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.
Ph.D. Computer Science, University of Toronto 2007
Thesis: Algorithms for rational vaccine design (PDF)
B.Sc. Mathematics and Computer Science, University of Illinois 2001
Stanford University, Department of Computer Science, Postdoc (Aug. 07 - Present)
Population sequencing using next-gen platforms, modeling immune cell differentiation, large scale convex optimization
University of Toronto, Probabilistic and Statistical Inference Group (Aug. 02 - Aug. 07)
Denoising of cDNA microarray data, HIV sequence analysis, denoising protein-protein interaction networks
Microsoft Corp., Machine Learning and Applied Statistics Group (Nov. 05 - Jan. 06)
Automating correction and inference of imperative programs from input/output examples
Microsoft Corp., Machine Learning and Applied Statistics Group (Jan. - Dec. 04)
HIV evolution modeling, HIV vaccine design, epitope crossreactivity modeling, self assembly in nanotechnology
Microsoft Corp., Machine Learning and Applied Statistics Group (June - Sept. 03)
Haplotype block analysis, approximate inference in phylogenetic tree reconstruction
Microsoft Corp., full-time, Adaptive User Interfaces/ASP.NET group (May 01 - Aug. 02)
Microsoft Corp., intern, Windows (CoreOS) group (May - Sept. 00)
The Santa Fe Institute, full-time, Swarm project (Oct. 98 - Sept. 99)
V. Jojic, S. Saria and D. Koller. Convex envelopes of complexity controlling penalties: the case against premature envelopment. AISTATS, 2011. (PDF)
V. Jojic, S. Gould and D. Koller. Accelerated dual decomposition for MAP inference. ICML, 2010. (PDF)
J. Laserson, V. Jojic, and D. Koller. Genovo: De novo assembly for metagenomics. RECOMB, 2010.(PDF)
V. Jojic, T. Hertz, and N. Jojic. Population sequencing using short reads: HIV as a case study. Pac Symp Biocomput, pages 114-125, 2008. (PDF)
D. Dueck, B. J. Frey, N. Jojic, V. Jojic, G. Giaever, A. Emili, F. Musso, and R. Hegele. Constructing treatment portfolios using affinity propagation. In International Conference on Research in Computational Molecular Biology (RECOMB), 2008. (PDF)
M. Rolland, D. C. Nickle, W. Deng, N. Frahm, C. Brander, G. H. Learn, D. Heckerman, N. Jojic, V. Jojic, B. D. Walker, and J. I. Mullins. Recognition of HIV-1 peptides by host CTL is related to HIV-1 similarity to human proteins. PLoS ONE, 2:e823, 2007. (link)
N. Jojic, V. Jojic, B. J. Frey, C. Meek, and D. Heckerman. Using epitomes to model genetic diversity: Rational design of hiv vaccine cocktails. In Advances in Neural Information Processing Systems, 2005. (PDF)
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.
N. Jojic, V. Jojic, and D. Heckerman. Joint discovery of haplotype blocks and complex trait associations from SNP sequences without family data. Uncertainty in Artificial Intelligence, 2004. (PDF)
V. Jojic, N. Jojic, C. Meek, D. Geiger, A. Siepel, D. Haussler, and D. Heckerman. Efficient approximations for learning phylogenetic HMM models from data. Bioinformatics, 20 Suppl 1:i161-168, 2004. (link)
W. T. Peng, M. D. Robinson, S. Mnaimneh, N. J. Krogan, G. Cagney, Q. Morris, A. P. Davierwala, J. Grigull, X. Yang, W. Zhang, N. Mitsakakis, O. W. Ryan, N. Datta, V. Jojic, C. Pal, V. Canadien, D. Richards, B. Beattie, L. F. Wu, S. J. Altschuler, S. Roweis, B. J. Frey, A. Emili, J. F. Greenblatt, and T. R. Hughes. A panoramic view of yeast noncoding RNA processing. Cell, 113:919-933, 2003. (link)
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.
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)
IEEE Transactions on Computational Biology and Bioinformatics (TCBB)
Uncertainty in Artificial Intelligence (UAI)
Society membership: American Mathematical Society, Association for Computing Machinery
Microsoft Research Fellowship
Studenica Foundation Fellowship