Alexander Nikolaev is an Associate Professor in the Department of Industrial and Systems Engineering at the University at Buffalo (University at Buffalo), within the School of Engineering and Applied Sciences. His research focuses on operations research, decision-making under uncertainty, machine learning, social network analysis, causal inference, e-health, and e-learning. He holds a PhD from the University of Illinois at Urbana-Champaign (2008), an MS in Industrial Engineering from The Ohio State University (2003), and dual MS and BS degrees in Applied Physics and Mathematics from Moscow Institute of Physics and Technology (2001 and 1999). His work emphasizes network-based methodologies for addressing societal challenges such as political communication dynamics, healthcare outcomes, and consumer behavior. Recent projects include optimizing security systems, analyzing influenza transmission via agent-based models, and developing causal inference techniques using observational data. He has also contributed to crowdlearning frameworks for collaborative problem-solving at scale. In 2018, he received the INFORMS Impact Prize for impactful research and the 2017 University at Buffalo Teaching Innovation Award for enhancing student learning through pedagogical innovation. His research has been supported by U.S. Air Force fellowships and published in journals like Socio-Economic Planning Sciences and Nursing Outlook . He has advised students who have won multiple awards, including NSF travel grants and ISE Best Poster Awards. His current projects explore disinformation resilience in social commerce, pandemic modeling, and optimizing multi-modal transportation systems using automated fare data.








