Dr. Mohammad Naraghi is a Professor in the Department of Mechanical Engineering at Manhattan University, specializing in thermal analysis of rocket engines, sustainable building systems, and radiative heat transfer. His research focuses on rocket thermal evaluation (RTE), solar energy optimization, and crystal growth processes. He holds a PhD from the University of Akron, MS from the University of Wales, and BS from the University of Tehran. Research areas include: Thermal modeling of regeneratively cooled rocket engines Radiative heat transfer in enclosures and aerospace systems Solar energy systems optimization (panel orientation, photovoltaic plants) Energy dynamics of green buildings and data centers CFD analysis of fluid flow and heat transfer in propulsion systems His 30+ years of publications span advanced thermal modeling techniques, including RTE software development and stochastic methods. Key contributions include NASA-recognized rocket engine thermal models and a patented seasonally selective building façade. Grants include NASA-funded rocket thermal research and ARPA/AFOSR crystal growth projects. Awards include ASME Fellow, AIAA Associate Fellow, and multiple NASA/ASEE fellowships. Teaching includes courses on solar energy systems, fluid mechanics, and green building energy dynamics. Advises graduate students in mechanical engineering and contributes to industry partnerships through applied thermal research.
Mircea R. Stan is a Professor of Electrical and Computer Engineering at the University of Virginia, serving as Director of Computer Engineering and Virginia Microelectronics Consortium (VMEC) Professor. He leads the High-Performance Low-Power (HPLP) lab and is an associate director of the Center for Automata Processing (CAP). His research focuses on AI hardware, Processing in Memory, Low Power Design, Cyber-Physical Systems, and Spintronics. Education: Ph.D. (1996) and M.S. (1994) from UMass Amherst; Diploma (1984) from Politehnica University, Bucharest. Research interests include energy-efficient computing architectures, IoT systems, and emerging technologies like magnetic skyrmions and memristors. He has pioneered work on asynchronous stochastic computing, thermal-aware microarchitecture, and microfluidic cooling for 3D-ICs. Key awards include the 2024 A. Richard Newton Technical Impact Award, 2018 ISCA Influential Paper Award, and IEEE Fellow (2014). He has held editorial roles at IEEE TVLSI, IEEE TNano, and IEEE Design & Test. Notable contributions include the HPLP lab’s advancements in low-power logic computing, the VCRFID framework for Industry 4.0, and thermal-aware design tools like Hot-LEGO and Cool-3D.
Steve E. Rigdon is a Professor in the Department of Epidemiology and Biostatistics at the College for Public Health and Social Justice, Saint Louis University. His expertise lies in statistical inference, biosurveillance, and reliability modeling. Education: Ph.D. in Statistics, University of Missouri-Columbia M.A., University of Missouri-St. Louis B.A., University of Missouri-St. Louis Dr. Rigdon's research focuses on biosurveillance , election prediction models , quality engineering , and survival analysis . He has made significant contributions to the statistical modeling of repairable systems and optical experimental design. His work bridges theoretical statistics with real-world public health and engineering applications. His publications appear in top-tier journals including Technometrics , Journal of Quality Technology , and Quality Engineering . He is also the author of influential textbooks such as Calculus (8th and 9th editions) and Statistical Methods for the Reliability of Repairable Systems . His election prediction models were featured in the Wall Street Journal and local media in 2008. Research Funding: National Science Foundation (NSF) grant for research on optical experimental design, in collaboration with Arizona State University. Dr. Rigdon teaches Bayesian Statistics and the Capstone in Biostatistics , mentoring students in advanced statistical applications. He is active in research dissemination through platforms like ResearchGate. His office is located at Wool Center, Second Floor, Room 276H, 3545 Lindell Blvd., St. Louis, MO 63103.
Donald C. Wunsch is the Mary K. Finley Missouri Distinguished Professor in Electrical and Computer Engineering at Missouri University of Science & Technology (Missouri S&T), where he directs the Applied Computational Intelligence Laboratory. He previously served as Interim Director of S&T’s Intelligent Systems Center and holds courtesy appointments in Computer Science, Systems Engineering, and Engineering Management. His research focuses on neural networks, reinforcement learning, adaptive resonance theory, bioinformatics, and robotics. Wunsch is an IEEE Fellow, INNS Fellow, and recipient of the 2015 INNS Gabor Award and 2019 Ada Lovelace Service Award. He has led numerous professional organizations, including the INNS Board of Governors and IJCNN conferences. Education: Executive MBA, Washington University in St. Louis (2006) Ph.D. in Electrical Engineering, University of Washington (1991) M.S. in Applied Mathematics, University of Washington (1987) B.S. in Applied Mathematics, University of New Mexico (1984) Research Interests: Donald’s work spans adaptive resonance systems, reinforcement learning architectures, neurofuzzy regression, robotic swarms, and bioinformatics. He emphasizes practical applications in energy systems (e.g., pumped storage hydro optimization), cybersecurity, and medical data analysis. Recent projects include Hamiltonian-driven adaptive dynamic programming and cluster validity indices for streaming data. Awards & Recognition: 2015 INNS Gabor Award INNS Senior Fellow (2007–2013) NSF CAREER Award 2019 INNS Ada Lovelace Service Award Labs & Leadership: As director of the Applied Computational Intelligence Lab, he oversees research in AI/AS applications for critical infrastructure and smart grids. He also chairs the Missouri S&T Information Technology and Computing Committee and actively contributes to interdisciplinary initiatives like the University of Missouri Bioinformatics Consortium.
Andrea Carpinteri is a Full Professor of Structural Mechanics in the Department of Engineering and Architecture at the University of Parma, Italy. He has been a leading figure in the fields of fracture mechanics, fatigue of materials, and structural integrity for over three decades. He previously served as an Associate Professor at the University of Parma (1994–2000) and the University of Padua (1988–1994). He earned his degree in Civil Engineering from the University of Bologna in 1980 with top honors (110/110 cum laude). His academic journey reflects a strong foundation in structural engineering, which evolved into a research career focused on material failure mechanisms. His research interests include fracture mechanics , multiaxial fatigue , size effects in structures , fatigue crack propagation , and constitutive modeling of traditional and advanced materials . He has developed influential fatigue criteria, such as the Carpinteri-Spagnoli (C-S) criterion, and applied fractal theories to model fatigue behavior. His work bridges theoretical modeling, numerical simulation, and experimental validation. The 15 most recent publications highlight a consistent focus on multiaxial fatigue , fretting fatigue , crack path modeling , and energy-based life assessment . His research spans metallic alloys (e.g., Inconel 718, Al 7075), composites, and natural fiber-reinforced materials, often using critical plane and damage mechanics approaches. Recent works emphasize random loading, spectral analysis, and innovative modeling of crack morphology. ESIS Fellow (2012) IGF Honorary Member (2017) Publons Reviewer Award (Top 1% in Engineering, 2018) Multiple 'Most Active Reviewer Awards' (2013–2018) Winner of the BANDO OPEN-UP Prize (2018) International Prize on Renewable Energy Projects (2011) He has supervised numerous PhD students and collaborated with researchers globally. He has been the Principal Investigator or Local Coordinator of multiple national and EU-funded research projects, including H2020 and MIUR grants. His editorial leadership includes serving as Guest Editor for 26 special issues and as a board member of 10 international journals. He chairs TC3 (Fatigue) of ESIS and has organized over a dozen international conferences on fatigue and fracture. He leads research in structural integrity, particularly through his involvement in the Laboratory of Materials and Structures Testing at the University of Parma. His team focuses on both theoretical advancements and practical applications in civil, mechanical, and aerospace engineering.
Ernst Hansen is an Associate Professor at the Department of Mathematical Sciences, University of Copenhagen . He is based at University Park 5, Copenhagen Ø, and his work spans mathematical statistics, probability theory, and applied statistical modeling in public health and finance. Email: erhansen@math.ku.dk Research areas: Public health interventions, Markov chain applications, measure-theoretic probability, and statistical education His publications include textbooks and peer-reviewed articles on topics such as neck/shoulder pain prevention , continuous-time rating transition probabilities , and geometric drift analysis . While his work intersects with causal inference and stochastic processes, no formal awards or student advising records are documented in the provided texts.
Peter Tankov is a Professor of Quantitative Finance at ENSAE (the French national school for statistics and economic administration), part of the Institute Polytechnique de Paris. He is also a researcher at CREST and member of the FIME Laboratory. His academic career includes previous positions at Paris-Cité University and Ecole Polytechnique. Dr. Tankov specializes in applied probability and stochastic processes, with current research interests spanning quantitative finance, energy finance, green finance, sustainable finance, and mean field games applications to economics. His work bridges mathematical rigor with practical financial applications, particularly in the context of climate change and environmental transition. His research output shows a clear trend toward climate-related finance, with recent publications focusing on carbon pricing, transition risk modeling, energy market dynamics, and sustainable investment strategies. The articles demonstrate a strong interdisciplinary approach combining mathematical finance, game theory, and climate science to address pressing environmental finance challenges. 2016 Best Young Researcher in Finance award of the Europlace Institute of Finance 2024 Louis Bachelier award of London Mathematical Society, Natixis Foundation and SMAI Professor Tankov serves as scientific director of the Green and Sustainable Finance program at Louis Bachelier Institute and is a member of editorial boards for top quantitative finance journals including Mathematical Finance and Finance and Stochastics. He is currently guest editing a Special Issue on Climate and Nature Risk in Mathematical Finance. His teaching includes courses on green finance, energy risk management, and financial derivatives.
Vicky Fasen-Hartmann is a Professor at the Karlsruhe Institute of Technology (KIT) within the Department of Mathematics, specifically affiliated with the Institute of Stochastics. She has held her W3 Professor position since October 2012, with two periods of parental leave (August 2016-August 2017 and October 2018-October 2019). Prior to her current position, she held postdoctoral research positions at ETH Zurich (RiskLab), TU Munich, Université Pierre et Marie Curie, and Cornell University. Her educational background includes: Habilitation (2010) in Heavy Tails in Finance, Insurance and Telecommunication from TU Munich Ph.D. (2004) in Extremes of Lévy Driven Moving Average Processes with Applications in Finance from TU Munich Diploma in Mathematics (2002) from Karlsruhe Institute of Technology Professor Fasen-Hartmann's research spans multiple areas of theoretical and applied statistics with a focus on extreme value theory, heavy-tailed distributions, and their applications in finance and risk management. Her work bridges theoretical probability with practical financial applications, particularly in modeling rare events and systemic risks. She has made significant contributions to the understanding of Lévy processes, continuous-time ARMA models, and multivariate extremes. Her research combines rigorous mathematical theory with practical applications in financial mathematics, insurance, and telecommunications networks. The trends in her recent publications (2020-2025) show a clear evolution toward high-dimensional extreme value theory, financial network risk contagion, and advanced modeling of continuous-time processes. Her work increasingly addresses the challenges of modern financial systems, including systemic risk measurement, high-dimensional dependency structures, and the statistical properties of extreme events in complex systems. She has developed innovative methodologies for analyzing multivariate extremes, risk contagion, and continuous-time state space models. Professor Fasen-Hartmann has served in significant editorial roles including Associate Editor for the Scandinavian Journal of Statistics since 2014, Managing Editor of Lévy Matters (2008-2014), and Editor of Bernoulli News (2009-2011). She has also been active in academic service through committee work, including the Steering Committee of the Probability and Statistics Group in Germany (2014-2016) and the Examination Board of the Department of Mathematics at KIT (since 2017). She has supervised numerous doctoral and master's students, with current PhD candidates including Lucas Butsch (since 2021) and previously Lea Schenk, Celeste Mayer, Markus Scholz, and Sebastian Kimmig. Her teaching portfolio includes advanced courses in Time Series Analysis, Continuous Time Finance, Extreme Value Theory, and Asymptotic Stochastics. She regularly organizes workshops and conferences on specialized topics in probability and statistics, demonstrating her leadership in the academic community.
Raffaella Mulas is an Assistant Professor in the Department of Mathematics at the Faculty of Science, Vrije Universiteit Amsterdam. She previously served as a Group Leader and Minerva Fast Track Fellow at the Max Planck Institute for Mathematics in the Sciences, where she maintains an ongoing affiliation. Her research lies at the intersection of spectral graph theory, discrete mathematics, and network science. Research Interests: Her work focuses on the spectral theory of graphs and hypergraphs, particularly the properties of discrete Laplacians and non-backtracking operators. She investigates extremal combinatorics problems such as graph coloring and the Turán problem, often applying spectral methods to derive sharp bounds. Her research has strong applications in modeling and analyzing complex networks. Recent Research Trends: Analysis of the 15 most recent publications reveals a consistent focus on spectral characterizations of graphs and hypergraphs, including signed and complex unit hypergraphs. She frequently studies the normalized Laplacian and its extremal eigenvalues, develops non-backtracking operators, and explores measure-theoretic and geometric representations of networks. A strong thread connects spectral bounds to combinatorial invariants like chromatic number. VU Startpremie Grant Elected Member, European Mathematical Society Young Academy (EMYA) Elected Member, Elisabeth-Schiemann-Kolleg, Max Planck Society Minerva Fast Track Fellow, Max Planck Institute Advising and Grants: While no formal students are listed, she is an active researcher with significant grant funding, notably the VU Startpremie Grant. She collaborates internationally and supervises research projects in spectral graph theory and network analysis. Her affiliation with both VU Amsterdam and MPI-MiS enables broad academic mentorship and collaborative supervision. Labs and Research Groups: Raffaella Mulas leads research within the Mathematics Department at VU Amsterdam and is affiliated with the research group at the Max Planck Institute for Mathematics in the Sciences. Her work contributes to advancing theoretical foundations in discrete mathematics with applications in data science and network modeling.
Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.
Fabio Pusateri is an Associate Professor in the Department of Mathematics at the University of Toronto , holding a faculty position since 2022. He previously served as an Assistant Professor at the University of Toronto (2018-2022) and Princeton University (2014-2018), following postdoctoral work at Princeton (2011-2014). His research focuses on Nonlinear Partial Differential Equations , with specialized studies in Dispersive and Wave Equations , Fluid Dynamics , and Harmonic Analysis . Education : PhD in Mathematics from New York University (2011), MSc from Università Roma Tre (2006) Awards : Coxeter-James Prize (2022), Antonio Ambrosetti Medal (2021), ISAAC Award (2019) Research Contributions : Pioneering work on global existence and stability for water wave systems, radiation damping in Klein-Gordon equations, and wave turbulence theory. His research extends to quantum propagation speeds and nonlinear Schrödinger equations with potentials. Grants : NSERC Grant (2018-2025), NSF Grant (2013-2017), Simons Fellowship (2011-2014) Leadership : Co-organizer of the Fields Colloquium in Applied Mathematics and editor for Nonlinear Analysis .
James Gross is a Professor at the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology, Stockholm. He leads research in mobile systems and networks, with a focus on 5G/6G, edge computing, and performance evaluation. He is Associate Director of KTH's Digital Futures center and a board member of the Innovative Centre for Embedded Systems. Previously, he directed the ACCESS Linnaeus Centre (2016–2019) and was Assistant Professor at RWTH Aachen University. PhD, TU Berlin (2006) Studies: TU Berlin, UC San Diego His research lies at the intersection of wireless networking, edge computing, and mathematical performance modeling. Key areas include ultra-reliable low-latency communications (URLLC), age-of-information, network calculus, and resource allocation. He applies these to 5G/6G, cyber-physical systems, and industrial IoT. His work combines theoretical modeling with real-world implementation and standardization impact. The recent publications highlight a strong focus on deterministic and reliable communications for future networks. Topics include hierarchical inference at the edge, age-of-information optimization, finite blocklength coding, and integration of TSN with wireless systems. There is a clear trend towards AI/ML for resource management and semantic communications, reflecting the evolution of intelligent edge networks. Best Paper Award, ACM MSWiM 2015 Best Demo Paper Award, IEEE WoWMoM 2015 Best Paper Award, IEEE WoWMoM 2009 Best Paper Award, European Wireless 2009 ITG/KuVS Dissertation Award, 2007 James Gross has supervised PhD students such as Samie Mostafavi and advises numerous master's projects. His research has been funded by national science foundations in Germany and Sweden, the ICT TNG SRA, Linnaeus ACCESS Centre, DFG-funded UMIC Centre, German Ministry of Science, and various industry partners. His work has led to patents and influenced wireless standards. He is involved in initiatives like the TECoSA project on trustworthy edge computing and organizes summer schools on Edge AI and 6G. His lab conducts experimental research on edge computing testbeds (e.g., Ainur, ExPECA) and wireless performance evaluation.
Dr. Robin Stephenson is a Lecturer at the School of Mathematical and Physical Sciences, University of Sheffield. His research focuses on Probability Theory, Stochastic Processes, and Applied Probability, with a particular emphasis on random trees, fragmentation processes, and Markov additive processes. His publications span topics like scaling limits of random graphs, self-similar fragmentation, and bivariate Markov chain convergence. He teaches the Probability Modelling module (MAS275) and is based in the Hicks Building (I18), Sheffield, S3 7RH, UK. His work reveals trends in theoretical probability, including critical Galton-Watson trees, directed graph scaling, and the analysis of Markov additive processes. Research applications extend to random maps and self-similar fragmentation dynamics.
Erwin W. Hans is a Full Professor at the University of Twente, affiliated with the TechMed Centre and the Department of Industrial Engineering & Business Information Systems. He co-founded the Center for Healthcare Operations Improvement & Research (CHOIR) and specializes in healthcare operations management, focusing on capacity management, planning, and scheduling. He lectures in graduate and undergraduate programs and contributes to the UN Sustainable Development Goals (SDG4: Quality Education). Educational Background: PhD in Resource Loading by Branch-and-Price Techniques (University of Twente, 2001) Master in ELMA: Model of a Liberalized Electricity Market (University of Twente, 1996) His research applies operations research methodologies to healthcare challenges, including nurse allocation, hospital resource management, and organizational resilience. Recent work emphasizes stochastic scheduling, simulation-optimization, and data-driven capacity planning. Scientific Awards: Best Student Advocate Award for the Health Science program (2025) Canadian OR Society Practice Prize (2012) Central Education Award: Best Lecturer at University of Twente (2015) Decentral Education Prize (IEM Program, 2019) Decentral Education Prize (IEM Program, 2007) Hans supervises academic research and organizes conferences like ORAHS 2012. He serves as an editor for Operations Research for Health Care and leads the CHOIR PhD Summer School.
Dr. Cormac Lucas is a Senior Lecturer in the Department of Mathematics at Brunel University London, affiliated with the College of Engineering, Design and Physical Sciences. His work bridges mathematical optimization with practical applications in finance and operations management. Lucas specializes in Mathematical Optimisation Stochastic Optimisation Asset and Liability Management (ALM) Risk Analytics Portfolio Optimization Supply Chain Planning Under Uncertainty His research combines theoretical advancements with industrial projects, such as US Coast Guard Cutter Scheduling, Insight Investment's ALM, and Unilever's Natural Oil Buying Policy. Recent publications (2013–2024) highlight his focus on Portfolio Rebalancing with Transaction Costs Scenario Generation for Stochastic Programming Heuristic Algorithms for Cardinality Constraints Queuing Systems with Standby Servers Robust Supply Chain Planning Financial Derivative Modeling These works utilize methods like Variable Neighbourhood Search, Differential Evolution, and Lagrangian Relaxation. Email: cormac.lucas@brunel.ac.uk
