Alexander Olshevsky
SCH: INT: Distributed Analytics for Enhancing Fertility in Families
The demands of modern life, education and career choices, as well as the availability of assisted reproductive technologies, are leading many individuals and couples to delay childbearing. This has contributed to infertility and sub-fertility emerging as significant public health problems in the U.S., affecting about 15% of couples, involving both men and women, and resulting […]
Efficiently Distributing Optimization Over Large-Scale Networks
This project will design new algorithms for distributed optimization which can work without any kind of central coordinator or processor server and whose asymptotic performance always improves in larger networks. The algorithms will run over communication networks based on peer-to-peer nearest neighbor with connectivity backbones that can vary with time. Our model will explicitly account […]
CAREER: Algorithms and Fundamental Limitations for Sparse Control
The proposal is to study the design of feedback control strategies which stabilize and steer systems by affecting them in only a few variables. The motivation comes from applications which are either large-scale or geographically distributed and therefore cannot be feasibly affected in many places. A primary motivating application is the control of metabolic chemical […]
Achieving Consensus Among Autonomous Dynamic Agents using Control Laws that Maintain Performance as Network Size Increases
Recent advances in automation and robotics have created a pressing need for new “protocols,” that is, for algorithms or control laws that allow teams of multiple autonomous agents to cooperate and accomplish complex tasks. Unfortunately, many of the best protocols for multi-agent coordination problems suffer from scalability issues, that is, while they perform well when […]