Prof. Ivo J.B.F. Adan is a Full Professor at Eindhoven University of Technology (TU/e), holding chairs in both Industrial Engineering & Innovation Sciences and Mechanical Engineering. His research focuses on stochastic operations research, queueing models, and manufacturing systems design. He has held visiting positions at the University of North Carolina and was part-time professor at the University of Amsterdam (2008–2011). Education: MSc and PhD in Mathematics from TU/e Affiliations: Eurandom Senior Fellow, Beta Research Director, editorial roles at Queueing Systems and Probability in Engineering and Informational Sciences His work addresses warehouse optimization, transportation logistics, and semiconductor manufacturing. Notable achievements include: Developed analytical models for zone picking systems and conveyor networks Advanced understanding of FCFS infinite bipartite matching systems Recipient of multiple best paper awards including the IE&IS Valorization Prize (2023) Current projects include the DigiTwop digital twin-based warehouse optimization initiative and modular construction research. He teaches courses on stochastic modeling, manufacturing systems, and smart industry applications.
Andrew Li is an Associate Professor of Operations Research at Carnegie Mellon University's Tepper School of Business since 2024, previously serving as Assistant Professor since 2018. His research bridges statistics, optimization, and machine learning with applications to healthcare operations and retail management. Current teaching: Optimization, Business Analytics Capstone, and Topics in Optimization and Statistics PhD from MIT's Operations Research Center (2018), BS in Operations Research/Applied Mathematics from Columbia University (2012) Research Focus: Dr. Li develops data-driven decision frameworks for complex systems. Key areas include: Experience-based learning models with fairness constraints (organ allocation) Anomaly detection in low-rank matrices (retail inventory accuracy) Nanoparticle-based diagnostic systems for CAD and Alzheimer's Nonstationary demand forecasting in supply chains Publication Trends: Recent work combines bandit algorithms with healthcare applications (split liver transplants, CAD detection) and retail operations (inventory accuracy). Theoretical contributions include regret-optimal policies and entrywise anomaly detection guarantees. Scientific Honors: INFORMS Nicholson Award (2018) INFORMS Pierskalla Award (2021) NSF CAREER Award (2023) Professional Leadership: Active in INFORMS and CMU committees including MBA Analytics Curriculum, Thompson Award, and ENAiBLE AI-driven retail collaborative co-founder since 2021.
Ye Zhisheng is the Dean’s Chair and Associate Professor in the Department of Industrial Systems Engineering & Management at the National University of Singapore (NUS). His research focuses on reliability engineering, inventory control, emergency response systems, and statistical modeling. He holds a PhD in Industrial and Systems Engineering from NUS, along with a BEng in Material Science and Engineering and a BEco in Economics from Tsinghua University. His work emphasizes practical applications in mission-critical systems, predictive maintenance, and data-driven decision-making. Current research initiatives include optimal maintenance policies for manufacturing systems, degradation analysis of bearings, and federated learning approaches for battery lifecycle prediction. He has pioneered methods for integrating physics-informed neural networks into prognostics and health management (PHM) systems. Key technical contributions span advanced statistical methodologies like sieve estimation for survival data, phase-type distributions modeling, and condition-based maintenance optimization. His interdisciplinary approach bridges operations research, mechanical engineering, and computer science to address complex reliability challenges. Recent projects include resilient consensus-based power grid management and contamination source identification frameworks. Notable collaborations involve developing intelligent cross-domain fault diagnosis systems using transformer networks and advancing the Internet of Federated Things (IoFT) for distributed data analytics. His work has been applied in aerospace, telecommunication infrastructure, and medical emergency response systems.
Tingliang Huang holds concurrent roles as the Amazon Distinguished Professor of Business Analytics at the University of Tennessee's Haslam College of Business and Honorary Professor at the UCL School of Management. He earned his PhD from Northwestern University's Kellogg School of Management. His research focuses on business analytics, AI-driven strategies, supply chain optimization, and behavioral operations, with notable contributions to Marketing Science, Management Science, and Production and Operations Management. Affiliations: Amazon Distinguished Professor, Haslam College of Business, University of Tennessee Honorary Professor, UCL School of Management Former tenured Associate Professor at Boston College's Carroll School of Management Education: PhD in Management, Kellogg School of Management, Northwestern University (2011) M.S. and B.S. from University of Science and Technology of China (USTC) Research Interests: Huang’s work bridges analytics and operations, exploring topics like opaque selling, bounded rationality in consumer decisions, supply chain dynamics, and sustainable operations. He has pioneered frameworks for probabilistic selling and dynamic pricing under uncertainty. His interdisciplinary approach integrates behavioral economics and big data analytics. Publications: Over 20 peer-reviewed articles in top journals, emphasizing service systems, supply chain strategy, and marketing-operations interfaces. Recent work explores AI's societal impacts and algorithmic targeting in vertical markets. Awards: 2025 Vallett Family Outstanding Researcher Award 2018 POMS Wickham Skinner Early Career Award 2015 POMS Best Paper Award Multiple Meritorious Service Awards (M&SOM, Management Science) Editorial Roles: Senior Editor at Production and Operations Management, Associate Editor at Manufacturing & Service Operations Management, Decision Sciences, and others. He also serves on editorial review boards for leading journals. Teaching & Mentorship: Award-winning educator recognized as Carroll School Teaching Star (2021). Advises doctoral students at UCL, UTK, and Chinese institutions, with placements at top schools like George Mason University and USTC. Labs & Teams: Leads the Business Analytics PhD Program at UTK and collaborates on AI ethics research through cross-institutional projects.
Mustafa Akan is an Associate Professor of Operations Management at the Tepper School of Business, Carnegie Mellon University . He holds a Ph.D. in Managerial Economics and Strategy from Northwestern University (2008) and a B.Sc. in Industrial Engineering from Carnegie Mellon University (2004). Research Interests : His work focuses on healthcare operations management , queueing theory , and dynamic pricing strategies . He investigates efficient resource allocation in service systems, equity in organ transplantation, and optimization of remanufacturing processes under uncertainty. His research bridges applied mathematics , computation theory , and business strategy . Article Trends : Recent publications address liver allocation equity (2025), two-sided market pricing (2025), and task allocation in tandem queueing systems (2024). Earlier works explore transplant health disparities (2024), remanufacturing procurement (2023), and fashion product pricing (2021). Common themes include service science , healthcare operations , and policy-driven optimization . Scientific Awards : Best Dissertation Award (INFORMS Aviation Applications Section, 2008) Xerox Faculty Chair (2009) INFORMS Best Paper in Service Science (2009) POMS Healthcare Best Paper Award (2012) Lave-Weil Prize (2013) Gerald L. Thompson Teaching Award (2014) NSF CAREER Award (2014) Mehrotra Research Excellence Award (2024) DEIJ Best Paper Award (2023) Teaching & Grants : He teaches courses like Healthcare Operations , Risk Analytics , and Demand Management & Price Optimization . His NSF CAREER Award (2014) supports research in operational systems. He has served on committees for INFORMS , POMS , and Naval Research Logistics .
Shirin Saeedi Bidokhti is an Assistant Professor at the University of Pennsylvania's School of Engineering and Applied Science with primary appointment in Electrical and Systems Engineering and secondary appointment in Computer and Information Science. She is affiliated with the Warren Center for Network and Data Sciences. She holds M.Sc. and Ph.D. degrees from EPFL and completed postdoctoral work at Stanford and Technical University of Munich. Her research focuses on information theory, networking, data compression, and machine learning. Her recent publications demonstrate strong emphasis on neural compression algorithms, network optimization during the COVID-19 pandemic, and age-of-information theory. Awards include: 2023 IEEE Communications Society & Information Theory Society Joint Paper Award 2021 NSF CAREER Award 2019 NSF-CRII Award Swiss National Science Foundation Fellowships She advises PhD students including Xingran Chen. Current research involves developing data compression algorithms for IoT applications and network strategies for pandemic response.
Paul G Dupuis is the IBM Professor of Applied Mathematics at Brown University. His research focuses on applications of probability theory, stochastic processes, control theory, and numerical methods. He holds affiliations with the American Mathematical Society, Society for Industrial and Applied Mathematics (SIAM), and the Institute for Mathematical Statistics (IMS). His work emphasizes large deviation theory, Markov chain approximations, Monte Carlo simulation, and partial differential equations. Education: Ph.D. in Applied Mathematics from Brown University (1985), M.S. from Northwestern University (1982), and B.S. from Brown University (1981). Research Interests: Control of deterministic and stochastic processes, differential games, numerical methods, operations research, and stochastic processes. His contributions include foundational work on large deviation theory, risk-sensitive control, and queueing networks. Awards: Elected SIAM Fellow (2010), Fellow of the Institute for Mathematical Statistics (2011), IBM Professor of Applied Mathematics (2012), and AMS Fellow (2014). Previously held an NSF Postdoctoral Fellowship (1985-1988). Grants: Current funding from the Army Research Office and National Science Foundation. Key collaborations include work on stochastic approximation, constrained diffusions, and reflected Brownian motion. Teaching: Courses include Operations Research: Probabilistic Models, Information Theory, and Advanced topics in Probability and Stochastic Control.
Philipp Afeche is a Professor of Operations Management and Statistics at the Rotman School of Management, University of Toronto. His research bridges operations and marketing/economics, focusing on revenue management, pricing strategies, and service design in congestion-prone systems like healthcare and transportation. He holds a BA from the University of St. Gallen and MS/PhD degrees from Stanford University. Afeche has been recognized with the 2014 Best Paper Award (MSOM) and the 2018 Roger Martin Teaching Award. Education: BA, University of St. Gallen, Switzerland MS, Stanford University, USA PhD, Stanford University, USA Research Interests: Afeche explores optimization challenges in dynamic service systems, including pricing under uncertainty, strategic customer behavior in queues, and platform design for shared mobility systems. His work integrates queueing theory, game theory, and empirical analysis to address real-world operational inefficiencies in healthcare delivery and transportation networks. Recent studies focus on ride-hailing market mechanisms and bipartite matching systems. Awards: 2014 Best Paper Award, Manufacturing & Service Operations Management 2018 Roger Martin Award for Excellence in Teaching Grants & Editorial Roles: Editor for Management Science and Operations Research, with funding reviews for agencies in Canada, Hong Kong, Israel, and the US. Past chair of the Service Management SIG for MSOM Society. Labs/Teams: Active in Rotman's Operations Management group and collaborates with industry partners on supply chain optimization and revenue management projects.
Haipeng Shen is a Professor of Innovation and Information Management at HKU Business School, The University of Hong Kong, serving as Associate Dean (EMBA and IMBA) and holding the Patrick S C Poon Professorship in Analytics and Innovation. He chairs the Business Analytics and Innovation program and joined HKU in 2015 after previously holding a professorship at the University of North Carolina at Chapel Hill. His academic credentials include: PhD in Statistics, The Wharton School of Business, University of Pennsylvania, 2003 MA in Statistics, The Wharton School of Business, University of Pennsylvania, 2000 BS in Mathematics, School of Mathematical Sciences, Peking University, 1998 Professor Shen's research focuses on data-driven decision making under uncertainty, with expertise spanning big data analytics, business analytics, healthcare analytics, and service engineering. He develops advanced statistical and machine learning methodologies to solve complex operational problems in call centers, optimize stroke care protocols, and enhance financial risk modeling, emphasizing real-time applications in high-stakes environments. Analysis of his recent publications reveals a consistent interdisciplinary approach bridging operations research, statistics, and domain-specific knowledge. His work demonstrates strong methodological innovation in time-series forecasting for service systems, risk assessment frameworks for medical complications, and covariance structure analysis for financial markets, with direct translational impact on business operations and clinical outcomes. His scientific contributions have been recognized with prestigious awards including: Most Influential Publication Award from China Stroke Association (2018) Fellow of the American Statistical Association (2015) Best Advisor of the Year Award from Academy of Asian Business (2018) Elected Member of International Statistical Institute (2015) Cluster Chair for Big Data Analytics at INFORMS International (2015) As an academic leader, Professor Shen has secured significant research funding from organizations including The Xerox Foundation and National Institute on Drug Abuse. He serves as Associate Editor for Management Science, Journal of the American Statistical Association, and Technometrics, while mentoring graduate students in statistical methodology and applied analytics. His current initiatives position HKU Business School at the forefront of healthcare innovation through big data analytics, driving collaborations with medical institutions to transform stroke care and hospital operations in Asia.
David Alan Goldberg is an Associate Professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University, part of Cornell Engineering. He joined Cornell in 2017 and previously held the A. Russel Chandler III Associate Professorship at Georgia Tech’s Industrial and Systems Engineering department. Goldberg earned his Ph.D. in Operations Research from MIT (2011) and a B.S. in Computer Science from Columbia University (2006). Education: B.S. in Computer Science, Columbia University (2006) Ph.D. in Operations Research, MIT (2011) Research Interests: Goldberg’s work focuses on applied probability and stochastic processes, including optimal stopping, inventory and queueing models, combinatorial optimization, and robust optimization. He develops algorithms and insights for complex systems, addressing challenges like the curse of dimensionality. His research spans applications in data science, operations research, and stochastic modeling. Notable contributions include distributionally robust inventory control and high-dimensional decision-making frameworks. Awards and Honors: 2025 Community-Engaged Practice and Innovation Award (David M. Einhorn Center) 2023 Sunny Yau ’72 Teaching Award (Cornell) 2019 INFORMS Applied Probability Society Best Publication Award 2015 NSF CAREER Award Multiple INFORMS Nicholson Student Paper Competitions (First Place, 2019 & 2015) Teaching and Service: Goldberg leads Cornell ORIE’s undergraduate research program, connecting students to real-world applications of OR and data science. He teaches courses in probability modeling, stochastic models, and academic skills for PhD students. He chairs the INFORMS Applied Probability Society and serves on editorial boards for Operations Research and Stochastic Systems . At Cornell, he advises the Undergraduate ORIE Society and directs undergraduate studies in ORIE. Labs & Collaborations: Goldberg’s research integrates theoretical rigor with practical applications, often involving collaborations across disciplines. His work bridges operations research, statistics, and computer science to address modern challenges in inventory systems, queueing networks, and decision-making under uncertainty.
Retsef Levi is the J. Spencer Standish (1945) Professor of Operations Management at the MIT Sloan School of Management, affiliated with the MIT Operations Research Center. He co-directs the Leaders for Global Operations (LGO) Program. His work focuses on data-driven decision models for healthcare systems, supply chain optimization, and risk management. Levi holds a PhD in Operations Research from Cornell University and has led industry collaborations with major hospitals and organizations like the FDA and Walmart Foundation. Education: PhD in Operations Research, Cornell University, 2005 Bachelor’s in Mathematics, Tel-Aviv University, 2001 Research Interests: Levi’s research addresses complex decision-making under uncertainty in healthcare, supply chains, and logistics. Key areas include food safety analytics, risk-based sampling, and predictive modeling for zoonotic diseases. He designs algorithms for inventory control, appointment scheduling, and healthcare resource allocation. Articles Overview: Recent work spans AI-driven epidemiological models, supply chain cybersecurity, and agricultural market interventions. His articles emphasize practical applications of operations research in healthcare and public health. Awards: NSF Career Grant INFORMS Optimization Prize (2008) Wagner Prize (2013) Harold W. Kuhn Award (2016) Advising & Grants: Advised 10 PhD students and 34 master’s students. Led multi-million-dollar projects like the Walmart Foundation initiative for China’s food safety. Active in hospital process optimization and FDA risk management contracts. Labs & Teams: Runs MIT’s Food Supply Chain Analytics and Sensing Initiative, collaborating with global partners on predictive risk tools and healthcare analytics.
Halina Frydman is a Professor in the Department of Statistics and Operations Research at the Leonard N. Stern School of Business, New York University, where she has been a faculty member since 1978. Her academic work bridges statistical theory and real-world applications in finance and labor economics. Institution: New York University School: Leonard N. Stern School of Business Department: Department of Statistics and Operations Research Academic Rank: Professor Email: hf2@stern.nyu.edu Education: Ph.D. in Mathematical Statistics, Columbia University, 1978 M.A. in Mathematical Statistics, Columbia University, 1974 B.S. in Physics and Mathematics, Cooper Union, 1972 Research Interests: Professor Frydman specializes in survival analysis and Markov processes , with a strong focus on their applications in financial modeling and labor market dynamics . Her work explores mixture models of Markov chains to capture heterogeneity in longitudinal data, particularly in the context of corporate credit rating migrations and employment/unemployment transitions. She also contributes to methodological advances in stochastic modeling and statistical inference for time-to-event data. Publication Trends: Her recent research, reflected in reconstructed articles, demonstrates a consistent focus on developing and applying advanced statistical models—particularly survival models, Markov chains, and mixture models—to problems in finance and economics. There is a clear progression toward more complex, data-driven models incorporating Bayesian methods, high-dimensional estimation, and time-varying effects. Scientific Awards: No awards explicitly mentioned in the source text. Advising and Grants: While specific advisees and grant funding are not listed in the available text, Professor Frydman's long-standing research program and publications in premier journals such as the Journal of the American Statistical Association and The Journal of Finance suggest a significant scholarly impact and likely history of research sponsorship. She teaches core courses including Regression & Forecasting Models , Stochastic Processes I , and Stochastic Models in Finance , indicating active engagement in graduate education. Labs and Research Teams: No specific laboratories or research groups are mentioned in the provided content. However, her research aligns with interdisciplinary efforts in financial statistics and econometric modeling, potentially involving collaboration within NYU’s broader quantitative research community.
Prof. Rama Cont is a Statutory Professor of Mathematics at the University of Oxford and a Professorial Fellow at St Hugh's College . He serves as Director of the Centre for Doctoral Training in Mathematics of Random Systems , Faculty Member of the Stochastic Analysis Group , and Senior Research Fellow at the Institute for New Economic Thinking . Additional roles include Director of the Oxford Martin Programme on Systemic Resilience , Principal Investigator at the Oxford Suzhou Centre for Advanced Research , and Editor-in-Chief of Mathematical Finance . His research interests span pathwise methods in stochastic analysis, rough analysis, functional Ito calculus, mathematical modeling in finance, systemic risk, and data-driven decision systems. Recent publications focus on causal transport, rough volatility, and deep residual networks, reflecting his interdisciplinary approach to mathematics and finance. Functional Ito calculus and pathwise integration Rough volatility and financial market dynamics Systemic risk in financial networks Deep learning applications to finance and stochastic processes He has received prestigious awards including the Louis Bachelier Prize , SIAM Fellowship, Royal Society APEX Award, and IMA Fellowship. His editorial roles and seminar leadership underscore his influence in mathematical finance and stochastic analysis.
Lauren K. Williams is the Dwight Parker Robinson Professor of Mathematics at Harvard University and the Sally Starling Seaver Professor at the Radcliffe Institute. Her research focuses on algebraic combinatorics, cluster algebras, and mathematical physics, with notable contributions to the study of the positive Grassmannian, amplituhedron geometry, and integrable systems. She holds affiliations with Harvard’s Department of Mathematics and the Radcliffe Institute for Advanced Study. Her work bridges combinatorics, algebraic geometry, and physics, particularly in understanding geometric structures like the amplituhedron, which encode scattering amplitudes in quantum field theory. Key areas include cluster algebras, Schubert varieties, and applications of combinatorial methods to stochastic processes such as the asymmetric exclusion process (ASEP). Recent activities include organizing conferences on combinatorics, mathematical physics, and the legacy of mathematicians like Richard P. Stanley. Her research often explores connections between discrete structures and continuous systems, with a focus on positivity and geometric positivity principles. Awards and grants are not explicitly listed in the provided texts, but her contributions have been recognized through invitations to major international conferences and leadership in the field. She actively mentors students and postdocs in combinatorics and algebraic geometry.
Gayane Vardoyan is an Assistant Professor at the Manning College of Information and Computer Sciences, University of Massachusetts Amherst. She previously held a permanent Assistant Professor position at QuTech (Quantum Internet Division) and the Faculty of Electrical Engineering, Mathematics, and Computer Science at TU Delft (2022–2024). Her research focuses on quantum networking, particularly developing protocols for entanglement distribution and optimizing quantum systems. Vardoyan earned her B.S. in Electrical Engineering and Computer Sciences from UC Berkeley and her Ph.D. from UMass Amherst under Prof. Don Towsley. She has held postdoctoral and research roles at TU Delft, Inria, and Argonne National Lab. Education: Ph.D., University of Massachusetts Amherst (2017–2021) M.S., University of Massachusetts Amherst (2017) B.S., University of California, Berkeley (2013) Research Interests: Vardoyan’s work addresses challenges in distributed quantum systems, including entanglement distribution algorithms, quantum repeater architectures, and performance analysis of quantum networks. She integrates classical networking techniques with quantum principles to enhance protocol efficiency. Current projects emphasize utility maximization, resource allocation, and optimizing quantum network performance under hardware constraints. Awards: Best Paper Award, Performance 2021 Best-In-Session Presentation Award, INFOCOM 2018 Advising & Grants: Supervises PhD and Master’s students on quantum network design and optimization. Collaborates with industry and academic partners on projects funded by NSF and EU grants. Previously led initiatives at QuTech and co-organized events like the Quantum Software Consortium General Assembly. Labs & Teams: Leads the Distributed Quantum Systems group at UMass, focusing on theoretical and applied research in quantum networking. Engages in cross-disciplinary collaborations with computer science and electrical engineering teams.
