B. J. Frey. Untangling biological networks using graph priors. NIPS workshop on Computational Biology and the Analysis of Heterogeneous Data, 2005.
V. Jojic. Learning self-assembly. Nanotech Insight (keynote), 2005.
V. Jojic. An algorithm for design of protein receptors. Synthetic Receptors, 2005.
N. Jojic, V. Jojic, C. Meek, and D. Heckerman. Graphical models for rational design of AIDS vaccine cocktails. The Learning Workshop, Snowbird, 2004.
V. Jojic, N. Jojic, C. Meek, D. Geiger, A. Siepel, D. Haussler, and D. Heckerman. Efficient approximations for learning phylogenetic HMM models from data. The Learning Workshop, Snowbird, 2004.
M. Daniels, V. Jojic, and A. Lancaster. Simulation and swarm: Co-evolution from beta to the latest buzz, invited talk Los Almos National Laboratory, 1999.
Systems And Methods That Utilize Machine Learning Algorithms To Facilitate Assembly Of AIDS Vaccine Cocktails, USPA 20060095241, Assignee: Microsoft Corp.
Association-Based Epitome Design, USPA 20060160070 Assignee: Microsoft Corp.
Program Verification And Discovery Using Probabilistic Inference, USPA 20080172650, Assignee: Microsoft Corp.
Program Synthesis And Debugging Using Machine Learning Techniques, USPA 20080282108, Assignee: Microsoft Corp.
Population Sequencing Using Short Read Technologies, USPA 20090171640, Assignee: Microsoft Corp.
Page generated 2010-05-28 08:44:52 PDT, by jemdoc.

References: V. 

V. 
 V. 
 V. 

V. 
 V. 

V. 

V. 
 V. 

V. 
 V.