Esra Buyuktahtakin Toy
1145 Perry Street
Blacksburg, VA 24061
I am looking for highly motivated prospective Ph.D. students with a strong background in optimization and machine learning (ML) and who have an interest in contributing to cutting-edge research at the intersection of ML/AI, operations research, game theory, supply chain & logistics, healthcare, and environment & sustainability. Interested candidates should apply to the ISE Ph.D. program at Virginia Tech as soon as possible and are also strongly encouraged to contact Dr. Esra Buyuktahtakin Toy at esratoy@vt.edu with a CV and other application materials. See the related Graduate Assistantship position announcement here.
Research Areas
Main Area: Operations Research, Integrated Optimization and Machine Learning, Decision Making under Uncertainty
- Methodology: Risk-averse Stochastic Mixed-Integer Programming, Combinatorial Optimization, Machine Learning, Deep Learning, Reinforcement Learning, and Algorithms
- Applications: Emerging Applications in Healthcare, Epidemiological, Forestry and Agricultural Systems and Supply Chain & Logistics: tackling infectious diseases that ravage the human body, such as the COVID-19, Ebola virus disease, and the HIV, and invasive species outbreaks that create havoc on forests and agriculture, such as the emerald ash borer (EAB) and Sericea Lespedeza in North America
- Ph.D. in Industrial and Systems Engineering, University of Florida, 2009
- M.S. in Management Science, Lehigh University, 2007
- M.S. in Industrial Engineering, Bilkent University, 2005
Dr. Esra Büyüktahtakιn Toy is a Full Professor of Operations Research in the Grado Department of Industrial and Systems Engineering at Virginia Tech, where she directs the Systems Optimization and Machine Learning Lab (SysOptiMaL). Her research focuses on multi-stage stochastic mixed-integer programming (M-SMIP) and pushes the boundaries of integrated machine learning (ML), optimization, and artificial intelligence (AI) for high-stakes decision-making under uncertainty. She develops scalable, intelligent algorithms by combining optimization theory, deep learning, and advanced computational tools to address large-scale combinatorial problems. At SysOptiMaL, Dr. Toy mentors students engaged in cutting-edge research on AI-driven multi-stage optimization, uniting theoretical rigor with innovative algorithm design and computational analysis. She has graduated 8 Ph.D. students now in leading academic and industry roles and currently advises eight more.
Dr. Toy is widely recognized for her methodological innovations in stochastic and combinatorial optimization, such scenario dominance algorithms, as well as her leadership in transdisciplinary applications of operations research. She is also known for pioneering the integration of epidemic modeling with logistics and for being among the first to transfORm machine learning and data-driven methods to optimize decision-making. Her research portfolio spans a wide range of application domains, including health systems, environmental sustainability, and biosecurity. Recent work has focused on resource allocation under uncertainty and the control of epidemic diseases and invasive processes in natural and managed systems. These include decision-support models for managing complex disruptions, such as disease outbreaks (e.g., COVID-19, Ebola, and Dengue), 3-D cancer growth, ecological threats (e.g., emerald ash borer and zebra mussels), and national security challenges (e.g., submarine detection and surveillance in open-ocean warfare scenarios).
She is the recipient of the 2016 NSF CAREER Award and has secured funding from major agencies including the NSF, USDA, DOD, U.S. Forest Service, Office of Naval Research (ONR), the Minnesota Aquatic Invasive Species Research Center (MAISRC), the Virginia Department of Forestry, and the 4-VA Collaborative Research Program with a total award nearly $3 million in external research funding.
She has authored 45 peer-reviewed publications, including 23 in A or A-ranked journals* (ABDC Journal Quality List), and has received five INFORMS Best Publication Awards. Her work has been featured in ISE Magazine, the INFORMS Computing Society Newsletter, U.S. Forest Service communications, and other professional outlets for its broad impact on optimization and decision science.
She has served as President of the INFORMS Junior Faculty Interest Group (JFIG) and currently serves as an Associate Editor for the INFORMS Journal on Computing. Dr. Toy was also a member of the Steering Committee for the nationwide 2024 NSF ENG CAREER Award Workshop.