Peter Kondor is an Assistant Professor affiliated with the London School of Economics & Political Science (LSE) and Central European University (CEU). His research focuses on finance, asset pricing, liquidity risk, market microstructure, and arbitrage dynamics. Research Interests: Asset pricing with heterogeneous agents Liquidity risk and intermediary capital Over-the-counter market structures Behavioral finance and sentiment analysis Global financial cycles and investment waves Information diffusion and market stability Key Publications Trends: 2011-2025: Explores causal inference in asset pricing, hedge fund impacts on idiosyncratic risk, and liquidity risk dynamics. 2018: Investigates arbitrage capital and liquidity risk in global markets. 2020-2025: Analyzes rational sentiments, narrative momentum, and aggregate earnings.
Jeff Linderoth is the Harvey D. Spangler Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on large-scale numerical optimization, mixed-integer nonlinear programming, and stochastic programming, with applications in energy systems, global routing, and industrial processes. Education: BS in General Engineering (highest honors) from University of Illinois at Urbana-Champaign, MS in Operations Research from Georgia Institute of Technology, PhD in Industrial Engineering from Georgia Institute of Technology. Linderoth's work addresses theoretical and applied challenges in optimization, including developing algorithms for mixed-integer programming, analyzing knapsack polytopes, and creating tools like the Minotaur optimization toolkit. His recent publications explore integer programming techniques for subspace clustering, complementarity constraints, and customized coverage instrumentation. Selected trends in his research include advancements in stochastic programming, orbital branching for symmetric integer programs, and congestion analysis in power systems. His group contributes to optimization software and data-driven libraries like MIPLIB. Scientific Award: Harvey D. Spangler Professor.
Roel Leus is a full professor at KU Leuven's Faculty of Economics and Business (FEB), part of the Operations Research and Statistics Research Group (ORSTAT). He holds roles such as Program Director for the Business Engineering programs and Chairman of the KU Leuven Advisory Committee for the Chinese Region. He earned his PhD in Applied Economics from KU Leuven in 2003, focusing on project planning under uncertainty. His research emphasizes operations research and management, particularly scheduling, project planning, and decision-making under uncertainty. Education: PhD in Applied Economics (KU Leuven, 2003); Master's in Business Engineering (Handelsingenieur, KU Leuven, 1998). He has held academic positions since 2003, including adjunct professorships at Beijing Jiaotong University. His administrative roles include heading ORSTAT research group (2012–2016) and program directorships. Research Interests: Sequencing and scheduling, project planning under uncertainty, discrete optimization, and practical quantitative decision support. He has supervised 12 graduated PhD students as primary supervisor and contributed to numerous publications in top journals like INFORMS Journal on Computing and European Journal of Operational Research. Teaching: Courses include 'Introduction to Operations Research,' 'Operations Research,' and 'Applications of Operations Research.' He coordinates master's theses in Data Science and Business Analytics, focusing on practical optimization problems. Grants and Projects: Acquired over €2 million in research funding from private companies, the National Bank of Belgium, and KU Leuven. His work spans satellite scheduling, supply chain management, and cross-docking logistics. Labs/Teams: Active in ORSTAT, collaborating on projects like drone-assisted delivery and robust scheduling algorithms. His research bridges theoretical advancements with real-world applications in logistics, manufacturing, and aerospace.
Tamon Stephen is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. His research focuses on operations research, with an emphasis on combinatorial optimization, algorithms, discrete geometry, and computational biology. He holds a Ph.D. in Mathematics from the University of Michigan (2002). His work often bridges theoretical and computational aspects, addressing interdisciplinary applications. Stephen is affiliated with the Centre for Operations Research and Decision Sciences (CORDS) and has contributed to software tools for hypergraph transversals and colorful linear programming. He has taught courses such as Math 208W (Introduction to Operations Research) and has advised projects in metabolic network analysis and scheduling optimization. His office is located at the Surrey campus (SRYC 2886). Key research collaborations include studies on firefighter scheduling, nurse rostering, and metabolic pathway analysis. His methodologies often leverage algorithm design, polytope theory, and discrete mathematics. Stephen actively participates in academic service, organizing seminars and contributing to conferences such as the West Coast Optimization Meeting. His work emphasizes practical applications of theoretical results, with a focus on solving real-world optimization challenges.
Christiane Barz is a Professor of Mathematics at the University of Zurich's Institute for Business Administration since 2016. Previously, she held academic roles at the UCLA Anderson School of Management, the Chicago Booth School of Business, and the Technical University (TU) Berlin. Her research focuses on stochastic dynamic systems, Markov decision processes, and their applications in revenue management. She emphasizes making mathematical tools accessible and practical for real-world problem-solving, particularly in optimizing decision-making under uncertainty. Education includes a degree in industrial engineering and a doctorate from the University of Karlsruhe (TH), Germany. Her career path includes postdoctoral research at the University of Chicago's Booth School of Business and roles as an Assistant Professor at UCLA. She combines academic excellence with balancing family life, advocating for gender equity in STEM fields. Her research explores risk-sensitive decision-making frameworks, dynamic pricing models for transportation and healthcare, and optimizing resource allocation in complex systems. Recent work includes applications in FlixBus, air cargo networks, and improving patient admission scheduling in hospitals. Barz's teaching philosophy prioritizes demystifying mathematics for students, encouraging critical engagement rather than fear of complexity. She collaborates with industry partners to apply operations research methods to real-world challenges, emphasizing both theoretical rigor and practical relevance.
Krishna Jagannathan is a full-time Professor in the Department of Electrical Engineering at the Indian Institute of Technology Madras (IIT Madras), India. He specializes in stochastic modeling, communication networks, information theory, and queuing theory. He obtained his B.Tech from IIT Madras in 2004, followed by S.M. and Ph.D. degrees from MIT in 2006 and 2010, respectively. After post-doctoral positions at Caltech and MIT, he joined IIT Madras in 2011. Education: B.Tech in Electrical Engineering, IIT Madras (2004) S.M. in Electrical Engineering and Computer Science, MIT (2006) Ph.D. in Electrical Engineering and Computer Science, MIT (2010) Research Interests: His research focuses on stochastic modeling and analysis of communication networks , information theory , and queuing theory . He has made significant contributions to understanding network performance, resource allocation, and risk-aware decision-making in complex systems. He leads the Networks and Stochastic Systems lab at IIT Madras, mentoring a large cohort of Ph.D. and M.S. students working on cutting-edge problems in networking, optimization, and stochastic systems. Scientific Awards: Best Paper Award at WiOpt 2013, Tsukuba, Japan Young Faculty Recognition Award for Excellence in Teaching and Research, IIT Madras (2014) Teaching & Mentorship: He has taught a wide range of courses including Probability Foundations , Stochastic Modeling and Queuing Theory , Convex Optimization , and Signals & Systems , consistently receiving high teaching evaluations. He has supervised over 15 Ph.D. and M.S. students to completion and continues to guide several active researchers.
Joshua Ignatius is a Professor of Business Analytics at the Aston Business School, part of the College of Business and Social Sciences at Aston University. He focuses on research areas including supply chain analytics, prescriptive analytics, electronic commerce, and operations management. His work often addresses challenges such as information asymmetry in supply chains, user recommender systems, and logistics optimization. He is currently accepting PhD students in topics like Information Asymmetry in Supply Chains, User Recommender Systems, and Supply Chain Analytics. His research interests are centered on leveraging data-driven approaches to improve decision-making in supply chain and operational contexts. This includes studying disruption risk management, dynamic data modeling, and the integration of AI in cloud services. He also explores strategic decisions in e-commerce logistics, customer segmentation strategies, and environmental sustainability. Recent publications highlight his contributions to supply chain resilience, optimal security in cloud computing, and sustainable manufacturing processes. For instance, his 2025 work on supply chain network viability addresses disruption risks through dynamic data strategies. Another key area is the analysis of customer behavior in product upgrades, utilizing online review data to inform quality differentiation strategies. Dr. Ignatius has collaborated on projects involving platform information sharing, manufacturer encroachment, and logistics sourcing for e-commerce firms. His research often bridges theoretical frameworks with real-world applications, emphasizing practical solutions for operational challenges. He holds a strong record of supervising PhD students and guiding projects that combine academic rigor with industry relevance. His work frequently appears in leading journals such as the European Journal of Operational Research and Journal of Operations Management.
Dr. Shirley Coleman is a distinguished Professor at Newcastle University Business School, specializing in the application of statistical methods to business and industrial problems. With over two decades of academic contributions, she has established herself as a leading expert in statistics, data science, and quality management within industrial contexts. Her research interests span several interconnected domains: Statistics, Data Science, Business Analytics, Quality Management, Six Sigma methodologies, Kansei Engineering (which integrates emotional design with product development), Industrial Statistics, Design of Experiments, Predictive Maintenance, and Customer Lifetime Value analysis. Coleman's work consistently bridges theoretical statistical concepts with practical business applications across diverse sectors including healthcare, manufacturing, facilities management, and digital marketing. Analysis of her recent publications reveals a strong focus on the evolving role of statistics in the digital age, particularly examining how statistical expertise contributes to AI development, Industry 4.0 initiatives, and data-driven business transformation. Her work demonstrates increasing emphasis on customer analytics, predictive maintenance modeling, and the strategic implementation of data science in small and medium enterprises. Coleman's publications frequently address methodological challenges while maintaining strong practical relevance for industry practitioners. Throughout her career, Coleman has been actively involved with the European Network for Business and Industrial Statistics (ENBIS), contributing to the development and dissemination of statistical methods in business contexts. Her collaborative approach is evident in numerous co-authored publications across disciplines, demonstrating her ability to work effectively with researchers from diverse fields including engineering, healthcare, and business management. Her advisory work appears focused on helping organizations implement statistical thinking in business processes, with particular attention to small and medium enterprises seeking to leverage data analytics for competitive advantage. Though specific grant information isn't detailed in the available publications, her extensive industry-focused research suggests significant engagement with practical business problems and industry partnerships. Dr. Coleman has made substantial contributions to the field through her leadership in professional organizations, particularly ENBIS, where she has helped shape the discourse around industrial statistics and their business applications. Her work on Kansei Engineering demonstrates innovative approaches to integrating human factors with statistical methods for product development.
Elina Rönnberg is a Professor and Deputy Head of Department at the Department of Mathematics, Linköping University, where she leads research in discrete optimisation and intelligent decision-making. Her work bridges theoretical method development and real-world applications in sectors such as healthcare, aviation, mining, and transportation. She is actively involved in the Wallenberg AI, Autonomous Systems and Software Program (WASP) and has collaborated with industry leaders like Saab and Scania. Her research focuses on advanced optimisation techniques including Dantzig-Wolfe decomposition, Lagrangian relaxation, column generation, branch-and-price, and logic-based Benders decomposition. She also explores hybrid methods combining mathematical programming with constraint programming and machine learning. Applications span nurse rostering, electric vehicle routing, aircraft arrival scheduling, and underground mine planning. Recent publications highlight a strong trend toward integrating AI and machine learning—particularly graph neural networks—with classical optimisation frameworks to accelerate solution methods. Her work emphasizes practical impact, robustness, and scalability in solving complex scheduling and resource allocation problems. Nurse Rostering with Strategic Planning of Skills for Sick-Leave Robustness (2024) Pricing for the EVRPTW with Piecewise Linear Charging (2024) Speeding Up Logic-Based Benders Decomposition with Graph Neural Networks (2024) Elina supervises several PhD students and has co-supervised doctoral research at international institutions including Makarere University (Uganda) and the University of Exeter (UK). She has contributed to applied projects through student theses in collaboration with Scania and Saab, focusing on electric vehicle routing and search-and-rescue optimisation. She previously served as a Specialist in Optimisation at Saab Aeronautics (2014–2020) and co-founded Schemagi, a scheduling tool aimed at improving quality in healthcare. She teaches courses such as Introduction to Optimization (TAOP07) and Project - Applied Mathematics (TATA62). Her research group, 'Mathematics and algorithms for intelligent decision-making,' operates within the Division of Applied Mathematics (TIMA) at the Department of Mathematics. The team develops decision support tools that enhance efficiency and sustainability in complex systems, particularly under the growing demands of electrification and digitalisation in transport and logistics.
Banu Lokman is a Professor of Operational Research (OR) at the University of Portsmouth, serving as Associate Head (Research and Innovation) in the School of Organisations, Systems and People within the Faculty of Business & Law. She leads the Centre for Innovative and Sustainable Finance and contributes to the Centre for Operational Research & Logistics. Her expertise spans multi-criteria decision-making, optimization, and their applications in healthcare and sustainability. She holds editorial roles at OMEGA and the IMA Journal of Management Mathematics and organizes the NATCOR MCDM courses. Previously, she served as Deputy Director of CORL (2011–2024), Secretary of the International MCDM Society, and Board Member of INFORMS MCDM Section. She currently chairs the INFORMS MCDM Section as President-elect/Vice-President. Education: BSc, MSc, and PhD in Industrial Engineering from Middle East Technical University (METU, Turkey), followed by postdoctoral research at Aalto University (Finland). She taught at METU (2014–2019) and held visiting roles at Aalto University. Research Interests: Focuses on developing optimization methods for multi-criteria decision problems, particularly in healthcare (e.g., optimizing prostate biopsy decisions with Portsmouth NHS Trust) and sustainability. Her work emphasizes algorithms for nondominated set representation, robust efficiency analysis, and cluster ensemble methods. Key Awards: Bernard Roy Award (2022) for outstanding contributions to Multiple Criteria Decision Aiding, and Young Researcher Award (2015). Advising & Grants: Leads a healthcare-related PhD project and contributes to projects like the Social Investment Fund collaboration with Waltham Forest Council. She actively supervises students and participates in research initiatives on supply networks and data control systems. Labs & Teams: Engaged with interdisciplinary teams in operational research and logistics, particularly in applying OR to real-world challenges such as energy market optimization and MRO supply networks.
Stefano Leonardi is a Full Professor in the Department of Computer, Control and Management Engineering Antonio Ruberti at Sapienza Università di Roma. His research focuses on Algorithm Theory, Algorithms and Data Science, and Economics and Computation. He leads the ERC Advanced Grant project AMDROMA, exploring algorithmic mechanisms for online markets. He has held roles as Conference Chair for STOC 2021, WWW 2015, and FUN 2018, and coordinates the Sapienza School of Advanced Studies (2016-2018). His work spans approximation algorithms, online algorithms, and mechanism design. Awards include the ERC Advanced Grant and EATCS Fellowship. His research interests emphasize foundational algorithmic problems in web-based markets, leveraging rigorous design and large-scale data analysis. Recent projects include ALGADIMAR (PRIN 2019-2022) for digital market algorithms. He chairs the Highlights of Algorithms conference series and serves on program committees for top venues like EC, ICALP, and SODA. His lab focuses on web algorithmics and data mining, addressing challenges in online labor markets and fair division. Leonardi's academic contributions include over 100 publications, with recent work on fair algorithms, prophet inequalities, and mechanism design in auctions. He has pioneered methods for submodular optimization, online learning, and multi-agent systems. Grants and awards reflect his leadership in bridging theory with real-world applications, particularly in digital economies. Grants: ERC Advanced Grant (2018-2023), PRIN ALGADIMAR (2019-2022) Leadership: Chair of ACM STOC 2021, WWW 2015, and 9th FUN Conference Labs/Teams: Laboratory on Web Algorithmics and Data Mining Key Projects: AMDROMA (algorithmic mechanisms), ALGADIMAR (digital markets)
Xiaoming Li is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Delaware , focusing on compiler optimization, GPU computing, and hardware-software interaction. His work bridges machine learning with code generation to enhance program efficiency. B.S. and M.E. from Nanjing University (1998, 2001) Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (2006) Research interests include: Compiler optimizations for static and dynamic code transformation Machine learning-driven code generation techniques FFT algorithms for sparse and hybrid systems Non-traditional compilers for SAT solvers and virtual machines GPU acceleration for large-scale computational problems His publications span 15 years , emphasizing: FFT optimization across GPU/CPU architectures Compiler techniques for heterogeneous systems Adaptive scheduling and error resilience Integration of empirical and model-driven approaches Notable awards: NSF CAREER Award (2008) Best Paper Award at ADAPT Workshop (2013) Advising highlights: Current students: Ryan Taylor, Sha Li, Shuo Chen, Yuanfang Chen, Chao Yang, Chaoyu Chen Graduates: Liang Gu (FFT Libraries), Jakob Siegel (GPGPU Frameworks), Murat Bolat (Context-Aware Compilation)
Professor Daniel Oron is affiliated with the University of Sydney, where he joined in 2004 after completing his PhD in Operations Research at the Hebrew University of Jerusalem. His research focuses on Combinatorial Optimization, particularly Scheduling Theory, addressing challenges like batch scheduling with setups, customer delivery models, and scheduling under deteriorating conditions. He teaches courses such as Quantitative Business Analysis, Management Science, and Business Analytics Honours. His editorial role includes serving on the board of the Journal of Industrial & Management Optimization . Recent research contributions span multi-agent scheduling, energy recharging in scheduling, and coupled task optimization. He advises two current PhD students: Johnson (Two-agent scheduling problems) and Renjie Yu (Multi-agent scheduling with parallel batching). Publications highlight advancements in scheduling algorithms, resource allocation, and optimization under constraints. Notable works include minimizing late jobs with step-learning models and analyzing parameterized complexity in single-machine scheduling.
Gauthier Gidel is an Associate Professor at the Department of Computer Science and Operations Research (DIRO) within the Faculty of Arts and Science at Université de Montréal, where he also holds the prestigious Canada CIFAR AI Chair position. He is a core faculty member of Mila, Quebec's AI research institute, and maintains active research collaborations with leading institutions. His academic journey includes a PhD in Computer Science under the supervision of Simon Lacoste-Julien, with internships at Sierra, ElementAI, and DeepMind during his doctoral studies. Dr. Gidel's research spans multiple critical areas in machine learning, with particular emphasis on generative modeling , adversarial machine learning , and variational inequalities for machine learning. His work explores the intersection of optimization theory and practical AI systems, focusing on challenges like LLM safety alignment, multi-agent cooperation, and robustness against adversarial attacks. He is particularly known for his contributions to understanding the theoretical foundations of generative adversarial networks through variational inequality frameworks. His recent publications reveal a strong trend toward addressing critical challenges in large language model safety and alignment, with numerous 2024-2025 papers focusing on adversarial robustness, safety evaluation methodologies, and alignment techniques for LLMs. Simultaneously, his foundational work continues in optimization theory, particularly in variational inequalities and performative prediction, demonstrating his dual focus on practical AI safety concerns and theoretical machine learning foundations. Canada CIFAR AI Chair Core member of Mila Organizer of popular NeurIPS workshops on smooth games Co-founder of the ICLR blog post track Dr. Gidel actively supervises an extensive research group with approximately 10 current graduate students and numerous alumni who have secured positions at leading institutions including Inria Lyon, Oxford, and industry research labs. His research is supported by multiple substantial grants from CRSNG, MITACS, and IVADO, including the prestigious CRSNG Discovery Grant program and MITACS Acceleration Québec projects focused on fraud detection in music streaming and conditional generation. His laboratory maintains strong connections with both academic and industry partners, fostering a collaborative environment focused on advancing AI safety and theoretical understanding.
Professor Barak Weiss is a distinguished faculty member in the School of Mathematical Sciences at Tel Aviv University's Faculty of Exact Sciences. His research focuses on the intersection of dynamical systems, number theory, and geometry, particularly in the areas of homogeneous dynamics, ergodic theory, and Diophantine approximation. Professor Weiss has made significant contributions to the understanding of translation surfaces, lattice orbits, and the dynamics of flows on homogeneous spaces. His work often bridges pure mathematics with applications in number theory and geometry, revealing profound connections between seemingly disparate fields. His research on horocycle dynamics, measure rigidity for fractal carpets, and the classification of cut-and-project sets has advanced our understanding of geometric structures and their dynamical properties. His recent publications (2023-2025) demonstrate a strong focus on equidistribution phenomena, statistical properties of dynamical systems, and the application of homogeneous dynamics to problems in geometric number theory. A notable trend in his work is the interplay between geometric structures and their arithmetic properties, particularly in the context of Diophantine approximation. Professor Weiss actively organizes the "Homogeneous Dynamics and Applications" seminar at Tel Aviv University, which has been running continuously since at least 2014 with detailed schedules available through 2025. This seminar serves as a hub for cutting-edge research discussions, featuring both local and international speakers working on dynamical systems and related areas. He teaches advanced courses in analysis and supervises graduate students, with recent teaching assignments including Real Analysis for summer semester 2025. His office is located in Schreiber building, room 329, and his regular office hours are Tuesdays from 15:00-16:00.
