Dr. Jason Monios is a Senior Professor of Maritime Logistics at Kedge Business School in Marseille, France, specializing in operations, supply chain, and information management. His work focuses on the intersection of maritime transport, logistics, and environmental sustainability. Professor Monios' research spans three primary domains: Maritime Transport: Focusing on port system evolution, collaboration in port hinterlands, port governance and policy, and institutional frameworks Multimodal Transport: Examining multimodal corridors, dry ports, development of multimodal terminals, and urban logistics strategies Sustainability: Addressing maritime sustainability, decarbonization policies, green ports, climate change adaptation, and electric/autonomous vehicle integration His recent publications (2022-2025) demonstrate a strong focus on climate change adaptation in port governance, decarbonization of maritime transport, and innovative logistics solutions like urban consolidation centers. His work increasingly examines the intersection of environmental policy, technological innovation, and institutional frameworks in global shipping and logistics. Notable awards and recognitions include: Ranked in the top 2% of logistics researchers worldwide by Stanford/Elsevier (2021 and 2022) Research on climate change adaptation cited in the IPCC report (2022) Professor Monios has led research projects with a total budget exceeding €1 million and has supervised numerous doctoral theses. His work extends beyond academia through collaborations with national and regional transport authorities and technical reports for international organizations including UNCTAD, UN-ECLAC, and the World Bank.
Teddy Mekonnen is an Orlando Bravo Assistant Professor of Economics at Brown University's Department of Economics. Previously, he was a Linde Postdoctoral Fellow at Caltech (California Institute of Technology). He holds a PhD in Economics from Northwestern University (2017) and a BA in Economics with a Mathematics minor from Washington University in St. Louis (2011). His research focuses on information economics and mechanism design, particularly studying informational externalities, incentives for information acquisition/sharing, and their applications to industrial organization and political economy. He also explores decision theory and static/dynamic settings. Teaching responsibilities include courses such as ECON 1110 (Intermediate Microeconomics), ECON 2060 (Microeconomics II), and ECON 2970 (Workshop in Economic Theory). His recent research spans topics like search market efficiency, competition dynamics, and Bayesian comparative statics. Though no explicit grants or awards are listed, his work reflects deep engagement with theoretical and applied economic problems.
Petter N. Kolm serves as a Clinical Professor of Mathematics and Program Director at New York University, with his office located in Warren Weaver Hall (520). He can be contacted at petter.kolm@nyu.edu or 212-998-4855, and holds an editorial board position at the Journal of Portfolio Management. His academic qualifications include: Doctorate in Mathematics from Yale University M.Phil. in Applied Mathematics from the Royal Institute of Technology in Stockholm M.S. in Mathematics from ETH Zurich Dr. Kolm's research centers on quantitative finance, with primary focus areas including quantitative trading strategies, delegated portfolio management, financial econometrics, risk management, and optimal portfolio strategies. His work integrates advanced mathematical modeling with practical investment applications, bridging theoretical frameworks and real-world market dynamics through rigorous empirical analysis. Analysis of his 15 most recent publications reveals consistent emphasis on portfolio optimization techniques—particularly Bayesian methods and the Black-Litterman model—alongside significant contributions to algorithmic trading systems, factor-based equity portfolio construction, and machine learning applications for financial sentiment analysis. His scholarly output demonstrates evolution from foundational portfolio theory toward contemporary computational finance challenges. As Program Director, Dr. Kolm oversees academic programming and likely mentors graduate students in quantitative finance, though specific advisee details are not documented. His prior industry role at Goldman Sachs Asset Management provided direct experience in developing hedge fund strategies, informing his applied research approach. Dr. Kolm's professional trajectory includes significant industry engagement through his tenure in Goldman Sachs' Quantitative Strategies Group, where he developed quantitative investment systems. His current academic leadership position leverages this practical experience to shape quantitative finance education and research at NYU.
Grégoire DANOY is a Researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability, and Trust (SnT) and Head of the Parallel Computing and Optimization Group (PCOG). He specializes in artificial intelligence, with a focus on optimization algorithms, machine learning, and swarm intelligence. His work addresses challenges in cloud computing, high-performance computing, smart mobility, and unmanned autonomous systems like drone swarms. He has authored over 150 publications, including articles in IEEE Transactions and conferences like NeurIPS and GECCO. He currently leads major projects such as UltraBO (€1.019M), ADHOC (€1.291M), and SERENITY (€1.228M), collaborating with institutions in France and Poland. Education: PhD in Computer Science (2008) from École Nationale Supérieure des Mines de Saint-Étienne, Master’s in Computer Science (2004), and Industrial Engineering Degree (2003) from Luxembourg University of Applied Sciences. Research Interests: Developing novel AI techniques for solving large-scale optimization problems, with applications in distributed systems, autonomous robotics, and federated learning. He emphasizes scalable solutions for combinatorial challenges using parallel computing and swarm intelligence. Grants & Projects: Principal Investigator for EU-funded initiatives like ADARS (2021–2024) and FNR PoC/SIMMS (2019–2021). His work bridges academia and industry, with technology transfer projects in autonomous robot swarms. Awards: Recognitions include the Best Student Paper Nomination (2022), IEEE CybConf Best Paper Award (2017), and ACM GECCO nominations (2016, 2009). He serves on the editorial board of Engineering Applications of Artificial Intelligence (EAAI). Labs & Teams: Leads the Parallel Computing and Optimization Group (PCOG), focusing on interdisciplinary research in AI and distributed systems. He also contributes to outreach programs like FNR's Researchers at School.
Mark S. Hoddle is a Professor of Entomology and Biological Control Specialist at the University of California, Riverside (UCR), where he has headed research in his laboratory since 1997. He serves as the Director of the Center for Invasive Species Research and is a Principal Investigator focusing on biological control of invasive pests. His work bridges academic research with practical applications for California agriculture. Education: D.Sc. Zoology (2018), University of Auckland, New Zealand Ph.D. Entomology (1996), University of Massachusetts, Amherst M.S. Zoology (1991), University of Auckland, New Zealand B.S. Zoology (1988), University of Auckland, New Zealand Dr. Hoddle specializes in biological control, the intentional use of host-specific natural enemies to suppress pest populations. His research focuses on identifying pest problems amenable to biological control, locating and releasing natural enemies, and evaluating their impact on pest population growth. He works extensively with invasive species affecting California agriculture, particularly pests of citrus, avocado, palm trees, and other crops. His approach combines field evaluations with laboratory studies of pest and natural enemy biology and behavior. Analysis of Dr. Hoddle's recent publications reveals a strong focus on invasive species threatening California agriculture, particularly the Asian citrus psyllid, red palm weevil, and brown marmorated stink bug. His research integrates field and laboratory approaches, with emphasis on phenology modeling, natural enemy evaluation, and innovative control methods. Much of his work addresses the economic and ecological impacts of invasive pests on specialty crops. Scientific Awards: Entomological Society of America, Pacific Branch Entomology Team Leader Award (2023) UC-ANR Distinguished Service Award for Outstanding Research (2022) Fellow, Entomological Society of America (2018) California Department of Pesticide Regulations IPM Achievement Award for Asian Citrus Psyllid Biocontrol (2018) IOBC Distinguished Scientist of the Year (2015) Multiple awards from the Entomological Society of America (2007, 2012, 2013, 2014) Dr. Hoddle has mentored numerous students and researchers throughout his career, including postdoctoral scholars and specialists who contribute to his laboratory's work on invasive species. His research has been supported by various granting agencies, private institutions, and commodity boards, enabling extensive international projects focused on identifying and implementing biological control solutions for invasive pests. He has facilitated the Harry Scott Smith Scholarship Fund to support graduate students in biological control. Dr. Hoddle directs the Center for Invasive Species Research at UCR and leads a productive laboratory team that includes postdoctoral scholars, specialists, and students. The lab conducts research on multiple invasive species threatening California agriculture and natural ecosystems, with particular emphasis on citrus, avocado, and palm tree pests. Current projects include proactive biological control of the spotted lanternfly and research on invasive palm weevils, avocado pests, and other emerging threats.
Klaus Schmidt is a Professor of Economics at Ludwig Maximilian University of Munich, holding the chair in the Department of Economics within the Faculty of Economics. His research focuses on theoretical and applied aspects of contract theory, game theory, and industrial organization, with significant contributions to understanding venture capital finance, privatization, and fairness in economic behavior. His educational background includes a Ph.D. in Economics from the University of Bonn (1991) with the dissertation "Commitment in Games with Asymmetric Information" and Habilitation (1994) with "Contracts, Competition, and the Theory of Reputation". Early academic support included scholarships from Studienstiftung des Deutschen Volkes (1982-87) and a German Academic Exchange Service grant (1988/89). Professor Schmidt's research centers on contract theory applications across diverse domains. His work on fairness and reciprocity (notably with Ernst Fehr) revolutionized behavioral contract theory, while contributions to venture capital finance and privatization established foundational frameworks for analyzing incomplete contracts in real-world settings. He employs rigorous game-theoretic modeling to address incentive problems in procurement, privatization, and organizational design. His publication record since 1991 reveals consistent focus on contract-theoretic problems, with increasing emphasis on behavioral aspects after 1999. Key thematic clusters include venture capital finance (2002-2003), fairness/reciprocity (1999-2000), and privatization/incomplete contracts (1995-1996), demonstrating evolution from pure theory to policy-relevant applications. Gossen Prize of the German Economic Association (2001) Commerzbank Prize of the Berlin-Brandenburg Academy of Sciences (2001) Teaching Prize of the Bavarian ministry of science (2000) Walter-Adolf-Jörn Prize (1993) German Academic Exchange Service Grant (1988/89) Studienstiftung des Deutschen Volkes Scholarship (1982-87) Professor Schmidt has secured major research funding including German Science Foundation grants for "Incomplete Contracts" (1999-present) and "Venture Capital Finance" (1998-present). His teaching excellence was recognized with Bavaria's highest teaching award (2000), and he maintains active collaboration with leading economists including Ernst Fehr and Monika Schnitzer. While specific student mentorship details aren't documented, his extensive publication record and seminar leadership indicate significant academic supervision.
David M. Higdon is a Professor and Department Head of the Department of Statistics at Virginia Tech within the College of Science. He specializes in Bayesian statistical modeling of environmental and physical systems, focusing on integrating physical observations with computer simulations for prediction and inference. Previously, he spent 14 years at Los Alamos National Laboratory as a scientist and group leader in the Statistical Sciences Group. Education: Ph.D. in Statistics, University of Washington, 1994 M.A. in Mathematics, University of California San Diego, 1989 B.A. in Mathematics, University of California San Diego, 1987 Research Interests: Higdon’s work spans space-time modeling , inverse problems in hydrology and imaging , statistical modeling in ecology and environmental science , and multiscale models . He develops methods for parallel processing in posterior exploration , statistical computing , and Monte Carlo simulations . His research addresses critical challenges in uncertainty quantification (UQ), including climate modeling, nuclear density functional theory, and geophysical imaging. Publications Trends: His recent articles emphasize Bayesian methodologies applied to complex systems, such as climate forecasting, materials science, and cosmology. A recurring theme is the development of emulators and surrogate models to handle computationally intensive simulations. Awards: Fellow of the American Statistical Association Advising & Grants: While no specific advisees are listed, Higdon has contributed to interdisciplinary collaborations in UQ and statistical modeling. His work has been supported by grants from agencies such as the National Science Foundation and Department of Energy. Labs/Teams: He leads the Statistics Department’s efforts in UQ and computational statistics, fostering collaborations across engineering, environmental science, and physics.
Dr. Victoria C. P. Chen is a Professor in the Industrial, Manufacturing, and Systems Engineering (IMSE) department at The University of Texas at Arlington (UTA), where she has served since 2002. She previously held positions at the Georgia Institute of Technology from 1993-2001. Dr. Chen has held several leadership roles at UTA, including Interim Department Chair (2012-2014), Director of the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) (2008-2012, and again from 2017-present), and Director of Doctoral Studies (2019-present). She was also the George & Elizabeth Pickett Professor from 2015-2017 and was inducted into the UT Arlington Academy of Distinguished Teachers in 2019. Dr. Chen is actively involved with INFORMS (Institute for Operations Research and the Management Science), where she currently serves as Secretary on the Executive Board. Dr. Chen earned her B.S. in Mathematical Sciences from The Johns Hopkins University, and her M.S. and Ph.D. in Operations Research and Industrial Engineering from Cornell University. Her academic journey includes visiting professorships at the University of Genoa, Italy, and Iowa State University. Dr. Chen's research utilizes statistical perspectives to create new methodologies for operations research problems appearing in engineering and science. Her expertise includes the design of experiments, statistical modeling, and data mining, particularly for computer experiments and stochastic optimization. Through her statistics-based approach, she has developed computationally-tractable decision-making methods for many high-dimensional complex systems. Her work spans multiple domains including sustainability, energy, water management, healthcare, and law enforcement. Specific application areas include inventory forecasting, airline optimization, water reservoir networks, wastewater treatment, air quality monitoring, green building design, nurse assignment systems, and pain management programs. Her recent publications demonstrate continued innovation in mixed integer programming for electric vehicle charging stations, vacuum ultraviolet spectroscopy prediction, and sustainable building education. Senior Member, Institute for Operations Research and the Management Sciences (INFORMS) (2024) Data Mining Prize (Lifetime Achievement Award), INFORMS Society on Data Mining (2023) College of Engineering Teaching Award, UT Arlington (2021) Third Place Award, C3.ai COVID-19 Grand Challenge (2020) Academy of Distinguished Teachers, University of Texas at Arlington (2019) George & Elizabeth Pickett Professorship (2015-2017) As an educator and mentor, Dr. Chen has advised over 25 doctoral students across diverse research topics in operations research and systems engineering. She has secured substantial research funding from multiple sources including the National Science Foundation (over $1.5 million in active projects), Environmental Protection Agency, National Institute of Justice, and industry partners like Luminant and Dallas-Fort Worth International Airport. Her current research projects focus on decision analytics for sustainable urban environments, optimization for Texas water management, and statistical methods for pain management programs. She has served as Principal Investigator or Co-PI on more than 20 externally funded research projects totaling over $3 million in funding. Dr. Chen co-founded the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) at UTA with Dr. H. W. Corley. This research center brings together faculty and students from multiple disciplines to address complex problems through advanced statistical and optimization methods. She also leads interdisciplinary research teams working on projects related to sustainable infrastructure, energy systems, and healthcare optimization, frequently collaborating with researchers from civil engineering, environmental science, and medical fields.
Dr. Kevin G. Jamieson is a faculty member at the University of Washington , School of Computer Science , with prior affiliations at the University of California, Berkeley (Department of Electrical Engineering and Computer Sciences) and the University of Wisconsin-Madison (Department of Electrical and Computer Engineering). His work spans machine learning, reinforcement learning, bandit algorithms, and robotics. Current university: University of Washington Academic rank: Professor His research focuses on: Bandit algorithms and sequential decision-making Optimization in non-stationary environments Reinforcement learning with real-world applications Multi-agent systems and game theory Efficient data selection for multimodal learning Human-in-the-loop AI systems Recent publications highlight his expertise in pure exploration strategies, robotic manipulation, and bridging simulation-to-reality gaps in RL. He has mentored numerous collaborators, though formal student advising details are not explicitly listed here. No scientific awards are mentioned in the provided data.
Giorgio Ferrari is a Full Professor for Mathematical Finance at the Institute for Mathematical Economics (IMW), Faculty of Economics, Bielefeld University. His research bridges stochastic control theory with applications in economics, finance, actuarial science, and epidemiology. Education: B.Sc. and M.Sc. in Physics and Mathematical Physics from the University of Rome La Sapienza, Ph.D. in Mathematics for Economic-Financial Applications (2012). Academic Appointments: Post-Doctoral Researcher (2012–2015), Substitute Full Professor (2015), Junior Professor (W1) (2016–2017), Associate Professor (2017–2023), and Full Professor (2023–present) at Bielefeld University. Research Interests focus on Singular Stochastic Control , Optimal Stopping , and Stochastic Games , with applications to economic policy, financial markets, and epidemic modeling. His work extends to Mean-Field Games for large-scale strategic interactions and Free-Boundary Problems for investment decision-making under uncertainty. Scientific Contributions include groundbreaking publications in Stochastic Processes and their Applications , Mathematical Finance , and SIAM Journal on Control and Optimization . His research projects, such as the DFG SFB 1283 subproject C4 and the Research Training Group 2865 , address uncertainty in dynamic economies through game-theoretic and stochastic frameworks. Notable Awards: AMASES Best Young Researcher Paper (2014), YITP Research Prize (2017), and multiple research fellowships from the University of Padova. Leadership: Director of the Bielefeld Graduate School in Theoretical Sciences (2023–present) and Principal Investigator in major DFG-funded initiatives.
Erhan Bayraktar is a Professor of Mathematics at the University of Michigan, holding the Susan Smith Chair. He serves as Director of the Quantitative Finance and Risk Management Masters Program, which he established in 2015. His academic career at the University of Michigan spans since 2004, progressing from T. H. Hildebrandt Research Assistant Professor to his current full professorship. Professor Bayraktar earned his Ph.D. from Princeton University in 2004, following dual Bachelor's degrees in Electrical Engineering and Mathematics from Middle East Technical University in Turkey. His academic journey reflects a strong foundation in both theoretical and applied mathematical disciplines. Bayraktar's research focuses on mathematical finance, applied probability, machine learning, mean field games, stochastic analysis, stochastic control, and optimal stopping. His work bridges theoretical mathematics with practical applications in finance and risk management. He has developed sophisticated mathematical frameworks for analyzing complex financial systems, market behaviors, and optimal decision-making under uncertainty. His contributions to mean field games have provided new insights into large-scale interacting systems, while his work on stochastic control has advanced methodologies for optimal decision processes. His publication record demonstrates a consistent trajectory of high-impact research, with recent work focusing on Wasserstein space analysis, graphon particle systems, and applications of machine learning to financial mathematics. His research shows increasing interdisciplinary connections between traditional mathematical finance and modern computational approaches. Susan M. Smith Professorship (2010-present) National Science Foundation CAREER Grant (2010-2016) SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize (2010) Professor Bayraktar has mentored 14 Ph.D. students (13 graduated) and approximately 40 post-doctoral researchers. His students hold prestigious positions in academia and industry, including tenure-track positions at Boston University, University of Colorado, University of Sydney, and University of Toronto. He has secured continuous funding from the National Science Foundation, including the current grant DMS-2507940 (2025-2028) and previous grants totaling over 15 years of continuous NSF support. As Director of the Quantitative Finance and Risk Management Masters Program, Bayraktar has built a robust academic community through the Financial/Actuarial Math seminar series, which hosts about 10 outside speakers annually, and by organizing international workshops in stochastic analysis for finance and insurance in Ann Arbor.
Alexander P. Frankel is the Isidore Brown and Gladys J. Brown Professor of Economics at the University of Chicago Booth School of Business. His research focuses on mechanism design, game theory, and contracting, with applications across various economic domains. Previously, he worked at Yahoo! Research and has published in top economics journals including the American Economic Review and Journal of Political Economy. Education: BS in Mathematics from the University of Chicago BA in Economics from the University of Chicago PhD in Economic Analysis and Policy from Stanford Graduate School of Business Frankel specializes in information economics, mechanism design, and contract theory. His work explores how information structures affect economic outcomes, with applications to delegation, signaling, and strategic communication. He has made significant contributions to understanding how information is designed and used in strategic settings, particularly in areas such as R&D investment, admissions policy, and central banking. Frankel's publication record demonstrates a consistent focus on information design and its applications across diverse contexts. His work spans theoretical developments in signal structures and information hierarchies to practical applications in education policy, corporate decision-making, and monetary policy. The research shows increasing sophistication in modeling information environments and their economic consequences, with recent work addressing contemporary issues like test-optional admissions while maintaining strong theoretical foundations. As a faculty member at Chicago Booth, Frankel teaches Microeconomics (33001) and The Economics of Contracts (33931). His research has received attention in major media outlets including the New York Times, Chicago Tribune, and Freakonomics blog, indicating the broader relevance of his theoretical work to practical economic issues.
Associate Professor at the University of Klagenfurt , affiliated with the Department of Management Control and Strategic Management under the Faculty of Economics and Law . Research focuses on agent-based modeling applied to organizational dynamics , complex systems , and managerial economics . Holds a doctoral degree in Social Sciences and Economics (2012) and venia docendi in Business Economics (2018) . Core faculty member in the Self-Organizing Systems research cluster Academic editor for PLoS ONE and editorial board member for multiple journals Recipient of the 2021 Advancement Award (Humanities/Social Sciences) from Carinthian government Research integrates computational simulation with organizational theory , examining phenomena like decentralized task allocation , incentive mechanisms , and reproducibility in social sciences . Teaching portfolio includes business analytics , management control , and scientific modeling at undergraduate and graduate levels. Recent publications explore organizational resilience , team coordination dynamics , and financial modeling using agent-based simulation techniques. Active participant in international conferences like Social Simulation Conference and European Conference on Operational Research .
Heikki Peura is an Associate Professor in the Department of Information and Service Management at Aalto University School of Business. Previously, he served as an Assistant Professor at Imperial College Business School, Imperial College London. His educational background includes: PhD in Management Science and Operations from London Business School (awarded June 30, 2016) MSc in Engineering Physics and Mathematics from Aalto University, specializing in Systems and Operations Research (awarded April 27, 2010) Professor Peura specializes in analytics methods for understanding and improving firms' operational decisions . His research spans several interconnected domains including revenue management, sustainable energy generation, and supply chain management. He applies advanced analytical techniques to solve complex business problems, particularly focusing on optimization methods and decision-making under uncertainty. His work bridges theoretical operations research with practical business applications, making significant contributions to both academic knowledge and industry practice. Analysis of his recent publications reveals a consistent focus on operational decision-making with applications across multiple sectors. His work demonstrates strong methodological rigor in optimization techniques while addressing practical business challenges in energy markets, supply chain dynamics, and revenue management systems. There's a clear evolution in his research from foundational work in pricing and optimization toward more complex applications involving sustainability, risk management, and strategic decision-making in competitive environments. Professor Peura has been actively involved in teaching operations and supply chain analytics as well as revenue management courses at Aalto University. At Imperial College, he taught courses including Data Structures and Algorithms, and contributed to various Master's programs in Business Analytics and Financial Engineering. His research has established significant connections with industry applications, particularly in energy markets and platform businesses. Professor Peura maintains an active research agenda with several working papers addressing contemporary challenges in non-profit fundraising, decarbonization pathways, and healthcare financing.
Matteo Ploner is a Full Professor at the Department of Economics and Management , University of Trento, and Deputy Coordinator of the Doctoral Programme in Economics and Management under the Doctoral School of Social Sciences. He also serves as Deputy Manager of the Cognitive and Experimental Economics Laboratory (CEEL) at the University of Trento. Academic Roles: Full Professor, Deputy Coordinator (Doctoral School), Deputy Manager (CEEL) Location: Via Inama, 5 - 38122 Trento Contact: matteo.ploner@unitn.it Research Interests: His work focuses on Behavioral Economics , Behavioral Finance , and Experimental Economics , examining human decision-making in economic environments. His research spans topics like cooperation in multicultural societies, information avoidance in climate decisions, algorithmic delegation, and welfare dynamics in advisory markets. Recent Publications highlight experimental analyses of skewness-seeking in finance, cognitive barriers to energy efficiency, and behavioral interventions in low-income contexts. He also explores mental accounting principles and the interplay between trust and innovation through experimental frameworks. Labs & Teams: He is actively involved in the Cognitive and Experimental Economics Laboratory (CEEL) , contributing to experimental market analysis and behavioral research.
