(See also: Curriculum Vitae.)

I am broadly interested in the intersection of probability and optimization theory, and in particular in the optimization and control of large-scale stochastic systems. Research problems that I have considered in this setting are typically distinguished one of several features. First, the large size of problem instances precludes exact solution and requires approximation methods. Second, such systems are often naturally distributed, and hence decentralized methods of solution and distributed control policies are particularly important. Finally, explicit probabilistic models for aspects of the system dynamics are often unavailable, thus methods which can learn from historical data or from online system trajectories are relevant.

Specific methodologies include approximate dynamic programming, message-passing algorithms, and machine learning; and application areas include service and communications networks, e-commerce, data-mining, and financial engineering.

Message-Passing Algorithms

1. C.C. Moallemi. A Message-Passing Paradigm for Optimization. Ph.D. Thesis, Stanford University, Stanford, CA, September 2007.
   [preprint]
2. C.C. Moallemi and B. Van Roy. “A message-passing paradigm for resource allocation.” Posted: June 2007. Revised: November 2008.
   [preprint]
3. C.C. Moallemi and B. Van Roy. “Convergence of the min-sum algorithm for convex optimization.” Posted: May 2007.
   [preprint]
   Preliminary version:
   • C.C. Moallemi and B. Van Roy. “Convergence of the min-sum algorithm for convex optimization.” In Proceedings of the 45th Allerton Conference on Communication, Control and Computing, Monticello, IL, September 2007.
4. C.C. Moallemi and B. Van Roy. “Convergence of the min-sum message passing algorithm for quadratic optimization.” Posted: March 2006. Revised: May 2008.
   [preprint]
5. C.C. Moallemi and B. Van Roy. “Consensus propagation.” IEEE Transactions on Information Theory, 2006, 52(11): 4753–4766.
   [from publisher] [preprint]
   Preliminary version:
   • C.C. Moallemi and B. Van Roy. “Consensus propagation.” In Advances in Neural Information Processing Systems 18, MIT Press, 2006.

Optimal Control of Queueing Networks

1. C.C. Moallemi, S. Kumar, and B. Van Roy. “Approximate and data-driven dynamic programming for queueing networks.” Initial version: December 2006. Revised: September 2008.
   [preprint]
2. V.F. Farias, C.C. Moallemi, and B. Prabhakar. “Load balancing with migration penalties.” In Proceedings of the IEEE International Symposium on Information Theory, Adelaide, Australia, September 2005.
   [preprint]

Machine Learning

1. V.F. Farias, C.C. Moallemi, B. Van Roy, and T. Weissman. “Universal reinforcement learning.” Posted: July 2007.
   [preprint]
   Preliminary version:
   • V.F. Farias, C.C. Moallemi, B. Van Roy, and T. Weissman. “A universal scheme for learning.” In Proceedings of the IEEE International Symposium on Information Theory, Adelaide, Australia, September 2005.
2. C.C. Moallemi and B. Van Roy. “Distributed optimization in adaptive networks.” In Advances in Neural Information Processing Systems 16, MIT Press, 2004.
   [preprint] [appendix]
   Preliminary version:
   • C.C. Moallemi and B. Van Roy. “Decentralized protocols for optimization of sensor networks.” In Proceedings of the 42nd Allerton Conference on Communication, Control and Computing, Monticello, IL, September 2003.

Financial Engineering

1. C.C. Moallemi, B. Park, and B. Van Roy. “The execution game.”Posted: January 2008. Revised: April 2008.
   [preprint]

Bioinformatics

1. K. Mason, N.M. Patel, A. Ledell, C.C. Moallemi, and E.A. Wintner. “Mapping protein pockets through their potential small-molecule binding volumes: QSCD applied to biological protein structures.” Journal of Computer-Aided Molecular Design, 2004, 18(1): 55–70.
   [from publisher]
2. J.M. Johnson, K. Mason, C.C. Moallemi, H. Xi, S. Somaroo, and E. Huang. “Protein family annotation in a multiple alignment viewer.” Bioinformatics, 2003, 19(4): 544–545.
   [from publisher]
3. E.A. Wintner and C.C. Moallemi, “Quantized Surface Complementarity Diversity (QSCD): A model based on small molecule-target complementarity.” Journal of Medicinal Chemistry, 2000, 43(10): 1993–2006.
   [from publisher]
4. C.C. Moallemi, “Neural networks in the computer analysis of voided urine cells for bladder cancer.” IEEE Expert, 1991, 6(6): 8–12.
   [from publisher]