Moises Sudit

Moises Sudit is Professor in the Department of Industrial and Systems Engineering at the University at Buffalo's School of Engineering and Applied Sciences, where he serves as Chief Scientist for the Collaborative Institute for Multisource Information Fusion. His research focuses on information fusion, optimization methods, network analysis, and decision support systems for military and security applications. Educational background includes PhD and MS degrees from Purdue University and BS from Georgia Institute of Technology.

Research develops mathematical frameworks for integrating heterogeneous information sources, optimizing sensor resource allocation, and enhancing situational awareness. Recent work explores graph machine learning for healthcare optimization, satellite tasking algorithms, and geospatial analytics for fleet tracking. Applications span defense, healthcare logistics, and critical infrastructure protection.

Publications demonstrate sustained innovation in multi-source fusion methodologies, with current emphasis on spatiotemporal optimization, graph-based machine learning, and adaptive decision algorithms for complex domains including satellite resource management and organ exchange programs.

Professional affiliations include INFORMS, IEEE, IIE, SPIE, and International Society of Information Fusion (ISIF).