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stanford university, stanford, ca

m.s. degree in engineering economic systems and operations research in june 2000.

ph.d. degree in management science and engineering june 2004.

dissertation title: "multi-agent learning and coordination algorithms for distributed dynamic resource allocation."

dissertation advisor: nicholas bambos

massachusetts institute of technology, cambridge, ma

b.s. degree in mathematics in june 1997.

m.s. degree in systems science and control engineering from the department of electrical engineering and computer science in june 1998. masters thesis topic: context-sensitive planning for autonomous vehicles operating in complex, uncertain, and nonstationary environments.

experience

sun microsystems laboratories,  menlo park, ca

april 2003 – present:

conceiving, developing and implementing self-managing and self-optimizing capabilities in computer systems, covering domains such as: cache-aware thread scheduling and cpu power management, dynamic sharing of cpu/memory/bandwidth, dynamic data migration in distributed storage systems, dynamic job scheduling and job pricing in cloud computing, dynamic user migration in distributed virtual environments, etc.

principal investigator for the adaptive optimization project since 2006.

multiple patent applications filed, conference/journal papers published, multiple successful adaptive learning systems designed and implemented. the publicly available case studies are in the “technical reports” section of http://research.sun.com/people/vengerov/publications.html.

intelligent inference systems corp., sunnyvale, ca research scientist

april 2002 – april 2003: started a new research initiative in applying the acfrl algorithm and the previously developed multi-agent coordination algorithms to power control in wireless networks. published several conference papers on this topic. results demonstrate an improvement by more than a factor of 2 in comparison with the algorithms used in is-95 and cdma2000 standards.

april 2002 – april 2003: wrote a phase i sttr proposal to the office of naval research and received funding for the topic of “perception-based co-evolutionary reinforcement learning for uav sensor allocation.” developed theoretical algorithms and designed a practical implementation strategy, which demonstrated excellent results in a high-fidelity robotic simulator. published a conference paper.

october 1998 – april 2002: wrote a proposal to the nasa program in thinking systems and received multi-year funding for the topic of cooperation and coordination in multi-agent systems. developed, evaluated, and published new reinforcement learning algorithms for dynamic resource allocation among distributed agents operating jointly in complex, uncertain, and nonstationary environments.