Jan de Gier is a Professor at the School of Mathematics and Statistics, The University of Melbourne . He is also the Founding Director of MATRIX , Australia’s residential research institute in the mathematical sciences, and a former Deputy Director and Chief Investigator in the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) . Additionally, he co-founded the Australian and New Zealand Association for Mathematical Physics (ANZAMP) in 2011 and served as its inaugural Chair. His research focuses on solvable lattice models at the intersection of mathematical physics and statistical mechanics . Key areas include the application of quantum integrability , algebraic structures like the Yang-Baxter equation, Hecke algebras, and quantum groups, as well as analytical methods such as complex analysis and elliptic curves. His work bridges pure and applied mathematics through connections between enumerative combinatorics , representation theory , and real-world phenomena like traffic flow modeling via exclusion processes . The 15 most recent articles reflect his expertise in integrable systems , non-equilibrium statistical mechanics , and algebraic combinatorics . Topics span Macdonald polynomials , stochastic duality , quantum spin chains , and traffic modeling , with methodologies involving matrix product forms , exact solutions , and critical phenomena analysis. He has contributed to editorial efforts through the AustMS Gazette and MATRIX Annals, and has been involved in public science communication via opinion pieces on mathematics funding and applications. His work emphasizes the importance of fundamental research in driving technological innovation, as highlighted in media articles discussing pi calculation , zero-knowledge proofs , and mathematics education .
Leonardo Chamorro is a Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign (UIUC), with affiliations in Earth Science and Environmental Change, Aerospace Engineering, and Civil and Environmental Engineering. His research focuses on fluid dynamics, renewable energy systems, and turbulence modeling. He holds a Ph.D. in Civil Engineering from the University of Minnesota (2010) and has held academic positions at UIUC since 2013, advancing to Full Professor in 2024. Chamorro's work spans experimental and theoretical investigations of wind and hydrokinetic energy, geophysical flows, and particle dynamics. His research group, the Renewable Energy & Turbulent Environment Group (RE-TE-G), explores topics like tidal flow multifractality, vortex dynamics, and bio-inspired robotics. Key achievements include Nature and Lab on a Chip cover articles, and contributions to turbulence modeling for tidal energy systems. He has received awards such as the Best Paper Award in Energies (2018) and recognition for pandemic-related research (2021). His editorial roles include associate editorships at journals like Journal of Renewable and Sustainable Energy and Frontiers in Energy Research . Chamorro has supervised numerous graduate students and postdocs, contributing to over 150 peer-reviewed publications since 2009.
Geoff Pleiss is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC), affiliated with CAIDA's AIM-SI cluster. He is also a Canada CIFAR AI Chair and faculty member at the Vector Institute. His research bridges deep learning and probabilistic modeling, focusing on uncertainty quantification, Bayesian optimization, Gaussian processes, and ensemble methods. Pleiss earned his PhD in Computer Science from Cornell University (2020), followed by a postdoc at Columbia University. He holds multiple awards, including the AISTATS Top Reviewer and NeurIPS recognitions. His work emphasizes scalable algorithms and open-source contributions, such as the GPyTorch library. Pleiss advises students in Computer Science and Statistics, including Donney Fan (PhD), Tim G. Zhou (MSc), and others. He teaches advanced courses like STAT 547U (Deep Learning Theory) and STAT 520P (Bayesian Optimization). Grants include NSERC Discovery and New Frontiers in Research funding. Pleiss collaborates on interdisciplinary projects, such as astrophysical discovery via machine learning, and actively participates in academic service and outreach. Education: PhD in Computer Science, Cornell University (2020) MSc in Computer Science, Cornell University (2018) BSc in Engineering (Computing with Applied Mathematics), Olin College (2013) Key Research Themes: Uncertainty-aware decision-making with neural networks Scalable Gaussian processes and Bayesian optimization Ensemble methods and their theoretical limitations Recent Grants: NSERC Discovery Grant (2024) New Frontiers in Research Fund (2025, co-PI) His publications span foundational theory to applied machine learning, with over 14,500 citations. He actively mentors students through research internships and advises on open-source software development. Pleiss frequently presents at top conferences and collaborates with industry partners like Microsoft and ASAPP.
Christopher O'Donnell is a Professor of Econometrics at the University of Queensland's School of Economics. He holds a PhD from the University of Sydney and has held academic leadership roles including Director of the Centre for Efficiency and Productivity Analysis. His research focuses on productivity and efficiency analysis, econometric methods, and their applications in agriculture, public policy, and environmental economics. Education: PhD (University of Sydney), MCom (University of New South Wales), BAgEc (Hons) (University of New England). Research interests include economic and statistical methods for measuring productivity changes, stochastic frontier analysis, and metafrontier frameworks. He has authored/co-authored three books and over 80 journal articles, with notable contributions in the American Journal of Agricultural Economics , Journal of Econometrics , and European Journal of Operational Research . Scientific awards include being a Distinguished Fellow of the Australian Agricultural and Resource Economics Society. His work has been applied in sectors like healthcare, fisheries, and public utilities through collaborations with organizations such as the World Bank and Asian Productivity Organization. Key projects include measuring agricultural productivity in China, analyzing hospital efficiency, and evaluating climate impacts on farming. Grants include studies on productivity measurement in Australian universities and Northern Grains Region farms. Labs/Teams: Former Director of the Centre for Efficiency and Productivity Analysis, collaborating with global institutions on productivity benchmarking and policy analysis.
Professor Yongbo Xiao serves at Tsinghua University's School of Economics and Management, Department of Management Science and Engineering. His academic journey includes a BEng in Management Information Systems (2000), Master/PhD in Management Science and Engineering (2006), and postdoctoral research in applied economics at Tsinghua University. 2000: BEng in Management Information Systems 2006: Master/PhD in Management Science and Engineering 2006-2008: Postdoctoral Fellow in Applied Economics His research spans revenue management, pricing strategies, operations/supply chain management, and service systems. Recent work explores live-streaming e-commerce dynamics, supply chain resilience, and platform co-opetition models. Articles appear in top journals including Operations Research , Production and Operations Management , and Naval Research Logistics . Notable awards include National Natural Science Foundation Outstanding Young Scholars Fund, Changjiang Scholar Young Scholars recognition, and multiple Tsinghua University teaching/research honors. He serves as Associate Editor-in-Chief for Naval Research Logistics and Executive Editor for Journal of Systems Science and Systems Engineering . 2024: China Aviation Association Second Prize 2023: Huawei Collaborative Innovation Award 2022-2024: Tsinghua EMBA/Executive Education Teaching Excellence Professor Xiao teaches undergraduate Operations Research, Master's-level Operations Research & Optimization, and MBA courses including Data Models & Decisions, Operations Management, and ESG Frontier Exploration. His work bridges theoretical operations research with practical applications in digital commerce and supply chain innovation.
Saurabh Amin is a Professor in the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT), where he also serves as the Edmund K. Turner Professor and Undergraduate Officer. He is a Principal Investigator at the Laboratory of Information and Decision Systems and holds affiliations with the Operations Research Center and the Center for Computational Science and Engineering. His educational background includes: B.Tech. 2002, Indian Institute of Technology (IIT) Roorkee M.S. 2004, University of Texas (UT) Austin Ph.D. 2011, University of California (UC) Berkeley Saurabh Amin's research focuses on the design and control of infrastructure systems using game theory and optimization in networks. His work spans three main areas: resilient network control, information systems and incentive design, and optimal resource allocation in large-scale infrastructure systems. By concentrating on critical infrastructure domains including highway transportation, electric power distribution, and urban water networks, his research develops innovative theory and tools to enhance system performance against both stochastic and adversarial disruptions. His approach involves modeling cyber-physical interactions in infrastructures to assess vulnerabilities, developing detection and response tools for failures at various scales, and designing economic incentive schemes that improve aggregate public good while accounting for dependencies and private information among strategic entities. Amin's work bridges mathematical systems theory with practical civil engineering applications, creating a rigorous theoretical foundation for infrastructure resilience that addresses diverse failure mechanisms from natural disasters to deliberate malicious actions. His recent publications demonstrate a strong focus on decarbonization of energy systems, resilient infrastructure planning under climate uncertainty, optimization methods for complex networked systems, and game-theoretic approaches to sustainable infrastructure management. His work increasingly integrates artificial intelligence and machine learning techniques with traditional control theory to address contemporary challenges in infrastructure resilience and sustainability. The research shows a clear trajectory toward addressing climate change impacts on infrastructure systems while maintaining economic efficiency and operational reliability. Professor Amin has received numerous prestigious awards and honors: Common Ground Excellence in Teaching Award, 2025 HSCC Test-of-Time Award, 2024 MIT CEE, Distinguished Service and Leadership Award, 2023 Samuel M. Seegal Prize (SoE) – inspiring students in pursuing and achieving excellence, 2022 Earll M. Murman for Excellence in Undergraduate Advising, 2022 C3.ai Digital Transformation Institute Research Award, 2020 MIT, Ole Madsen Mentoring Award, 2020 MIT, Energy Initiative Research Award, 2020 National Academy of Engineering, China-America Frontiers of Engineering Symposium speaker, 2019 MIT, Robert N. Noyce Career Development Professor, 2015-2018 Google Faculty Research Award, 2015 National Science Foundation CAREER Award, 2015 Siebel Energy Institute Research Award, 2015 MIT, Solomon Buchsbaum AT&T Research Fund Award, 2012 Professor Amin has been actively involved in significant research projects including the C3.ai DTI project on Causal Reasoning for Real-Time Attack Identification in Cyber-Physical Systems and another on Learning in Routing Games for Sustainable Electromobility. He serves as the chief scientist on multi-institutional NSF grants, including the $9 million Foundations of Resilient Cyber-Physical Systems (CPS) project. His teaching portfolio includes courses such as 1.008 Engineering for a Sustainable World, 1.104 Sensing and Intelligent Systems, 1.020 Engineering Sustainability: Analysis and Design, and 1.208 Resilient Networks. As Undergraduate Officer, he plays a key role in shaping the educational experience for civil and environmental engineering students at MIT. Professor Amin leads the Resilient Infrastructure Networks Lab at MIT, where his team develops theoretical foundations and practical tools for infrastructure resilience. The lab focuses on the intersection of control theory, game theory, and optimization applied to cyber-physical infrastructure systems. Current research directions include pandemic-resilient urban mobility and hurricane-resilient smart grid operations, reflecting the lab's commitment to addressing pressing societal challenges through rigorous systems engineering approaches.
Rohit Kannan is an Assistant Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. He holds a Ph.D. and M.S. in Chemical Engineering from MIT and a B.Tech. from IIT Madras. His research focuses on integrating machine learning with global optimization and optimization under uncertainty, emphasizing energy systems applications. Previous roles include postdoc positions at Los Alamos National Laboratory and the Wisconsin Institute for Discovery. Education: Ph.D., Chemical Engineering, Massachusetts Institute of Technology, 2018 M.S., Chemical Engineering Practice, MIT, 2014 B.Tech., Chemical Engineering, IIT Madras, 2012 Research Interests: Global optimization, optimization under uncertainty, computational optimization, energy systems, and machine learning integration. Recent Highlights: Recipient of the Excellence in Teaching Spotlight Award (2024) Lead researcher in stochastic optimization and energy systems (e.g., hybrid polygeneration systems) Developed algorithms for chance-constrained nonlinear programs and distributionally robust optimization Service & Leadership: Elected Vice-Chair of Global Optimization, INFORMS Optimization Society (2025–2026) Reviewer for top journals like Operations Research and Mathematical Programming Advisor to ISE InclusiveVT and Graduate Admissions Committee Labs & Collaborations: Directs a research group advancing optimization and machine learning for energy and engineering systems. Active in interdisciplinary projects with LANL and UW-Madison.
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.
Tim Cohen is an Associate Professor of Physics at the University of Oregon, with affiliations at CERN and EPFL's Lausanne Theory Physics Laboratory. He is based at the Institute for Fundamental Science within the Department of Physics at the University of Oregon's College of Arts and Sciences. His research focuses on theoretical particle physics, particularly exploring phenomena beyond the Standard Model. Dr. Cohen's research interests center on particle physics beyond the Standard Model, with specific expertise in Large Hadron Collider phenomenology, effective field theory, electroweak naturalness, and dark matter. His work bridges theoretical frameworks with experimental possibilities at major particle physics facilities. His research program encompasses both theoretical developments in quantum field theory and practical applications to collider physics and cosmology. Analysis of his recent publications reveals a strong focus on effective field theory applications, de Sitter space physics, and dark sector phenomenology. His work demonstrates sophisticated mathematical approaches to problems in quantum field theory while maintaining connections to observable phenomena at particle colliders and in cosmological settings. He frequently collaborates with researchers across institutions including CERN, EPFL, and various US universities. Dr. Cohen serves as a senior researcher with active roles at multiple institutions, contributing to major collaborative efforts such as the Snowmass community planning process for particle physics. His work appears in leading journals including Journal of High Energy Physics, Physical Review D, and Physics Letters B, demonstrating consistent productivity and impact in the field. His research group operates within the Institute for Fundamental Science at the University of Oregon, with additional connections to theoretical physics groups at CERN and EPFL. This international collaboration network enables him to work at the intersection of theoretical developments and experimental frontiers in particle physics.
Albert G. Assaf is a Professor and Hadelman Family Faculty Fellow in the Department of Hospitality & Tourism Management at the Isenberg School of Management, University of Massachusetts-Amherst . He holds editorial roles in multiple journals including Editor-in-Chief of Tourism Economics and Associate Editor positions in Journal of Hospitality and Tourism Research and International Journal of Hospitality Management . His education includes a PhD in Managerial Economics (University of Western Sydney, 2007), alongside graduate diplomas in Quantitative Methods and Mathematical Science. Prior roles include Assistant and Associate Professor positions at UMass Amherst and Victoria University-Australia. Research focuses on applied economics, statistics, tourism/transport economics, and revenue management . His work integrates Bayesian methods, econometrics, and operations research to analyze hospitality industry dynamics, strategic management, and performance measurement. Recipient of prestigious awards including the Richard M. & Nancy S. Kelleher Teacher Award (2018-2019), Thea Sinclair Award (2016), and multiple Dean Research Excellence Awards. His publications emphasize methodological innovation in tourism and hospitality research. Professional service includes editorial leadership across 10+ journals and academic governance roles. Teaching expertise spans managerial economics, applied statistics, and strategic management courses.
Di Bu is an Associate Professor in the Department of Applied Finance at Macquarie University, leading the Macquarie University FinTech and Banking Research Centre. He holds a PhD in Finance from the University of Queensland (2015). His research focuses on FinTech innovations, climate finance, household finance, and behavioral finance, with an emphasis on embedding sustainability into financial systems. He has secured over AUD 4 million in research funding through ARC Linkage and Discovery projects, focusing on AI-driven credit assessments, Open Banking, ESG analytics, and climate resilience. Education PhD in Finance, University of Queensland (2015) Research Interests Di's work explores belief formation in financial decisions, sustainable lending practices, and climate adaptation tools. He pioneers projects such as AI credit scoring systems, behavioral interventions for sustainable investing, and digital platforms for climate resilience. His interdisciplinary approach bridges industry, government, and academia to address financial and environmental challenges. Projects & Funding AUD 4M+ in grants including two ARC Linkage and one ARC Discovery projects Current initiatives: Greenwashing detection, ESG rating divergence analysis, and climate-resilient finance platforms Labs/Teams Director of the FinTech & Banking Research Centre and affiliated with Data Horizons Research Centre and Frontier AI Research Centre at Macquarie University.
Dan A. Iancu is an Associate Professor of Operations, Information & Technology at Stanford Graduate School of Business, affiliated with the Emmett Interdisciplinary Program in Environment and Resources (E-IPER). He holds a BS from Yale, SM from Harvard, and PhD from MIT. His research bridges operations, finance, and sustainability, focusing on dynamic optimization under uncertainty and its applications in supply chains, FinTech, and healthcare. Notable work includes designing financing solutions for global supply chains and promoting ethical analytics. Education: BS in Electrical Engineering (Yale, 2004), SM in Engineering (Harvard, 2006), PhD in Operations Research (MIT, 2010). Professional experience includes a Goldstine Fellowship at IBM Research and teaching roles at Wharton and INSEAD. He has won multiple awards including INFORMS Optimization Society (2009) and JFIG (2013) prizes. Research emphasizes fairness and environmental impact, with recent projects on sustainable palm oil production and climate impacts of digital supply chains. His work appears in Management Science, Operations Research, and Mathematics of Operations Research. Awards include Srivani Faculty Scholar (2023–24) and teaching commendations at Harvard, MIT, and INSEAD. He advises on multi-disciplinary projects involving engineering, environmental science, and business students.
Professor Valentyn Panchenko is a leading academic in Economics at the UNSW Business School, specializing in advanced econometric methodologies and financial modeling. Holding a PhD from the University of Amsterdam and an MPhil from the Tinbergen Institute, his research bridges theoretical econometrics with real-world financial applications, emphasizing big data analysis, network structures, and dependence modeling in economic systems. His expertise spans financial econometrics, time series analysis, non-parametric statistics, and agent-based economic simulations. He focuses on Granger causality, model evaluation, structural economic modeling, and bounded rationality with heterogeneous agents. His work has secured significant grants including ARC Discovery Projects and DECRA fellowships, enabling cutting-edge research on market dynamics and economic interactions. Professor Panchenko's publications appear in top-tier journals like the Journal of Econometric Theory, AEJ: Micro, Journal of Economic Dynamics & Control, and Journal of Banking & Finance. His methodological contributions include novel approaches to copula-based forecasting, nonlinear causality testing, and evolutionary learning models in strategic economic environments. While specific student advising details aren't provided, his research leadership demonstrates sustained impact across econometric theory, financial markets, and experimental economics.
Christopher John O'Donnell is a distinguished Professor at the School of Economics, University of Queensland, Australia, where he holds a dual affiliation (50% each) with both the main School of Economics and the Centre for Efficiency and Productivity Analysis (CEPA). His research primarily focuses on efficiency and productivity analysis across various sectors including agriculture, fisheries, public services, and healthcare. As a leading scholar in his field, he has published extensively in top-tier economics and operations research journals and is recognized as being among the top 5% of authors globally according to multiple citation metrics. O'Donnell's research interests span several interconnected domains: efficiency analysis, productivity measurement, agricultural economics, econometrics, state-contingent production frontiers, and metafrontier analysis. His work often bridges theoretical methodology with practical applications, particularly in estimating efficiency and productivity changes under various constraints and uncertainties. He has developed innovative approaches for measuring productivity in public service providers, agricultural sectors, and healthcare institutions, with particular attention to how weather, climate change, and demand uncertainty affect performance metrics. His research output demonstrates consistent productivity, with publications spanning from the 1990s to the present, including significant contributions in the last five years. O'Donnell frequently collaborates with researchers internationally, particularly with scholars from Australia, Europe, and Asia, reflecting the global relevance of his work. His publications appear in leading journals such as the American Journal of Agricultural Economics, Journal of Productivity Analysis, European Journal of Operational Research, and Agricultural and Applied Economics journals. Ranked among top 5% authors by citation metrics (Number of Citations) Ranked among top 5% authors by citation metrics (Number of Citations, Discounted by Citation Age) Ranked among top 5% authors by citation metrics (Number of Citations, Weighted by Number of Authors) Ranked among top 5% authors by citation metrics (Number of Citations, Weighted by Number of Authors, Discounted by Citation Age) Ranked among top 5% authors by citation metrics (Euclidian citation score) O'Donnell has supervised numerous graduate students, as evidenced by his 'Record of graduates' noted in his RePEc profile. His research has been supported by various institutions, particularly focusing on agricultural productivity, public sector efficiency, and resource economics. He has contributed significantly to methodological developments in productivity measurement, including nonparametric approaches and metafrontier frameworks that allow for cross-technology comparisons. As a core member of the Centre for Efficiency and Productivity Analysis (CEPA) at the University of Queensland, O'Donnell contributes to one of the world's leading research centers in efficiency and productivity analysis. His work has practical applications for policymakers in agriculture, fisheries management, healthcare, and public service delivery, helping organizations measure and improve their performance in increasingly complex economic environments.
Jianhua Zhang is a Professor of Computer Science and founding deputy head of the AI Lab at the Department of Computer Science, OsloMet - Oslo Metropolitan University, Norway. He holds affiliations with the Faculty of Technology, Art and Design. His career includes roles as Scientific Director at Vekia (France), Head of Machine Learning Lab, and Professorships at East China University of Science and Technology and Beijing University of Technology. He has held visiting positions at TU Berlin, TU Dresden, and the University of Catania. Educations: PhD in Electrical Engineering and Information Sciences (Ruhr University Bochum, 2005), Postdoctoral Research at the University of Sheffield (2005-2006). Research focuses on artificial intelligence, computational intelligence, cognitive human-machine systems, neuroergonomics, affective computing, and AI-driven neuroergonomics. Applications span engineering, biomedicine, finance, and business. He has led over 20 large-scale projects and published extensively (4 books, 13 chapters, ~200 papers). Leadership roles include Chair of IFAC Technical Committee on Human-Machine Systems (2017-2023), Vice Chair of IEEE Norway Section, and editorial roles at journals like Frontiers in Neuroscience and Cognitive Neurodynamics . He organized major conferences like IFAC HMS2025 (Beijing) and ICMLT 2024 (Oslo). Awards: Stanford/Elsevier Top 2% Scientists (2023/2024), Senior Research Fellowship (CSC, 2012), Max Planck Fellowship (2011), Shanghai Pujiang Talent (2007), DAAD Scholarship (2002-2004). Grants and advising: PI for 20+ projects, advising PhD students in AI, machine learning, and control systems. Teaching includes courses on computational intelligence, IoT, and fuzzy systems at both undergraduate and graduate levels. Labs/Teams: AI Lab at OsloMet, Machine Learning Lab (Vekia), and collaborations with institutions globally. Current work emphasizes AI ethics, neuroergonomics in smart cities, and adaptive human-machine systems.
