Raúl Ordóñez is a full professor and Director of Graduate Programs in the Department of Electrical and Computer Engineering at the University of Dayton’s School of Engineering. He holds a Ph.D. and M.S. in Electrical Engineering from The Ohio State University (2001 and 1996) and a B.E. from Monterrey Institute of Technology. His research focuses on nonlinear control, adaptive systems, extremum-seeking control, and applications in aerospace, robotics, and power systems. Professional highlights include serving as Associate Editor for Automatica since 2006, co-authoring textbooks on adaptive control and extremum-seeking techniques, and holding editorial roles for IEEE conferences. Awards include the Boeing Welliver Faculty Fellowship (2008) and AFRL Summer Faculty Fellowship (2014). He has conducted research collaborations at institutions like Université de Picardie Jules Verne (France) and TU Wien (Austria). Key research areas include control of hypersonic vehicles, industrial robots, scramjets, and power generators. His work integrates advanced control methodologies with practical systems, emphasizing real-time optimization and robustness. Courses taught span control systems, nonlinear dynamics, and adaptive control. Education: Ph.D./M.S. (Ohio State), B.E. (Monterrey Tech) Professional Activities: IEEE Control Systems Society, CCCS Collaborative Center Recent Awards: AFRL (2014), Boeing (2008) Labs/Teams: Collaborates with robotics and aerospace teams at UD and international institutions
Konstantin Mischaikow is a Professor of Mathematics at Rutgers, The State University of New Jersey, affiliated with the Department of Mathematics. His research focuses on dynamical systems, computational topology, mathematical biology, and machine learning applications in complex systems analysis. He specializes in rigorously analyzing nonlinear dynamics using topological methods, with emphasis on robotics control, ecological modeling, and parameterized ordinary differential equations. His work combines theoretical rigor with computational techniques, addressing challenges in global dynamics characterization, data-driven modeling, and topological data analysis. Notable contributions include the development of the DSGRN (Dynamic Signatures Generated by Regulatory Networks) database for systematically analyzing regulatory networks. His research often bridges pure mathematics with applied fields like robotics, systems biology, and granular materials physics. Recent studies focus on applying persistent homology to chaotic systems (e.g., Rayleigh-Bénard convection), identifying attractors via machine learning, and quantifying dynamics in high-dimensional spaces. He has collaborated extensively on projects involving granular media, synthetic biology circuits, and spatiotemporal chaos analysis. His academic contributions span over three decades, with a focus on merging topological tools with computational methods to solve real-world problems. While no specific awards are listed, his work has significantly impacted computational dynamics and topological data science.
Radu Ioan Boț is a Professor at the Faculty of Mathematics at the University of Vienna . Since 2020, he has served as Dean of the Faculty of Mathematics and Head of the Institute of Mathematics . He is also the Speaker of the FWF DK Vienna Graduate School on Computational Optimization and a Founding Member of the Research Platform Data Science@Uni Vienna . Education Ph.D. in Mathematics (2003, Chemnitz University of Technology, Summa cum laude ) M.Sc. in Mathematics (1999, Babeş-Bolyai University, Grade: 10 ) Diploma in Mathematics (1998, Babeş-Bolyai University, Grade: 10 ) Research Interests focus on convex and nonconvex optimization , monotone operators , duality theory , and proximal algorithms . His work bridges continuous and discrete time models , emphasizing fast convergence rates and Tikhonov regularization for problems in image processing , machine learning , and inverse problems . Recent Publications highlight advancements in primal-dual splitting , inertial dynamics , and stochastic optimization , with applications to GANs , phase retrieval , and structured convex minimization . Scientific Awards include the 2003 University Prize of Chemnitz University of Technology , 1998 award for graduating with the best grade from Babeş-Bolyai University, and fellowships during his Ph.D. and pre-Ph.D. research stays. Editorial Roles include Editor-in-Chief of SIAM Journal on Optimization (2026–2029) and editorial board membership for journals like Mathematical Programming , Computational Optimization and Applications , and Journal of Optimization Theory and Applications .
Sebastien Loisel is an Assistant Professor in the School of Mathematical & Computer Sciences at Heriot-Watt University, specializing in the Department of Mathematics. His primary research focuses on domain decomposition methods for solving large-scale problems in massively parallel environments. He has contributed significantly to numerical analysis, stochastic processes, and computational mathematics. Research interests include algorithms, parallel computing, and finite element methods. His work spans topics such as the p-Laplacian, stochastic p-Laplace systems, and handling missing data in PCA. He has published extensively in journals like SIAM Journal on Numerical Analysis and Numerische Mathematik . Dr. Loisel received the Heriot-Watt University Teaching Excellence Award for Global Learning and Teaching in 2019, highlighting his commitment to education. His research collaborations span multiple institutions and countries, reflecting his global academic engagement. While specific advising details are not listed, his research contributions and collaborations indicate active participation in academic and industrial partnerships. His work often emphasizes computational efficiency and scalability in numerical methods.
Lorenzo Pareschi is a Professor and Chair of Applied and Computational Mathematics at the School of Mathematical & Computer Sciences, Heriot-Watt University, Edinburgh, UK. He holds a Ph.D. in Mathematics from the University of Bologna, Italy, and has held visiting professorships at institutions including Georgia Tech, University of Wisconsin-Madison, and Imperial College London. His research focuses on multiscale modeling, numerical methods for nonlinear PDEs, and applications in physics, engineering, and socio-economic systems. Recently, he has explored uncertainty quantification, optimal control, and machine learning. He is a Royal Society Wolfson Fellow (2023) and has served as Head of the Department of Mathematics and Computer Science at the University of Ferrara. He is an associate editor for journals like SIAM Journal on Scientific Computing and Multiscale Modeling & Simulation. Research interests include hyperbolic and kinetic equations, fluid dynamics, plasma physics, epidemiology, and computational hemodynamics. His work contributes to UN Sustainable Development Goals through applications in healthcare and environmental modeling. He has authored over 200 publications and five books, reflecting his leadership in applied mathematics and interdisciplinary collaboration. Education: Ph.D. in Mathematics, University of Bologna (Italy). Awards: Royal Society Wolfson Fellowship (2023), Nelder Fellowship (2015), John von Neumann Professorship (2019). Roles: Member of SIMAI steering committee, EMS CAIR committee, and editorial boards of key journals. Research Trends: Recent articles emphasize machine learning integration with kinetic models, plasma control strategies, and multiscale methods for epidemic spread. His work bridges theoretical developments with practical applications in fusion energy, traffic dynamics, and healthcare. Grants & Advising: Actively supervises PhD students and leads projects on computational methods for complex systems. His grants focus on advancing numerical techniques for multiscale phenomena and uncertainty quantification. Labs/Teams: Collaborates with global networks in computational mathematics and applied physics, contributing to high-impact journals and conferences.
Mateusz Majka is an Assistant Professor in Stochastics at the School of Mathematical & Computer Sciences, Heriot-Watt University, Edinburgh. His research focuses on probability theory, stochastic analysis, numerical analysis, optimization, optimal transport, computational statistics, and machine learning. He holds a PhD from the University of Bonn (2017) and has held postdoctoral positions at the University of Warwick and King's College London. Education: PhD in Applied Mathematics, University of Bonn (2017) Research Fellow, University of Warwick (2018–2020) Research Associate, King's College London (2017–2018) Research Interests: Stochastic differential equations Mathematical foundations of machine learning (mean-field optimization, optimal transport, stochastic gradient algorithms) Lévy processes Ergodicity of Markov processes Monte Carlo methods (MCMC, Multi-Level Monte Carlo) Recent Research Trends: His work emphasizes algorithmic development for stochastic systems, including mean-field games, Wasserstein geometry, and convergence analysis of numerical schemes. Notable contributions span Lévy processes, Euler schemes, and coupling techniques for Markov chains. Awards: None explicitly listed. Advising & Grants: Supervises PhD students Linshan Liu (since 2021) and Razvan-Andrei Lascu (since 2022). Active in securing research funding through collaborative projects. Labs/Teams: Engaged in cross-disciplinary collaborations in computational statistics and stochastic analysis at Heriot-Watt University.
Flore Nabet is an Assistant Professor in the Department of Applied Mathematics at École Polytechnique. She specializes in numerical analysis and computational methods for partial differential equations, particularly focusing on finite volume schemes, Cahn-Hilliard models, and Stokes flows. Education: PhD in Mathematics (Finite Volume Schemes for Multiphase Problems), Institute of Mathematics of Marseille, supervised by Franck Boyer and Pierre Bousquet (2014) Research Interests: Numerical analysis of PDEs Finite volume and finite element methods Stochastic differential equations Multiphase fluid dynamics Optimal transport and energy-stable discretizations Article Trends: Her work spans 2014–2025, emphasizing finite volume methods for Cahn-Hilliard equations, Stokes flows, and stochastic PDEs. Recent papers integrate optimal transport theory with nonlinear PDE discretizations. Scientific Awards: CANUM 2012 Poster Prize SMAI 2013 Poster Prize Teaching: She has taught courses on numerical approximation, optimization, and data science foundations at École Polytechnique and Aix-Marseille University, including co-organizing Collective Scientific Projects (PSC).
Dr. Manuel Dahmen serves as Head of Department at the Institute of Climate and Energy Systems (ICE-1) within the Research Center Jülich. His research focuses on designing sustainable and cost-efficient energy systems through numerical optimization and deep learning techniques. He leads efforts in advancing energy system technology, particularly in optimizing renewable energy integration, process network analysis, and the co-design of fuels and engines. His work emphasizes innovative applications of machine learning, such as physics-informed neural networks and reinforcement learning, to address challenges in energy system design and operation. Key areas include reducing greenhouse gas emissions in industrial processes, optimizing energy networks, and developing data-driven models for dynamic process control. Dr. Dahmen’s contributions span algorithm development (e.g., MUSE-BB decomposition algorithms), energy system scenario generation, and robust design methodologies under uncertainty. His research bridges computational methods with practical energy solutions, aiming to achieve decarbonization in industries like copper production and transportation. Publications highlight advancements in renewable energy integration, fuel design for spark-ignition engines, and the application of graph neural networks for molecular property prediction. His interdisciplinary approach fosters collaboration across chemical engineering, computer science, and energy economics to tackle global sustainability challenges.
Dr. Jenny Wang is a Senior Lecturer in Finance at the School of Business, University of Southern Queensland, affiliated with the Centre for Applied Climate Sciences. Holding a PhD in Finance from the National University of Singapore (2005) and the University of Queensland (2020), along with advanced qualifications in research methods and business, her work bridges finance, climate science, and agriculture. Her research focuses on climate finance, ESG investing, and innovative financial tools for climate risk management. Notably, she secured an ARC Early Career Industry Fellowship (2023) to develop weather index insurance using machine learning. She contributed to the Australian Sustainable Finance Institute's taxonomy initiative (2022–2023) and collaborates with governments (APRA, ASIC) and industries (Rio Tinto, SunWater). Recent publications span sustainable agriculture technology, farmer insurance demand, and financial risk modeling. Awards include the CFA Institute’s ESG Investing certificate. Her grants total over AUD 450,000, emphasizing climate resilience in agriculture.
Tomasz R. Bielecki is a Professor in the Department of Applied Mathematics at Illinois Institute of Technology (IIT). He serves as Director of the Professional Master in Mathematical Finance program at IIT. His research interests span stochastic analysis, stochastic processes, mathematical finance, credit risk, counterparty risk, performance measures, stochastic control, semigroup theory, and functional analysis. He has held editorial roles at journals including SIAM Journal of Financial Mathematics and International Journal of Theoretical and Applied Finance. Bielecki has collaborated with numerous researchers such as Igor Cialenco, Stephane Crepey, and Monique Jeanblanc. His work includes influential books like Structured Dependence between Stochastic Processes and Counterparty Risk and Funding: A Tale of Two Puzzles . His recent publications focus on topics like Hawkes processes, dynamic risk measures, and stochastic control under model uncertainty. He has consulted for firms including Bank of America, Bloomberg, and Merrill Lynch. His research emphasizes applications in financial mathematics, credit risk modeling, and the development of robust frameworks for risk assessment and derivatives pricing.
Ying Liang is a Phillip Griffiths Assistant Research Professor of Mathematics at Duke University, affiliated with the Department of Mathematics within Trinity College of Arts & Sciences. She holds a Ph.D. in Mathematics from The Chinese University of Hong Kong (2021) and a BSc from Wuhan University (2016). Her research focuses on inverse problems, mathematical imaging, scattering theory, and scientific machine learning. She has contributed to theoretical and numerical studies of inverse source problems, wave equations, and electromagnetic diffraction. Her work combines mathematical rigor with computational techniques to address challenges in applied mathematics and engineering. Liang has taught courses such as MATH 563 (Applied Computational Analysis) and MATH 561 (Numerical Linear Algebra) at Duke, and previously instructed multiple courses at Purdue University and The Chinese University of Hong Kong. Her academic journey includes roles as a Golomb Visiting Assistant Professor (Purdue, 2021–2024) and a Research Assistant (CUHK, 2016–2021). Her publications span topics like stability analysis of inverse problems, electromagnetic diffraction theory, and numerical methods for partial differential equations. She actively participates in conferences and workshops, presenting on neural network approaches to inverse problems and scientific computing innovations.
Samuel Isaacson is a Professor at Boston University's Department of Mathematics and Statistics, specializing in numerical analysis, mathematical biology, and mathematical physics. His research focuses on developing and analyzing numerical methods for stochastic reaction-diffusion models in cellular biology, with applications to cell signaling, T cell activation, and antibody-antigen interactions. He emphasizes rigorous coarse-grained modeling, unstructured mesh methods, and parameter inference from experimental data. Recent work includes advancements in reactive Langevin dynamics models, mean-field limits of particle systems, and molecular mechanisms underlying antibody efficacy. His interdisciplinary approach combines computational modeling, experimental collaboration, and mathematical theory to address biophysical questions. Notable contributions include the Catalyst software for reaction network modeling and studies on spatial effects in genetic circuits and chromatin structure. Isaacson's grants and collaborations span computational methods for stochastic systems, parameter estimation in biochemical networks, and the influence of cellular geometry on signaling. His lab focuses on bridging microscopic particle-level models with macroscopic biological observations, with applications in immunology and synthetic biology. Current projects explore the role of molecular 'reach' in antibody-virus interactions and the development of efficient simulation tools for complex biological systems.
Dr. Dimitrios Stafylas is an Assistant Professor of Finance at the University of York's School for Business and Society, with prior roles as Lecturer at Aston Business School and Doctoral Researcher at the University of York. He holds an MBA, MSc in Net-Centric Information Systems, and advanced teaching certifications including a PGCert in Learning & Teaching and Senior Fellowship of the Higher Education Academy. His research focuses on empirical finance, including investment funds, asset pricing, corporate finance, market efficiency, and behavioral finance. He has supervised five PhD students and actively contributes to academic committees, including roles in the FEBS Conference Scientific Committee and as Associate Editor at the European Journal of Finance. Dr. Stafylas has presented at numerous international conferences, such as the FEBS Conference, Computational Statistics Conference, and EFMA Conference. His publications span journals like the British Journal of Management, Studies in Nonlinear Dynamics & Econometrics, and the European Journal of Finance. He has also served in various administrative roles, including Deputy Head of the Board of Examiners (Management) and Programme Director for the MSc in Finance. Dr. Stafylas teaches courses in Corporate Finance, FinTech, Asset Pricing, and Behavioral Finance, reflecting his expertise in both academic and practical financial disciplines. Awards: Senior Fellow of the Higher Education Academy PGCert in Learning & Teaching Certified Management and Business Educator Key Research Themes: Hedge Fund Performance Analysis Portfolio Diversification Strategies Cryptocurrency Market Dynamics Managerial Behavior in Investment Funds Academic Services: Ad-hoc referee for journals including the European Journal of Finance and International Review of Financial Analysis External Ethics Reviewer (University of Sheffield) PhD Examiner and Competition Judge for finance projects His work bridges theoretical finance with practical applications, addressing topics like fund manager mobility, cryptocurrency diversification, and China’s equity fund dynamics. Dr. Stafylas continues to engage in interdisciplinary research, contributing to both academic discourse and industry practices through his extensive network and conference engagements.
João Pedro Hespanha is a Distinguished Professor holding dual appointments in the Electrical and Computer Engineering and Mechanical Engineering departments at the University of California, Santa Barbara. He is affiliated with the Center for Control, Dynamical-Systems and Computation (CCDC) and the Institute for Collaborative Biotechnologies, where he leads research at the intersection of control theory, networked systems, and biological applications. Dr. Hespanha has established himself as a leading authority in hybrid systems and networked control with significant theoretical contributions and practical implementations. Dr. Hespanha received his Licenciatura and MS in Electrical and Computer Engineering from Instituto Superior Técnico in Lisbon, Portugal, before earning his PhD in Electrical Engineering and Applied Science from Yale University in 1998. After serving as an Assistant Professor at the University of Southern California from 1999-2001, he joined UC Santa Barbara in 2002 where he has remained ever since, rising to his current distinguished position. His educational background reflects a strong foundation in both theoretical mathematics and practical engineering applications. His research program spans multiple interconnected domains including hybrid and switched systems, networked control systems, cooperative control of autonomous agents, and systems biology. Dr. Hespanha's work on hybrid systems has fundamentally advanced the mathematical frameworks for modeling systems that combine continuous dynamics with discrete logic transitions. His research on networked control systems addresses critical challenges in communication-constrained environments, while his work in cooperative control tackles computational complexity and limited communication in multi-agent systems. His systems biology research applies control theory to model gene regulatory networks using stochastic hybrid systems. Dr. Hespanha's recent publications demonstrate consistent innovation across theoretical foundations and practical applications. His work shows a clear trajectory toward more complex networked systems, with increasing emphasis on security, resilience, and uncertainty quantification. The publications reveal strong interdisciplinary connections between control theory, computer science, and biology, with applications spanning autonomous vehicles, communication networks, and biological processes. Among his numerous accolades: Elevated to IEEE Fellow in 2008 for contributions to stability techniques for switched and hybrid systems Awarded the prestigious Ruberti Young Researcher Prize in 2009 Received the George S. Axelby Outstanding Paper Award in 2006 Honored with the Automatica Theory/Methodology best paper prize in 2005 Named IFAC Fellow in 2016 Received ACM SIGBED HSCC Best Paper Award in 2019 Dr. Hespanha has successfully mentored over 25 PhD students who have gone on to prominent positions in academia and industry. His research has been consistently supported by substantial funding from NSF, NIH, ONR, and other agencies, with current projects including pandemic management decision systems, precision drug delivery, and control of autonomous vehicle networks. He has taught numerous influential courses including Linear Systems Theory and Noncooperative Game Theory, authoring widely used lecture notes published by Princeton Press. Dr. Hespanha leads an active research group within the Center for Control, Dynamical-Systems and Computation, collaborating with researchers across engineering disciplines and biology. His lab maintains strong connections with industry partners working on autonomous systems, communication networks, and biological applications. He has organized major conferences including serving as General Chair for the 9th International Workshop on Hybrid Systems: Computation and Control in 2006, further establishing UCSB as a leading center for control systems research.
Dr. Joern Wichmann is a Research Fellow in the School of Mathematical Sciences at Monash University. He holds a PhD from Bielefeld University (awarded in 2022) and previously served as a Research Fellow at Universitat Bielefeld until February 2023. His research focuses on Stochastic Partial Differential Equations (SPDEs), with expertise in theoretical analysis (existence, uniqueness, regularity) and numerical methods for SPDEs, including algorithm development and stability analysis. Key applications include modeling fluids, porous media, and superconductors with stochastic components. Education: PhD in Mathematics (2019–2022) from Universitat Bielefeld, specializing in stochastic p-Laplace and symmetric p-Stokes systems. Research interests span Numerical Analysis, Stochastic Analysis, Non-Newtonian Fluids, and Regularity Theory. Recent work emphasizes discretization methods (e.g., space-time schemes for stochastic p-Stokes systems) and noise-driven regularization phenomena in SPDEs. Publications include studies on convex integration for quasi-geostrophic equations and temporal regularity of stochastic systems. No awards are explicitly listed, but his work reflects active contributions to SPDE theory and numerical analysis. No advising or grant details are provided, though collaborations with institutions like Bielefeld University are noted. No dedicated lab or team information is available in the text.
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