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Zukui LiView profile

Professor

• Mathematical Optimization
• Machine Learning
• Process Systems Engineering
• +4 more

Zukui Li is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering, where he leads a research group focused on mathematical optimization, machine learning, and process systems engineering. His work spans oil sands extraction, steel production, biomedical applications, and advanced optimization methods. Education: Ph.D. in Chemical Engineering, Rutgers University (2010) M.Sc. in Control Theory and Control Engineering, University of Science and Technology of China (2005) B.Sc. in Automatic Control, University of Science and Technology of China (2002) Postdoctoral Training: Princeton University (2010-2012) Research Focus: Dr. Li's research integrates mathematical optimization and machine learning for complex process systems. His primary areas include: Advanced optimization techniques (robust, stochastic, and distributionally robust optimization) Machine learning applications in process monitoring and biomedical systems Industrial applications in energy, manufacturing, and resource extraction Specific innovations include physics-informed ML for anemia treatment, adaptive optimization for steel production, and distributionally robust methods for uncertainty management. Publication Trends (2019-2023): Recent articles demonstrate a strong focus on uncertainty-aware optimization methods, with increasing integration of machine learning techniques. Dominant themes include distributionally robust optimization, adaptive decision-making under uncertainty, neural network approximations for complex constraints, and applications in industrial process control and biomedical systems. Theoretical advancements are consistently coupled with practical implementations in energy and manufacturing sectors. Research Group: Leads an active team developing optimization frameworks and machine learning solutions for process engineering challenges. Group website: Dr. Zukui Li's Research Group

James SmithView profile

Professor

• Decision Analysis
• Management Science
• Operations Research
• +4 more

James Smith is a Professor in Decision Science at the Tuck School of Business, Dartmouth College. He holds a PhD (1990), MS (1986), and BS (1984) from Stanford University. Education: PhD, MS, BS - Stanford University His research focuses on Decision Analysis , Operations Research , and Stochastic Optimization , with applications in energy systems, medical decision-making, and technology adoption. Recent work includes dynamic programming approaches to energy systems under uncertainty and information relaxation techniques in stochastic dynamic programs. Key publication trends: 15 most recent articles span 2025–2004, emphasizing stochastic dynamic programming , resource allocation , and risk evaluation across energy, finance, and public health domains. Scientific awards: Jack Byrne Distinguished Professor in Decision Science He has collaborated extensively in Operations Research and Management Science journals, with notable coauthors like David Brown, Canan Ulu, and Robert Winkler. His work addresses theoretical and applied problems in portfolio management, commodity pricing, and sequential decision modeling.

• Applied Econometrics
• Time Series Modelling
• Tourism Economics
• +6 more

Professor Selva Selvanathan is a Professor of Econometrics at Griffith University, affiliated with the Department of Accounting, Finance, and Economics at the Griffith Business School. He serves as Director of the Economics Policy Analysis Program (EPAP) and a member of the Griffith Asia Institute. His research focuses on applied econometrics, time series modeling, tourism and energy economics, and consumer demand analysis. He has authored/co-authored over 100 articles and multiple monographs, contributing to leading journals such as the Review of Economic Studies and Journal of Econometrics . Education: PhD in Economics (University of WA and Murdoch University, Australia), MSc (University of Bucharest, Romania), BSc (University of Jaffna, Sri Lanka). Research Interests: Professor Selvanathan’s work spans econometric methodologies, tourism economics, energy policy, and consumer behavior. His recent studies include modeling financial asset demand in Sri Lanka, analyzing alcohol consumption patterns in Australia, and exploring the gender dimensions of cleaner energy transitions. Awards and Honors: Pro-Vice Chancellor’s Research Excellence Award 2014 (Research Supervision) University of Western Australia Distinguished Teaching Award (1988) Griffith Awards for Excellence in Teaching 2005 – Innovation Across the Institution Grants and Collaborations: Leading projects funded by the Department of Foreign Affairs and Trade, Australian Research Council, and Griffith University. Notable grants include policy capacity-building initiatives in Vietnam and Sri Lanka, and studies on alcohol control measures. Advising and Mentorship: Supervised over 20 doctoral students, including works on foreign aid impacts, renewable energy transitions, and rural livelihoods. Current research includes market integration dynamics and energy policy effectiveness. Labs/Teams: Active in Griffith Asia Institute and EPAP, fostering interdisciplinary research on Asian economies and policy analysis.

Martin WiddicksView profile

Academic

• Mathematical Finance
• Derivatives Pricing
• Financial Economics
• +3 more

Martin Widdicks is a Teaching Associate Professor of Finance and Director of the MSF Program at the Gies College of Business, University of Illinois at Urbana-Champaign. He holds the Josef and Margot Lakonishok Faculty Fellow distinction and has been recognized as an outstanding teacher repeatedly since 2011. His academic background includes a PhD in Mathematical Finance (University of Manchester, 2002) and a BSc in Mathematics (Manchester, 1999). Research interests focus on mathematical finance, particularly derivative pricing methodologies, singular perturbation theory applications, and executive compensation models. He has developed frameworks for multi-asset option valuation and analyzed incentive structures in equity-based compensation. Recent work includes extracting market crash and bankruptcy signals from options data. Teaching responsibilities span core quantitative finance courses (FIN 502, FIN 503), derivatives (FIN 512), and advanced electives like Complex Derivatives (FIN 514) and Term Structure Models (FIN 516/517). His awards include the Shebik Faculty Fellowship (2021–2022) and Towey Faculty Fellowship (2019–2021). Notable publications include Journal of Futures Markets , Journal of Corporate Finance , and Mathematical Finance . His work bridges theoretical finance with practical applications in risk management and corporate decision-making.

Stefan KlusView profile

Lecturer

• Data-driven model reduction
• Transfer operator approximation
• Machine learning
• +8 more

Dr. Stefan Klus is a Lecturer at the School of Mathematics and Physics, University of Surrey. His research focuses on data-driven model reduction, transfer operator approximation, and kernel-based machine learning applied to dynamical systems. He specializes in interdisciplinary applications across quantum physics, fluid dynamics, and computational biology. Education: PhD in Industrial Mathematics (2011, Paderborn University) and Habilitation (2020, Freie Universität Berlin). Research Interests : Data-driven modeling and reduced-order methods Koopman operator theory and transfer operators Machine learning for dynamical systems (e.g., Deeptime library) Tensor decompositions and quantum systems analysis Graph-based analysis (e.g., microbiome dynamics) Publications : Klus has contributed to over 50 peer-reviewed articles, with recent work emphasizing: Kernel methods for quantum chemistry and physics Tensor-based approaches for high-dimensional systems Applications in climate science (e.g., Pacific SST modeling) Agent-based modeling and social systems Technical Contributions : Co-developer of the Deeptime Python library for dynamical modeling Pioneer in Koopman operator-based model reduction Advanced graph kernel methods for microbiome analysis

Sandy IraniView profile

Professor

• Quantum Computing
• Algorithm Design
• Computational Complexity Theory
• +5 more

Sandy Irani is a Full Professor at the University of California, Irvine (UCI) in the Department of Computer Science within the Donald Bren School of Information and Computer Sciences. She received her Ph.D. from UC Berkeley in 1991 and has been at UCI since 1992. Her research focuses on algorithm design, computational complexity theory, and quantum computing, with notable contributions to online algorithms and quantum complexity theory. She currently serves as Associate Director of the Simons Institute for the Theory of Computing at UC Berkeley, a role she has held since 2022. This position allows her to collaborate with researchers across theoretical computer science and related disciplines. Irani’s teaching excellence is recognized through the UCI Distinguished Faculty Award for Teaching (2021), and she has contributed to education through her zyBook on Discrete Mathematics, used by over 94,000 students globally. Her work bridges foundational computer science with practical applications, including power management strategies and distributed computing algorithms. Notably, she has collaborated with industry leaders like Mike Luby on optimizing distributed systems. Her research in quantum computing explores computational problems inspired by condensed matter physics, aiming to understand quantum advantage over classical systems. She has also authored influential papers on topics like cache hierarchy design, scheduling algorithms, and the theoretical limits of electronic structure calculations. Awards: ACM Fellow (2022), UCI Distinguished Faculty Award for Teaching (2021). Key Roles: Associate Director, Simons Institute; Vice Chair, Computing Division at UCI. Recent Projects: Quantum algorithms for condensed matter systems, maximal independent set algorithms in distributed networks.

Mahmoud ZarepourView profile

Professor

• Time Series Analysis
• Nonparametric Bayesian Inference
• Analysis of Random Variables with Infinite Variance
• +3 more

Mahmoud Zarepour is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa, affiliated with the Faculty of Science. He holds an MSc from Shiraz University (Iran) and a PhD from the University of Toronto. His research focuses on advanced statistical methodologies including Time Series Analysis, Nonparametric Bayesian Inference, and Analysis of Random Variables with Infinite Variance. He has supervised numerous graduate students and postdoctoral researchers, including Nada Habli, Reyhaneh Hosseini, and Sicheng Huang, among others. Education: MSc (Shiraz University, Iran) PhD (University of Toronto) Dr. Zarepour's research interests emphasize Bayesian nonparametric techniques, stochastic processes, and robust statistical methods. His work bridges theoretical developments with practical applications in areas such as change point detection, multivariate analysis, and resampling schemes. Notable contributions include advancements in Dirichlet process-based methods, bootstrap techniques for complex distributions, and asymptotic theory for unstable time series. His publications span over two decades, with recent works addressing Bayesian bootstrapping, quasi-Bayesian change point detection, and nonparametric inference for spherically symmetric distributions. His research group is part of the Statistics and Probability cluster at the University of Ottawa. Dr. Zarepour has advised multiple students and contributed to collaborative projects in statistical theory and methodology. His work often explores the intersection of probability theory and applied statistics, with an emphasis on rigorous mathematical foundations.

• Nonlinear Control Theory
• Dynamic Optimization
• Distributed Control Systems
• +3 more

Thulasi Mylvaganam is a Senior Lecturer in Control Engineering at the Department of Aeronautics, Imperial College London. They specialize in nonlinear control theory, dynamic optimization, and applications to robotics and renewable energy systems. Mylvaganam holds an M.Eng. in Electrical and Electronic Engineering from Imperial College London (2010) and a Ph.D. in Control and Power (2014). They have held roles including Postdoctoral Research Associate (2014–2016), Research Fellow (2016–2017), Lecturer (2017), and Senior Lecturer (2021). Research interests include distributed control, data-driven control, and optimal control strategies for complex systems. They teach courses such as Mechatronics and Computing and Numerical Methods 2 for Aeronautics students. Their work spans robotics, renewable energy systems, and multi-agent systems. Affiliations include the Computational Methods and Mathematical Modelling group and the Robotics Forum. Mylvaganam actively supervises PhD students focusing on advanced nonlinear control topics and emphasizes rigorous academic preparation for prospective candidates.

Jim W EvansView profile

Professor

• Non-Equilibrium Statistical Physics
• Multi-Scale Modeling
• Nanoscience
• +6 more

Jim W Evans is a Professor of Physics & Astronomy and Mathematics at Iowa State University, and a Faculty Scientist at the Ames Laboratory (USDOE). His research focuses on non-equilibrium statistical physics and multi-scale modeling of nanoscale systems, including metallic nanoclusters, epitaxial thin films, catalytic surface reactions, and nanoporous materials. Evans holds a B.Sc. (Hons) in Mathematics from the University of Melbourne (1975) and a Ph.D. in Mathematical Physics from the University of Adelaide (1979). He has authored over 360 publications and maintains editorial roles at journals like Nanomaterials and Surface Science . His research interests span: Stability and dynamics of metallic nanocrystals Coarsening mechanisms in epitaxial films Reaction-diffusion systems and non-equilibrium phase transitions Interfacial catalysis and nanoporous transport phenomena Recent work includes: Real-time KMC simulations of nanocrystal intermixing Thermodynamic modeling of intercalated metal systems Statistical mechanics of surface dynamics Honors include APS Fellowship (2002), APS Outstanding Referee (2015), and an h-index of 58 (Google Scholar). He leads DOE-funded projects on exascale software for catalysis modeling and intercalation chemistry in layered materials.

Min LiuView profile

Professor

• Lean Construction
• Digital Twin Design
• Data Mining
• +5 more

Dr. Min Liu is a Professor and the Abdallah H. Yabroudi Endowed Professor in Sustainable Civil Infrastructure at Syracuse University, where she directs the Syracuse University Infrastructure Institute. She holds a Ph.D. in Engineering Project Management from UC Berkeley, and prior degrees from National University of Singapore and Xi’an University of Architecture and Technology. Her research focuses on integrating human and engineering aspects in construction planning, with emphasis on Lean Construction, Digital Twin design, and machine learning applications. She has published over 50 articles in top-tier journals and won prestigious awards such as the 2021 ASCE Thomas Fitch Rowland Award. Dr. Liu advises numerous graduate students and postdocs, offering positions in Construction Engineering and Management. Her lab develops innovative approaches for infrastructure project delivery, worker mental health, and bridge preservation strategies. Education: Ph.D. in Engineering Project Management, UC Berkeley (2007) MSCE, Xi’an University of Architecture and Technology (1997) MSc in Building Science, National University of Singapore (2001) BSc in Civil Engineering, Qingdao University of Technology (1994) Research Highlights: Large language models for construction planning reliability Ontology-based knowledge systems for construction methods Lean techniques for worker mental health improvement Socioeconomic analysis of bridge preservation strategies Awards: 2021 ASCE Thomas Fitch Rowland Award Multiple Best Paper Awards (2017-2018) "Thank a Teacher" awards (2011-2018) Her advising record includes notable students like Chuanni He (2023 Chinese Government Award) and Gongfan Chen (2022 Three-Minute Thesis Award). Current opportunities include Ph.D. financial support and postdoc positions. Dr. Liu’s work appears in journals like ASCE Journal of Management in Engineering and Engineering, Construction and Architectural Management.

• Optical Solitons
• Quantum Optics
• Nonlinear Chromatic Dispersion
• +3 more

Dr. Anjan Biswas is a Professor in the Department of Math & Physics at the College of Arts & Sciences. His research focuses on analytical and computational approaches to quantum optics and nonlinear wave propagation. Education: Ph.D. in Mathematics from the University of New Mexico. Metrics: Erdos Number 4, Hirsch Index 111. Research interests center on optical solitons in fibers with linear and nonlinear chromatic dispersion, Lie symmetry analysis for quiescent solitons, and stochastic modeling in photonic systems. His work explores fractional temporal evolution, polarization-mode dispersion, and Kudryashov’s power-law structures. Recent publications highlight advancements in cubic-quartic soliton modeling, dispersion triplet systems, and noise-resilient optical communication frameworks. Studies span Boussinesq-type models, magneto-optic waveguides, and metamaterial applications. Key methodologies include Adomian decomposition, Sardar sub-equation techniques, and Lie symmetry analysis. Applications range from quantum fluid dynamics to internet bottleneck suppression via exotic self-phase modulation structures.

Frank E. CurtisView profile

Professor

• Mathematical Optimization
• Machine Learning
• Numerical Analysis
• +3 more

Frank E. Curtis is a Professor in the Department of Industrial and Systems Engineering at Lehigh University, where he has been since 2009. He holds a B.S. in Mathematics and Computer Science from the College of William and Mary (2003), an M.S. and Ph.D. in Industrial Engineering from Northwestern University (2004 and 2007), and completed a postdoctoral fellowship at New York University’s Courant Institute (2007–2009). His research focuses on developing numerical methods for large-scale nonlinear optimization, with applications in machine learning, operations research, and energy systems. Key achievements include the 2021 SIAM/MOS Lagrange Prize for Continuous Optimization (with Bottou and Nocedal) and the 2018 INFORMS Computing Society Prize (with Burke, Lewis, and Overton). He has secured significant funding from the NSF, DoE, and ONR, including a TRIPODS grant and ARPA-E awards. His work on the ARPA-E Grid Optimization Competition earned second place in 2020. Curtis’s research interests span mathematical optimization, numerical analysis, and algorithm design. His recent articles emphasize stochastic optimization, fairness in machine learning, and robust algorithm development for constrained systems. He serves as an Area Editor for Mathematics of Operations Research and Associate Editor for multiple top journals, including Mathematical Programming and SIAM Journal on Optimization . Notable grants include DoE ASCR Early Career Awards and NSF TRIPODS funding for collaborative projects with Northwestern, Boston University, and Cornell. His OptML @ Lehigh team develops cutting-edge optimization tools and frameworks for real-world applications.

Luis Nunes VicenteView profile

Professor

• Continuous Optimization
• Computational Science and Engineering
• Machine Learning
• +4 more

Luis Nunes Vicente is the Timothy J. Wilmott '80 Endowed Faculty Professor and Chair of Lehigh University's Department of Industrial and Systems Engineering since August 2018. He previously served as a faculty member at the University of Coimbra's Department of Mathematics. His research focuses on Continuous Optimization, Computational Science and Engineering, and Machine Learning/Data Science. Education: Ph.D. in Applied Mathematics (Rice University, 1996), M.A. in Applied Mathematics (Rice University, 1994), B.S. in Mathematics and Operations Research (University of Coimbra, 1990). Honors include the Lagrange Prize (2015), SIAM Fellowship (2024), and Fulbright Scholarship (1996). He co-authored the influential book Introduction to Derivative-Free Optimization (2009). Research Interests: Development of optimization algorithms for derivative-free and stochastic scenarios, multi-objective optimization, machine learning applications, and computational methods for engineering problems. Key contributions include trust-region methods, bilevel optimization frameworks, and fairness-aware machine learning models. Grants and Leadership: Secured over $1M from the Office of Naval Research (2024) and the Air Force Office of Scientific Research (2023). Served as Editor-in-Chief of Portugaliae Mathematica (2013–2018) and on editorial boards of top journals like SIAM Journal on Optimization . Elected President of the Operations Research Association of Chairs (2024). Visiting Positions: IBM T.J. Watson Research Center (2002/2003), Courant Institute/NYU (2009/2010), and CERFACS/Toulouse (2010–2015). Active in international conferences, delivering plenary lectures at 13th French-German Conference on Optimization, ICCOPT III, and ISMP 2018.

Agostino CapponiView profile

Professor

• Systemic Risk
• Financial Technology
• Blockchain and Crypto Economics
• +4 more

Agostino Capponi is a Professor of Industrial Engineering and Operations Research at Columbia University, affiliated with Columbia Engineering and the Data Science Institute (DSI). He holds academic fellowships at the Luohan Academy (Alibaba Group) and the Fintech@Cornell Center. His research focuses on systemic risk, financial technology, blockchain economics, and machine learning applications in finance. He has authored a best-selling book on machine learning in financial markets and received prestigious awards including the NSF CAREER Award and the JP Morgan AI Faculty Research Award. Education: Master's and PhD in Computer Science and Applied & Computational Mathematics from Caltech (2006-2009). Professional roles include Editor of Management Science , co-editor of Mathematics and Financial Economics , and leadership positions in the Bachelier Finance Society and INFORMS Finance Section. His research has been funded by NSF, DARPA, J.P. Morgan, Ethereum Foundation, and others. Research interests span blockchain governance, decentralized finance protocols, and systemic risk mitigation in financial networks. Notable contributions include work on liquidity risk, crypto-economic systems, and causal inference in financial modeling. Media coverage includes American Banker, Vox, and Chicago Booth Review. He holds a patent in military network tracking and served as a visiting scholar at the Federal Reserve Bank of New York.

June-Yub LeeView profile

Professor

• Numerical Analysis
• Inverse Problems
• Computational Fluid Dynamics
• +3 more

June-Yub Lee is a Professor of Mathematics and Vice President at Ewha Womans University, leading the Office of University Planning and Coordination. He is also a member of the Institute of Mathematical Sciences. His academic background includes a Physics major and Mathematics minor from KAIST, followed by a Ph.D. in Mathematics from New York University's Courant Institute. Prof. Lee specializes in numerical analysis, inverse problems, computational fluid dynamics, and phase-field modeling. His research focuses on developing robust numerical methods for partial differential equations, with applications in materials science and fluid dynamics. Notable contributions include work on operator splitting methods, energy-stable schemes for phase-field models, and inverse problem solutions in electrical impedance tomography. He has organized major international conferences like ICM2014 and ICIP series, and serves on editorial boards of journals such as the Journal of the Korean Society for Industrial and Applied Mathematics (KSIAM). His work bridges theoretical mathematics with computational science, emphasizing interdisciplinary applications. Prof. Lee has actively contributed to academic administration, including serving as Director of the Division of Natural Sciences at the National Research Foundation (NRF). His research lab focuses on scientific computation, with recent projects exploring long-time simulations of phase-field crystal models and high-order numerical methods for multiphase systems.