جست‌وجوی استاد و پژوهشگر

William N. Caballeroمشاهده پروفایل

استادیار

• Decision Support Systems
• Statistical Modeling
• Mathematical Optimization
• +۵ مورد دیگر

Dr. William N. Caballero is an Assistant Professor of Data Science in the Department of Operational Sciences at the Air Force Institute of Technology (AFIT). His research focuses on developing statistical and mathematical models for decision support in uncertain, multi-agent environments, with applications in defense and security. Methodologically, his work integrates deterministic/stochastic optimization, Bayesian analysis, and interpretable machine learning. Education: Doctor of Philosophy in Operations Research, Air Force Institute of Technology (2019) Master of Science in Operations Research, Air Force Institute of Technology (2017) Bachelor of Science in Industrial Engineering, University of Houston (2011) Research Interests: His interdisciplinary research bridges statistics and operations research, emphasizing Bayesian decision analysis for security problems and modern data science applications in defense contexts. Primary domains include adversarial risk analysis, security games, ethical AI systems, automated driving technologies, and military personnel training optimization. Publication Trends: Recent articles demonstrate strong focus on machine learning applications in national security (LLMs, pilot selection), adversarial modeling (security games, data poisoning), and autonomous systems (driving mode management, ethical frameworks). Methodological innovations frequently combine Bayesian approaches with optimization techniques. Awards and Honors: Seiler Award for Mathematical Sciences Research (2023) Finalist for Clemen-Kleinmuntz Decision Analysis Best Paper (2022) USAF-MIT AI Accelerator Datathon: 5 awards including Overall Winner (2021) Multiple Field/Company Grade Officer of the Quarter awards (2013-2024) Distinguished Graduate honors (AFIT, OTS, Squadron Officer School) General Omar Nelson Bradley Fellowship (2018) Inductee to Omega Rho and Tau Beta Pi honor societies

Jim E. Morelمشاهده پروفایل

استاد

• Deterministic Particle Transport
• Hybrid Deterministic/Monte Carlo Methods
• Radiation-Hydrodynamics and Multiphysics Methods
• +۲ مورد دیگر

Jim E. Morel is a Professor and the Nancy & Ron Stinson Chair in Nuclear Engineering at Texas A&M University. He serves as Director of the Center for Large-Scale Scientific Simulations (CLASS) and holds a joint faculty appointment at Los Alamos National Laboratory. Educational Background: Ph.D., Nuclear Engineering, University of New Mexico (1979) M.S., Nuclear Engineering, Louisiana State University (1974) B.S., Mathematics, Louisiana State University (1972) His research focuses on Discretization and Multilevel Solution Techniques for Deterministic Particle Transport , Hybrid Deterministic/Monte Carlo Methods , and Radiation-Hydrodynamics . He has developed advanced methods for neutron transport, radiation diffusion, and multiphysics simulations. Recent publications explore topics in deterministic transport discretization, Monte Carlo integration, and radiation-hydrodynamics, with applications to reactor physics, radiative shocks, and inverse radiation problems. His work emphasizes numerical accuracy, parallel computing, and physics-based reduced-order modeling. Scientific Awards: Nancy & Ron Stinson Professorship in Nuclear Engineering For contact, email morel@tamu.edu or call 979-845-6072 at his office (AIEN M306).

S.C. Hilleمشاهده پروفایل

استادیار

• Biomathematics
• Dynamical Systems
• Mathematical Modeling of Biological Processes
• +۶ مورد دیگر

Sander Hille is an Assistant Professor in the Department of Mathematics at Leiden University's Faculty of Science, specializing in biomathematics and dynamical systems. He maintains active affiliations with the Plant BioDynamics Laboratory (PBDL) within the Institute of Biology Leiden (IBL) and has collaborated extensively with the microbiology group of Oscar Kuipers at the Rijksuniversiteit Groningen through the NWO-funded BetNet project. His educational background includes a PhD in Canonical Representations from Leiden University (1999) under supervisor G. van Dijk. His research spans both theoretical mathematics and biological applications, with particular focus on: Dynamical systems in spaces of measures Stochastic perturbations of deterministic systems Mathematical modeling of biological transport processes Population dynamics and gene regulatory networks Analysis of signaling networks in biological systems Hille's publication record shows consistent output in high-impact mathematical journals, with recent work focusing on measure-valued solutions to transport equations, Markov operators, and applications to plant biology. His research demonstrates strong interdisciplinary connections between pure mathematics and life sciences, particularly in modeling auxin transport in Arabidopsis thaliana and sporulation signaling networks in Bacillus subtilis . Hille has received research funding through the Netherlands Organisation for Scientific Research (NWO) Computational Life Science programme, though specific awards are not listed in available materials. He actively collaborates with the Plant BioDynamics Laboratory (PBDL), where experimental work on polar auxin transport in plant tissues is combined with mathematical modeling and data fitting. His work bridges discrete and continuum approaches to biological modeling through measure-theoretic frameworks.

Alireza Haghighatمشاهده پروفایل

استاد

• Nuclear Engineering
• Particle Transport Methods
• Computational Nuclear Physics
• +۴ مورد دیگر

Alireza Haghighat is the Robert E. Hord Jr. Professor of Nuclear Engineering and Director of the Nuclear Engineering Program at Virginia Tech's College of Engineering. He leads the Virginia Tech Transport Theory Group (VT3G), focusing on advanced particle transport methods and real-time simulation technologies such as the RAPID code system. His work bridges nuclear energy, security, and medical applications, emphasizing deterministic and hybrid computational methods. Education includes a Ph.D. in Nuclear Engineering from the University of Washington (1986) and B.S. in Physics from Pahlavi University (1978). He has held leadership roles including Director of the University of Florida Training Reactor and Chair of the Nuclear Engineering Department at Pennsylvania State University. Research interests include multi-stage response-function transport (MRT) methodologies, computational particle transport, and virtual reality systems for scientific collaboration. Key projects include the RAPID code for real-time reactor analysis, medical imaging reconstruction algorithms, and nonproliferation safeguards tools. Notable collaborations involve institutions like Idaho National Lab, MIT, and industry partners such as Westinghouse and TerraPower. He holds patents on innovations like the Test and Education Microreactor and collaborative VR systems for scientific computing. Awards include the ANS Radiation Protection Professional Excellence Award (2011) and ANS Fellow (2002). He advises students in advanced reactor design, computational methods, and safeguards technologies.

• Astrodynamics
• Space Mission Design
• Asteroid Deflection
• +۳ مورد دیگر

Dr. Joan Pau Sanchez Cuartielles is a Lecturer in Space Engineering at Cranfield University's School of Aerospace, Transport and Manufacturing. He holds a PhD from the University of Glasgow and has extensive experience in postdoctoral research, including a Marie Curie Fellowship at the Universitat Politècnica de Catalunya. His expertise spans small body missions, asteroid deflection, and space mission design. He has contributed to high-profile projects such as the ESA's Comet Interceptor mission and has pioneered methods in trajectory optimization using dynamic programming and graph-based algorithms. Education Background: Bachelor's Degree in Physics, Universitat de Barcelona MSc in Mission Analysis and Design, University of Glasgow PhD in Space Engineering, University of Glasgow (2009) Research Interests: Focuses on complex space trajectory design, including gravity-assist techniques, asteroid exploration, and autonomous navigation systems. His work integrates advanced optimization algorithms with practical mission constraints, enabling low-cost CubeSat missions and innovative sample-return architectures. Grants & Collaborations: Supported by ESA, UK Space Agency, and European Commission. Active in interdisciplinary projects involving spacecraft navigation, planetary defense, and interplanetary mission planning. Labs/Teams: Core member of the Visionary Space Systems theme within the ERC-funded VISIONSPACE project, and collaborator on international missions like the ESA F-Class Comet Interceptor.

• Financial Risk Management
• Econometrics
• Algorithmic Contract Standards
• +۳ مورد دیگر

Prof. Henriette Elise Breymann is a Professor in the Department of Finance, Risk Management and Econometrics at the ZHAW School of Engineering, Zurich University of Applied Sciences (ZHAW). Her research focuses on financial risk modeling, algorithmic contract standards (ACTUS), big data analytics, and regulatory technology (RegTech). She has led numerous projects, including Data Driven Financial Risk and Regulatory Reporting, Strengthening Swiss Financial SMEs through Reinforcement Learning, and Explainable AI in Credit Risk Management. Her work intersects finance, physics, and computational methods, with contributions to energy economics (e.g., renewable energy systems), high-frequency financial data analysis, and quantum chaos applications. She co-authored Unified Financial Analysis: The Missing Links of Finance (2009) and pioneered the ACTUS framework for standardizing financial contracts. Projects: 12+ leadership roles in applied finance, risk modeling, and energy systems. Key Expertise: Regulatory reporting, stress testing, and data-driven financial systems. ACTUS Standard: Global initiative for granular financial contract representation. Her research spans interdisciplinary collaborations, including the Risk- and Finance-Lab, and has been presented at major conferences like the Society for Economic Measurement. She advises on regulatory innovations and financial resilience strategies for SMEs.

Jose Fernandez Hernandezمشاهده پروفایل

استاد

• Location Science
• Competitive Facility Location
• Global Optimization
• +۴ مورد دیگر

José Fernández Hernández is a Full Professor at the Department of Statistics and Operations Research, Faculty of Mathematics, University of Murcia (Spain). His academic career spans over two decades with significant contributions to location science, global optimization, and interval analysis. Ph.D. in Mathematics (1999), University of Murcia (Thesis: "Nuevas técnicas para el diseño y resolución de modelos de localización continua") Member of Operations Research Group (IOMUR), Spanish Society of Statistics and Operations Research (SEIO), and Euro Working Group on Locational Analysis (EWGLA) Research Interests: Specializing in Location Science where he develops models for optimal facility placement considering transportation costs, market share capture, and customer behavior. His work extends to Global Optimization techniques using interval analysis to solve nonlinear problems with multiple local optima, and Multiobjective Optimization frameworks for balancing conflicting objectives in spatial decision-making. Article Trends: His publications demonstrate a progression from geometric decomposition methods (2000s) to advanced competitive facility location models (2010s) parallel evolutionary algorithms (2010-2020) MINLP formulations for firm expansion (2015-2024) probabilistic consumer choice modeling attractiveness adjustment in existing facilities Stackelberg games in facility location Grants & Projects: Over 20 funded projects including national grants (Ministerio de Ciencia e Innovación) 1997-2024 regional projects (Fundación Séneca, Junta de Andalucía) 1998-2022 international cooperation with Hungary 2002-2006 Coordinating research networks on location analysis and computational methods. Collaboration: Regularly works with researchers from University of Almería University of Szeged (Hungary) International Federation of Operational Research Societies with over 50 co-authored publications.

• Computer Networking
• Low Latency Communication
• Multi-Path Systems
• +۳ مورد دیگر

Johan Garcia is an Associate Professor of Computer Science at Karlstad University, Sweden, where he earned his Ph.D. in 2005. His research focuses on computer networking, particularly low-latency communication in multi-path systems, Starlink satellite performance, and deterministic network emulation using tools like KauNetEm. He has led projects such as the 8-year DRIVE initiative and the HITS project, collaborating with industry partners like Icomera. His work includes evaluating cellular and satellite connectivity for trains, as well as machine learning explainability and network protocol optimization. Education: Ph.D. in Computer Science (Karlstad University, 2005). Key projects include KauNetEm development, mobility testing across the Arctic Circle for Starlink/5G, and sectorization-based localization algorithms for eNodeBs. His research spans wireless networking, protocol evaluation, and algorithmic optimization for network reliability and performance. Research interests include: satellite communication performance, cellular networks in high-speed mobility, deterministic network emulation, ML model interpretability, and protocol efficiency in heterogeneous networks. He has advised multiple master’s and bachelor’s projects and contributed to over 100 publications since 2000. Notable collaborations include the EU FP6 Newcom Network of Excellence and the Swedish Knowledge Foundation-funded 'Reliable and Secure Communication in SCTP' project. Current roles include sub-project manager in DRIVE and co-leader of wireless network modeling work packages.

Torstein Kastberg Nilssenمشاهده پروفایل

دانشیار

• Nonlinear stochastic partial differential equations
• Rough paths
• Malliavin calculus
• +۲ مورد دیگر

Torstein Kastberg Nilssen is an Associate Professor in the Department of Mathematical Sciences at the University of Agder, Norway (since 2019). He has held postdoctoral positions at Technische Universität Berlin (2017-2019), University of Oslo (2014-2017), and guest researcher roles at University of Southern California (2015-2016), University of Manchester (2014), and Humboldt University Berlin (2013). Current affiliation: University of Agder Prior institutions: TU Berlin, University of Oslo, USC, University of Manchester, Humboldt University Berlin Research Focus: Nilssen's work revolves around nonlinear stochastic partial differential equations, rough path analysis, Malliavin calculus, and regularization effects of noise in systems with irregular coefficients. His research explores pathwise solutions for rough PDEs, stochastic transport equations, and robust methods in fluid dynamics under rough perturbations. Publication Trends: Recent articles (2019-2025) emphasize rough path applications to fluid mechanics, geometric rough paths in infinite dimensions, McKean-Vlasov dynamics for Kalman filters, and regularization of SDEs/PDEs with singular drift. Key keywords include stochastic differential equations, fractional Brownian motion, variational principles, and nonlocal diffusions.

Kristian Debrabantمشاهده پروفایل

دانشیار

• Numerical Analysis
• Stochastic Models
• Numerical Methods and Simulation
• +۷ مورد دیگر

Kristian Debrabant is an Associate Professor at the Department of Mathematics and Computer Science, Faculty of Science and Engineering, University of Southern Denmark. He is also affiliated with the STEM Center for Educational Research – FNUG, highlighting his dual engagement in research and pedagogical development. His academic credentials include a Dr.rer.nat. degree, reflecting a strong foundation in mathematical sciences. Department: Department of Mathematics and Computer Science Position: Associate Professor in Computational Science Additional Affiliation: STEM Center for Educational Research – FNUG Email: debrabant@imada.sdu.dk ORCID: https://orcid.org/0000-0003-2901-162X His research lies at the intersection of numerical analysis and stochastic modeling, with a focus on developing and analyzing numerical methods for stochastic differential equations (SDEs). Key areas include Runge-Kutta methods, exponential integrators, weak and strong convergence, and applications in computational science. He also contributes to interdisciplinary work, such as urban traffic modeling and environmental systems. Recent publications show a consistent trend in advancing numerical schemes for SDEs, particularly those with non-standard noise (e.g., fractional Brownian motion), stiff systems, and non-globally Lipschitz coefficients. His work spans theoretical developments in convergence and order conditions to practical implementations in finance, ecology, and engineering. The integration of B-series and tamed schemes reflects a deep engagement with both classical and modern numerical frameworks. Kristian Debrabant supervises PhD students, as indicated by the 'PhD supervision (2)' note in his profile. While specific grant details are not listed, his publication record and active research suggest ongoing funding support. His involvement with the STEM Center implies leadership or significant contribution to educational innovation in STEM disciplines.

Marco Viebachمشاهده پروفایل

پژوهشگر

• Nuclear Reactor Physics
• Neutron Noise Analysis
• Nuclear Safety Systems
• +۴ مورد دیگر

Dr. Marco Viebach serves as a Research Associate and leader of the Junior Research Group NAUTILUS at the Chair of Hydrogen and Nuclear Energy within the Institute of Process Engineering and Environmental Technology at Technical University of Dresden. His primary affiliation spans since November 2013 with promotion to group leader in March 2023. Ph.D. in Nuclear Engineering (2021, TU Dresden) Diploma in Physics (2013, TU Dresden) Preliminary Diploma in Mechanical Engineering (2008, TU Dresden) Dr. Viebach's research focuses on nuclear reactor physics with specialization in neutron noise analysis, reactor stability behavior, and nuclear safety systems. His experimental work centers on the AKR-2 research reactor, investigating neutron flux fluctuations in pressurized water reactors through projects like NEUS and CORTEX. He develops advanced methods for nuclear waste management through the NAUTILUS initiative and explores molten salt reactor technologies. His publication record shows consistent output in nuclear engineering journals, with recent emphasis on hydrogen integration with nuclear power plants, experimental validation of reactor physics codes, and zero-power reactor experiments. Current research trends indicate growing focus on hybrid energy systems combining nuclear power with hydrogen production. Dr. Viebach actively mentors student assignments, final theses, and PhD candidates while teaching courses in Fundamentals of Nuclear Technology, Reactor Physics Aspects, and Thermalhydraulics and Safety of Nuclear Facilities. His collaborative work spans international institutions including Helmholtz-Zentrum Dresden-Rossendorf and Joint Institute for Nuclear Research in Dubna.

John-Paris Pantouvakisمشاهده پروفایل

استاد

• Structural Engineering
• Construction Management
• Project Scheduling
• +۴ مورد دیگر

Professor John-Paris Pantouvakis is affiliated with the School of Civil Engineering at the National Technical University of Athens (NTUA), where he serves in the Department of Structural Engineering. His expertise spans construction management, project scheduling, and productivity optimization. Email: jpp@central.ntua.gr Research interests include: Risk analysis in structural engineering Digitalization and automation in construction Evolutionary algorithm applications Disaster risk reduction frameworks Interdisciplinary education models Cost-benefit analysis under uncertainty Recent publications highlight trends in: 3D printing for emergency shelters Learning curve modeling Corporate responsibility metrics Optimized resource allocation Construction process simulation International project management standards

Daniel Solowمشاهده پروفایل

استاد

• Complex Systems
• Optimization
• Operations Research
• +۳ مورد دیگر

Daniel Solow serves as Professor in the Department of Operations at Case Western Reserve University's Weatherhead School of Management, where he has maintained continuous faculty appointment since 1978. His interdisciplinary expertise bridges operations research, complex systems theory, and mathematics education within the university's business school framework. His educational background includes foundational training at premier institutions: PhD in Operations Research from Stanford University (1978) MS in Operations Research from University of California at Berkeley (1972) BS in Mathematics from Carnegie-Mellon University (1970) Solow's research program operates at three interconnected frontiers: (1) Mathematical modeling of complex adaptive systems to analyze leadership emergence and optimal central control in organizational contexts; (2) Development of advanced optimization algorithms for deterministic, combinatorial, and nonlinear problems; (3) Creation of systematic pedagogical frameworks for teaching mathematical proofs and quantitative methods. His work demonstrates consistent translational impact from theoretical mathematics to practical business applications, particularly in team dynamics and decision-making under complexity. Analysis of his 15 most recent publications reveals strong thematic continuity in applying mathematical rigor to organizational phenomena, with increasing focus on leadership modeling since 2014. His research evolves from pure optimization techniques toward complex systems applications, maintaining strong representation in top-tier journals like Management Science and Organization Science while expanding into interdisciplinary outlets such as Mathematics and The American Scientist . His scientific recognition includes: Weatherhead Teaching Excellence Award (1981, 2010, 2015) Solow's academic contributions extend beyond publications through significant educational leadership. He has developed influential textbooks including How to Read and Do Proofs and Linear Programming: An Introduction to Finite Improvement Algorithms , while teaching quantitative methods across Weatherhead's MBA, master's, and PhD programs. His course development spans foundational topics like Statistics and Decision Modeling to advanced subjects including Operations Analytics and Python Programming, with documented excellence through multiple teaching awards based on student nominations. External service includes editorial roles for INFORMS Journal on Computing and committee leadership for curriculum development and faculty recruitment.

Alessandro Bertoniمشاهده پروفایل

استاد

• Value-Driven Design
• Product-Service Systems
• Systems Engineering
• +۴ مورد دیگر

Alessandro Bertoni serves as Professor of Mechanical Engineering at Blekinge Institute of Technology (BTH) since April 2025, affiliated with the Department of Mechanical Engineering at the Karlskrona campus. He leads research in value-driven design methodologies and product-service systems development, collaborating extensively with aerospace and civil engineering industry partners. His research interests span value-driven design, product-service systems (PSS), and data-intensive technologies for engineering decision support. Bertoni's work focuses on developing methods and tools to support early phases of product and system development, particularly through value visualization techniques and changeability assessment frameworks. He actively investigates how data mining and machine learning can create predictive models that address reliability issues in deterministic engineering models, with applications in aerospace, civil engineering, and construction machinery sectors. His publication record demonstrates consistent growth in impact, with recent work emphasizing digital twin applications, changeability frameworks for circular systems, and data-driven approaches to value assessment. The research trajectory shows increasing integration of artificial intelligence techniques with traditional systems engineering approaches, particularly in the context of sustainable product development and resilient system design. Bertoni teaches across all academic levels, including systems engineering, research methodology in engineering design, and innovative sustainable product development courses. His educational approach incorporates value-driven design principles into engineering education, preparing students to address complex design challenges through quantitative and qualitative assessment methods. As part of the Product Development Research Lab at BTH, he collaborates with researchers like Marco Bertoni, Tobias Larsson, and other international partners on projects addressing challenges in high-variety, low-volume manufacturing contexts, sustainable design, and digital transformation of engineering processes.

• Agent-based Modeling
• Epidemiology
• Computational Statistics
• +۳ مورد دیگر

Nikolas Popper is a Senior Scientist in the Research Area of Computational Statistics at TU Wien. His work focuses on agent-based modeling, epidemiological simulations, and computational methods for decision support systems. He has contributed to pandemic response strategies through predictive modeling and multi-model frameworks. Popper has co-supervised numerous theses on topics including agent-based influenza models, maintenance analysis in aviation networks, and obesity prevalence modeling in Austria. His research integrates diverse methodologies like Bayesian networks, Monte Carlo simulations, and co-simulation techniques to address complex systems challenges. Notable contributions include a multi-model warning system for public health policies and vaccination strategy optimization frameworks. Popper's work emphasizes interdisciplinary approaches to healthcare, transportation systems, and environmental impact analysis. Key Projects: Multi-criteria decision support for pandemic management, train delay analysis using Bayesian networks Grants and Funding: Not explicitly listed in provided texts Labs/Teams: Part of the Network Lab and collaborative initiatives involving computational statistics and epidemiology.