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Yossiri AdulyasakView profile

Professor

• Supply Chain Analytics
• Optimization under Uncertainty
• Large-Scale Optimization
• +4 more

Yossiri Adulyasak is a Full Professor in the Department of Logistics and Operations Management at HEC Montréal, holding the Canada Research Chair in Supply Chain Analytics. He is also an Adjunct Professor at the University of Montreal's Department of Computer Science and Operations Research and a scientific advisor at IVADO Labs. His research focuses on supply chain optimization, data-driven decision-making, and robust optimization under uncertainty, with applications in retail, transportation, and humanitarian logistics. Education: PhD in Operations and Logistics Management (HEC Montréal), Master of Civil Engineering in Transportation Engineering (Chulalongkorn University). Research Interests : Supply chain analytics, large-scale optimization algorithms, stochastic programming, machine learning integration, and resilient supply chain design. His work emphasizes practical applications in logistics networks, inventory management, and dynamic resource allocation under uncertainty. Recent Articles : Focus on AI-driven retail analytics, robust optimization for lot-sizing and facility location, and humanitarian logistics. Key topics include pandemic response strategies, electric vehicle routing, and drone delivery systems. Awards : Recipient of the 2021 Prix de recherche Chenelière Éducation/Gaëtan Morin for excellence in scientific publications. Supervision : Directed 3 PhD dissertations and over 40 master’s and project supervisions, collaborating with companies like Bombardier, Pratt & Whitney Canada, and JDA. Active in industrial partnerships for supply chain optimization solutions. Lab/Teams : Member of GERAD (Group for Research in Decision Analysis) and IVADO Labs, contributing to cross-disciplinary AI and operations research initiatives.

Zuyi LiView profile

Professor

• Power Systems
• Smart Grid
• Renewable Energy
• +5 more

Zuyi Li is a Professor and Grainger Endowed Chair in Electrical and Power Engineering at Illinois Institute of Technology (IIT), affiliated with the Armour College of Engineering and the Department of Electrical and Computer Engineering . He serves as Associate Director of the Robert W. Galvin Center for Electricity Innovation , leading research in smart grid technologies and renewable energy integration. His research focuses on power systems optimization , smart grid security , renewable energy integration , and cyber-physical resilience . Key areas include large-scale optimization for grid operations, market design for electricity systems, and mitigating cyber threats in critical infrastructure. Dr. Li’s awards include the 2025 IEEE Fellow , 2011 ECEDHA Innovation Award , and multiple best paper awards. His work bridges theory and practice, addressing challenges like renewable energy penetration and grid reliability under cyberattacks. He leads projects such as the Bronzeville Community Microgrid and IIT Campus Microgrid , advancing decentralized energy systems. His research also explores machine learning for grid analytics, data-driven demand response, and carbon market integration.

Sadegh SoudjaniView profile

Researcher

• Cyber-Physical Systems
• Formal Verification
• Stochastic Hybrid Systems
• +5 more

Sadegh Soudjani is currently a Senior Research Group Leader at the Max Planck Institute for Software Systems (MPI-SWS) since December 2023. Prior to this, he served as the director of the AMBER group and a Reader (full professor) in Cyber-Physical Systems at Newcastle University, United Kingdom. His research focuses on developing integrated environments for programming, verifying, and designing high-performance, scalable cyber-physical systems in uncertain physical environments. Education: BSc in Pure Mathematics BSc in Electrical Engineering MSc in Control Engineering PhD in Systems & Control Research Interests: Formal synthesis, abstraction, and verification of complex dynamical systems under probabilistic temporal specifications. Applications include energy networks, autonomous systems, smart grids, transportation systems, and systems biology. Projects: SymAware (€4 million EIC-funded), CodeCPS (EPSRC-funded), MoVeS, Safe-CPS, AMBI. His work bridges control theory and formal methods, with a focus on stochastic hybrid systems. Scientific Awards: EPSRC New Investigator Award (2021, £484k) DISC Best PhD Thesis Award (2015) QEST Best Paper Award (2018) Newcastle Teaching Award (2020) Best Repeatability Prize at IFAC ADHS'21 Advising & Grants: Supervised 4 PhD students jointly with Max Planck Institute and Ludwig Maximillian University. Secured €4 million for SymAware and €728k for 7 PhD studentships. Led collaborative projects with institutions like KTH, Uppsala, TU Eindhoven, Siemens, and Netherlands Aerospace Centre. Labs & Teams: Currently leads the AMBER group at MPI-SWS, previously directed AMBER group at Newcastle University. Organized international workshops including V2CPS, SNR, VARS, and ARCH (stochastic category).

Andrea BellèView profile

Researcher

• Energy Systems Engineering
• Resilience of Critical Infrastructures
• Optimization under Uncertainty
• +3 more

Dr. Andrea Bellè is a Researcher at the Research Center for Energy Networks (FEN-ETH) at ETH Zürich since January 2024. His work focuses on optimizing resilience of critical infrastructures and energy systems through advanced modeling and optimization techniques. He holds a Ph.D. in Complex Systems Engineering from University of Paris-Saclay (2022), an M.S. in Nuclear Engineering from KTH Royal Institute of Technology and University of Paris-Saclay (2019), and a B.S. in Energy Engineering from University of Padua (2017). His current research emphasizes the RESAIL project collaboration with Swiss Federal Railways (SBB), developing strategies for energy transition and reliable demand fulfillment. Prior to ETH, he was a research engineer at Thales Group working on defense and space projects. Research interests include: resilience modeling of interdependent systems, optimization under uncertainty, and energy system design. His publications span applications in power networks, satellite constellations, and molten salt reactors. Key technical areas are multi-objective optimization, distributionally robust approaches, and vulnerability analysis. Advising: Currently focuses on postdoctoral research with no listed advisees. Grant activity includes FEN-funded initiatives. Lab affiliations: Primarily works within FEN-ETH's Research Center for Energy Networks and collaborates with industry partners like SBB on applied energy transition projects.

Stefano MoretView profile

Lecturer

• Energy Systems
• Uncertainty Modeling
• Strategic Decision-Making
• +3 more

Dr. Stefano Moret is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zurich, leading the RIMES (Robust Integrated Modeling for Energy and Engineering Systems) project funded by the Swiss National Science Foundation (SNSF). His research focuses on energy systems modeling, particularly addressing uncertainty in strategic decision-making and policy design. He collaborates with the Energy & Process Systems Engineering (EPSE) laboratory under Prof. André Bardow. Stefano holds a BSc and MSc in Industrial and Mechanical Engineering from the University of Padova (Italy) and a PhD in Energy from EPFL. Previously, he was an SNSF Early Postdoc Mobility Fellow at Imperial College London. His research group develops methods to embed uncertainty into energy systems models, aiming to support industry and public-sector decision-making. Key areas include low-regret strategies for energy transitions, decarbonization pathways, and resilience planning against climate variability. Notable awards include the SNSF Ambizione grant and Early Postdoc Mobility Fellowship. His work spans energy transition scenarios, healthcare scheduling optimization during pandemics, and geothermal energy integration. Labs/Teams: RIMES Group (ETH Zurich), collaborating with EPSE Lab. Ongoing projects include the Ambizione-funded strategic energy modeling initiative and geothermal energy integration studies.

Shahab Shariat TorbaghanView profile

Assistant Professor

• Sustainable Energy
• Operations Research
• Optimization
• +6 more

Dr. Shahab Shariat Torbaghan is a tenure-track Assistant Professor of Smart Grids in Urban Energy and Water Systems at Wageningen University & Research. His expertise spans mathematical models, operations research, decision support systems, optimization, stochastic processes, sustainable energy, green infrastructure, machine learning, and energy transition. He holds a B.Sc. in Electrical Engineering from the University of Tehran (2008), an M.Sc. from Chalmers University of Technology (2010), and a Ph.D. from Delft University of Technology (2015). He previously worked as a Postdoctoral Fellow at Eindhoven University of Technology (2015–2017) and as a Research Scientist at VITO/EnergyVille in Belgium (2017–2020). His research focuses on applying sequential decision-making under uncertainty to water and energy infrastructure modeling and optimization, leveraging convex optimization, combinatorial optimization, and machine learning (deep learning, reinforcement learning). He collaborates with the City of Amsterdam, AMS Institute, water companies, and academic partners like HZ University of Applied Sciences and Utrecht University. He participates in European H2020 projects such as Magnitude, FHP, and Coordinate. Dr. Torbaghan teaches courses including 'Sustainability Transitions,' 'Data-Driven Environmental Modelling and Optimization,' and 'Managing Urban Environmental Infrastructure.' His work intersects urban infrastructure transitions, energy storage arbitrage, hydrogen production, and market-based mechanisms for flexibility provision. He is a Senior Member of IEEE and part of INFORMS and the GO-P2P community.

Debora Daniela EscobarView profile

Assistant Professor

• Risk Management
• Cyber Risk
• Sustainable Finance and Insurance
• +5 more

Dr. Debora Daniela Escobar is an Assistant Professor at the School of Mathematical & Computer Sciences in Heriot-Watt University , specializing in Actuarial Mathematics & Statistics . Prior to this role, she served as an Assistant Professorial Lecturer at the London School of Economics (LSE), where she directed the BSc Actuarial Science program, and held a Fellowship after completing her PhD at the University of Vienna. Education: PhD in Statistics and Operations Research (University of Vienna), MSc in Statistical and Computational Data Analytics (Complutense University of Madrid, Technical University of Madrid), BSc in Mathematics (Complutense University of Madrid) Her research focuses on dynamic programming , robust optimization , and model ambiguity , with applications in risk management , operational resilience , and sustainable finance and insurance . She has applied these methodologies to problems like thermal power plant valuation and insurance pricing under uncertainty. Dr. Escobar's recent publications demonstrate expertise in computational management science and operations research , particularly in addressing multi-scale optimization and robustness in actuarial models . Her work has earned citations in Scopus (4 and 13 for the two main papers) and institutional downloads.

Johannes WieselView profile

Assistant Professor

• Mathematical Finance
• Mathematical Statistics
• Optimal Transport
• +3 more

Johannes Wiesel is an Assistant Professor in the Department of Mathematical Sciences at Carnegie Mellon University (CMU), affiliated with the Mellon College of Science. Previously, he held the same rank at Columbia University's Department of Statistics (2020–2023). He earned his Ph.D. in Mathematics from the University of Oxford under Jan Obloj's supervision. His research bridges mathematical finance, statistics, and optimal transport theory, focusing on robust financial strategies and risk assessment through market-derived information. He serves on the editorial board of Dependence Modelling and has been recognized with the Bruti-Liberaty Prize (2021) and an NSF grant. Research interests emphasize optimal transport of stochastic processes, robust statistical methods, and machine learning applications. He explores connections between these fields to develop universal tools for time-consistent trading and risk evaluation. Recent talks include engagements at TU Berlin, the SIAM Conference on Mathematical Finance, and the Vienna Congress on Mathematical Finance. Education: Ph.D., University of Oxford Awards: Bruti-Liberaty Prize 2021, NSF Grant DMS-2345556 Professional Activities: Editorial Board Member of Dependence Modelling

Mohammad FattahiView profile

Assistant Professor

• Operations Research
• Stochastic Programming
• Data-Driven Decision Making
• +4 more

Dr. Mohammad Fattahi is an Assistant Professor in Business Analytics at Northumbria University’s Newcastle Business School, specializing in the Department of Marketing, Operations, and Systems. He holds a Ph.D. in Industrial Engineering from Amirkabir University of Technology (Tehran Polytechnic) and has been a visiting researcher at the University of Southern Denmark. His research focuses on Operations Research, Stochastic Programming, and Data-Driven Decision Making, with applications in Healthcare, Energy, and Supply Chain Management. He has contributed to over 30 peer-reviewed publications in top journals such as Production and Operations Management and European Journal of Operational Research . Dr. Fattahi’s work addresses complex systems under uncertainty, including healthcare districting, resilient supply chains, and energy transitions. His teaching spans courses like Probability Theory, Supply Chain Management, and Linear Algebra. He actively collaborates on sustainable development goals, particularly in education and responsible consumption. Key research themes include optimizing surgical care under uncertainty, mitigating pandemic impacts through resource sharing, and integrating environmental and social considerations into supply chain design. His methodologies emphasize robust stochastic optimization and data-driven frameworks.

Dionysis KalogeriasView profile

Assistant Professor

• Machine Learning
• Reinforcement Learning
• Optimization
• +5 more

Dionysis Kalogerias is an Assistant Professor in the Department of Electrical & Computer Engineering at Yale University. He holds a PhD from Rutgers University and previously served as an Assistant Professor at Michigan State University, with postdoctoral positions at the University of Pennsylvania and Princeton University. His research focuses on machine learning, optimization, risk-aware decision-making, and their applications in autonomous systems, wireless communications, and financial risk management. Education: PhD in Electrical & Computer Engineering, Rutgers University MEng/MSc in Electrical & Computer Engineering, University of Patras, Greece Research Interests: Dr. Kalogerias explores mathematical optimization, statistical learning, and decision-making under uncertainty, with emphasis on applications in resource allocation, autonomous systems, and financial risk management. His work bridges theory and practice, addressing challenges in wireless networks, robust control, and algorithmic trading. Awards: ICASSP Best Paper Award (2020) Rutgers SOE Outstanding Graduate Student Award (2017) Rutgers ECE Graduate Program Academic Achievement Award (2017) Advising & Grants: Recipient of NSF grant for reliable wireless autonomous networks Advising PhD student Baturay Saglam at Yale Collaborations: Active involvement in federated learning marketplaces (FEDSTR) and decentralized AI protocols.

Stephen WrightView profile

Professor

• Computational Optimization
• Machine Learning
• Data Science
• +3 more

Stephen Wright is a Professor in the Department of Computer Sciences at the University of Wisconsin-Madison, serving as Department Chair from 2023-2025. He previously held roles at Argonne National Laboratory (1990-2001) and the University of Chicago (2000-2001). His research focuses on computational optimization, with applications in data science, machine learning, and engineering. He co-authored seminal books such as Numerical Optimization (with J. Nocedal) and Optimization for Data Analysis (with B. Recht). Wright leads the Wisconsin Institute for Discovery's research initiatives and has developed widely-used optimization software like PCx and SpaRSA . Key awards include the 2024 George B. Dantzig Prize, 2020 Khachiyan Prize, and SIAM Fellow status since 2011. He has served as editor-in-chief of the SIAM Journal on Optimization and Mathematical Programming, Series B . His teaching includes courses on nonlinear optimization (CS726) and introductory optimization (CS524). Wright’s work bridges theory and practice, emphasizing scalable algorithms and interdisciplinary applications.

• Operations Research
• Optimization
• Fuzzy Systems
• +4 more

Shu-Cherng Fang is a prominent academic in the fields of Operations Research , Optimization , and Machine Learning . His work spans theoretical advancements and practical applications in Mathematical programming Supply chain network design Fuzzy systems Support vector machines Algorithm development . While specific institutional affiliations and academic rank are not explicitly stated in the provided text, his extensive publication record in high-impact journals indicates a faculty-level role. Research interests include optimization under uncertainty , supply chain logistics , and kernel-free machine learning models . Key trends in recent articles focus on fourth-party logistics (4PL) network design distributionally robust optimization for machine learning mathematical modeling of customer behavior stochastic programming . Co-authors frequently include Min Huang, Zhibin Deng, Jian Luo, and Wenxun Xing, reflecting sustained collaborations. Articles emphasize interdisciplinary approaches combining fuzzy logic , game theory , and computational geometry to solve complex decision-making problems.

Sean HoldenView profile

Professor

• Automated Theorem Proving
• Machine Learning
• Artificial Intelligence
• +5 more

Sean Holden is a Professor in the Department of Computer Science and Technology at the University of Cambridge, affiliated with The Computer Laboratory. He holds positions at both the University and Trinity College. His research focuses on automated theorem proving, machine learning, and their intersections with formal methods and AI. He leads the development of the Connect++ theorem prover, which won the Best Newcomer award at CASC 2024. His work spans areas including connection calculus, graph neural networks, and reinforcement learning in games like Mahjong. Holden's academic contributions include over 50 publications in top venues such as IJCNN, ICPRAM, and Journal of Automated Reasoning. Notable works include foundational research on Bayesian methods in theorem proving and machine learning applications in bioinformatics and medical imaging. He serves as an Associate Editor for IEEE Transactions on Artificial Intelligence since 2023. His research group explores topics like protein graph embeddings, medical AI (e.g., breast cancer classification via MUGI-MRI), and the integration of machine learning with automated reasoning systems. Collaborations span institutions including Trinity College and international partners in computational biology and AI.

Anne BarrosView profile

Researcher

• Resilience Engineering
• Optimization
• Critical Infrastructure Networks
• +4 more

Anne Barros is a prominent researcher specializing in resilience engineering, optimization of critical infrastructure networks, and stochastic modeling. Her work focuses on enhancing the reliability and resilience of power systems, telecommunications networks, and interdependent infrastructures under uncertainty. She has contributed significantly to the development of maintenance strategies, condition-based monitoring systems, and decision-making frameworks under stochastic environments. Key research areas: Resilience quantification, stochastic optimization, infrastructure interdependencies, 5G communication reliability, and climate change impact modeling. Co-authored over 50 publications including articles in top journals and conference proceedings. Her recent work emphasizes the integration of reinforcement learning techniques with maintenance optimization for industrial systems. Collaborations include projects on pandemic control strategies, smart grid resilience, and disaster recovery modeling. Barros' research has been applied to real-world systems such as offshore wind farms, railway-power networks, and critical infrastructure coordination frameworks. She actively participates in international conferences and publishes across interdisciplinary domains including reliability engineering, operations research, and systems engineering.

Laurent El GhaouiView profile

Professor

• Robust Optimization
• Decision-making under Uncertainty
• Statistical Estimation
• +5 more

Laurent El Ghaoui is a Professor in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. He joined the faculty in 1999 after serving as Acting Associate Professor. Prior to Berkeley, he held academic positions at the École Nationale Supérieure de Techniques Avancées (Paris) and part-time roles at École Polytechnique and Université de Paris-I. His research focuses on robust optimization, decision-making under uncertainty, statistical estimation, and applications in air traffic management, bioinformatics, and finance. He is affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Center for New Media (BCNM), and CLIMB. El Ghaoui holds a Ph.D. in Aeronautics and Astronautics from Stanford University (1990) and a B.S. in Mathematics from École Polytechnique (1985). He has been recognized with prestigious awards, including the SIAM Optimization Prize (2008) and the CNRS Bronze Medal (1998). His work integrates optimization theory with real-world challenges, such as robust filtering, dynamic routing, and financial risk modeling. His research spans machine learning, control systems, and stochastic processes. Notable contributions include algorithms for sparse graphical models, distributionally robust optimization, and air traffic flow scheduling. He has collaborated on projects involving microarray data analysis and MEMS design. His academic career combines teaching and industry experience, including a leave at SAC Capital Management (2003–2006).