جست‌وجوی استاد و پژوهشگر

Ryan W. Matzkeمشاهده پروفایل

پژوهشگر ارشد

• Potential Theory
• Discrepancy Theory
• Discrete Geometry
• +۷ مورد دیگر

Ryan W. Matzke is an NSF Mathematical Sciences Postdoctoral Research Fellow at Vanderbilt University's Department of Mathematics, sponsored by Professor Edward B. Saff. His research focuses on Potential Theory, Discrepancy Theory, and Discrete Geometry, with applications in Harmonic Analysis, Frame Theory, and Additive Combinatorics. He has held postdoctoral positions at Technische Universität Graz and completed his Ph.D. at the University of Minnesota-Twin Cities under Professor Dmitriy Bilyk. His research bridges theoretical mathematics with computational methods, including studies on energy optimization, spherical configurations, and deterministic point distributions. Current projects analyze multivariate kernels, Riesz potentials, and geometric discrepancy principles. His work has been published in journals like the Proceedings of the American Mathematical Society, Mathematika, and Probability Theory and Related Fields. In 2025, he will return to the job market for postdoctoral or tenure-track positions. He has received the American Institute of Mathematics' 2025 Alexanderson Award for collaborative research on spherical energy measures. Education Ph.D. in Mathematics, University of Minnesota-Twin Cities (2021) Grants NSF Mathematical Sciences Postdoctoral Research Fellowship (current)

Aneta Stefanovskaمشاهده پروفایل

استاد

• Nonlinear Dynamics
• Biomedical Physics
• Chronotaxic Systems
• +۷ مورد دیگر

Professor Aneta Stefanovska is a leading researcher in the Department of Physics at Lancaster University and a prominent member of the Data Science Institute. Her work focuses on understanding the physical principles underlying living systems through the lens of nonlinear dynamics and oscillatory phenomena across multiple biological scales. Stefanovska's research centers on chronotaxic systems (from chronos - time and taxis - order), a framework she pioneered for characterizing biological systems that maintain stable oscillatory behavior despite continuous external perturbations. Her work spans from cellular processes to cardiovascular and brain dynamics, with applications in aging, anesthesia, and disease states. She has developed the MODA Toolbox , a suite of numerical methods for studying chronotaxic systems as an inverse problem. Her recent publications reveal a strong interdisciplinary focus on neurovascular coupling, cardiovascular dynamics, and time-frequency analysis methods. The research demonstrates how similar oscillatory patterns emerge across vastly different systems - from surface state electrons on liquid helium to human physiological processes. Her work bridges fundamental physics with clinical applications in Alzheimer's disease, autism spectrum disorder, and cardiovascular health. Professor Stefanovska leads multiple significant research projects including 'DSI:Quantitative Assessment of Autistic Spectrum Disorder' (2021-2025), 'Network dynamics-based standards for endothelial health' (2023-2025), and 'Creation and evolution of quantum turbulence in novel geometries' (2023-2027). She has received funding from various sources including the H2020: COSMOS project. She is an active scientific contributor with numerous invited talks worldwide on chronotaxic dynamics, multiscale oscillatory phenomena, and the physics of biological rhythms. Her current research extends into investigating dynamical markers of cancer, further demonstrating the broad applicability of her theoretical framework.

• Critical Infrastructure Resilience
• Logistics Systems
• Emergency Medical Services
• +۷ مورد دیگر

Lavanya Marla is an Associate Professor with tenure in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign (UIUC). She holds a PhD in Transportation Systems from MIT (2010) and dual M.S. degrees in Transportation and Operations Research from MIT (2007). Her academic roles include visiting appointments at NYU (2023-2024), Indian School of Business (2015), and a prior position as Assistant Professor at UIUC (2013–2022). She also holds zero-time appointments in Civil and Environmental Engineering, Information Trust Institute, and the Center for Autonomy. Her research focuses on applying large-scale optimization, machine learning, and data-driven methods to enhance logistics and transportation systems' efficiency, resilience, and sustainability. Key areas include air transportation networks, emergency medical services (EMS), and cyber-physical systems. She bridges Operations Research and AI, emphasizing decision-making under uncertainty and robust network design. Recent work addresses climate change impacts on airline networks, cybersecurity in shipping ports, and algorithmic solutions for healthcare logistics. Her articles span journals like Transportation Science , IEEE Transactions , and Operations Research Forum . Notable awards include the Alfred P. Sloan Fellowship (2008–2009) and the 2021 Center for Advanced Study Fellowship. Her grants include funding from NSF, DHS, and collaborations with industries like Flyr Labs and Deepair Solutions. She mentors over 20 students, emphasizing diversity and equity in STEM. Current projects include drone-bystander-ambulance coordination and pandemic healthcare management through the Jump-ARCHES initiative.

Frank Norbert Proskeمشاهده پروفایل

استاد

• Stochastic Analysis
• Financial Mathematics
• Probability Theory
• +۳ مورد دیگر

Frank Norbert Proske is a Professor in the Department of Risk and Stochastics at the University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. His research focuses on stochastic analysis, financial mathematics, and probability theory, with applications to stochastic differential equations, Malliavin calculus, and mathematical finance. Education and Background : While specific academic credentials aren’t detailed, his position as a professor indicates a PhD in mathematics or a related field. His work is centered at the intersection of theoretical probability and applied stochastic processes. Research Interests : Proske’s research emphasizes stochastic analysis, including singular stochastic differential equations, McKean-Vlasov systems, fractional Brownian motion, and applications in finance and optimization. He explores regularization by noise, optimal control of SPDEs, and theoretical foundations of stochastic processes. Recent Work Trends : His 2020–2025 publications address topics like Fokker-Planck equations for McKean-Vlasov systems, adaptive optimization via SDEs, and stability of stochastic processes. He collaborates frequently on projects involving Malliavin calculus and stochastic control. Grants and Projects : Engaged in projects such as Norway-Ukrainian Cooperation in Mathematical Education and Robustness, Asymptotic Behavior, and Stability of Stochastic Models . These highlight his role in both academic research and international educational initiatives. Labs/Teams : While no specific lab is mentioned, his collaborations with researchers like Øksendal, Meyer-Brandis, and Agram suggest active participation in interdisciplinary research groups focused on stochastic analysis and financial mathematics.

• Sustainable Process Technology
• Process Optimization
• Carbon Capture and Utilization
• +۳ مورد دیگر

Edwin Zondervan is a Full Professor in Sustainable Process Technology, specializing in process optimization, renewable energy systems, and carbon capture technologies. His work contributes to UN Sustainable Development Goals related to affordable and clean energy, climate action, and industry innovation. Research focuses on biofuel production, green hydrogen systems, and sustainable process design through superstructure optimization frameworks. He actively engages in interdisciplinary studies, integrating data-driven methods and digital twin technologies to address energy transition challenges. Recent research highlights include advancing hydrogen-to-power systems, optimizing biodiesel production with heat integration, and stochastic modeling for biorefinery design. His contributions span academic publications, conference presentations (e.g., 2022 talk on PSE in a changing world), and collaborative projects. Zondervan emphasizes agile education strategies to prepare graduates for evolving industry needs, advocating for process systems engineering as a cornerstone of sustainable development. Key themes in his work include decarbonization pathways, energy storage solutions, and circular economy applications. While no awards are explicitly listed, his high citation count (2,895) and h-index (28) reflect significant academic impact. Ongoing efforts focus on integrating renewable energy systems with industrial processes to achieve net-zero goals.

Matthias Katzfussمشاهده پروفایل

استاد

• Computational Spatial Statistics
• Spatio-Temporal Statistics
• Gaussian Processes
• +۵ مورد دیگر

Matthias Katzfuss is a Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on computational spatial and spatio-temporal statistics, Gaussian processes, uncertainty quantification, and data assimilation, with applications in environmental science and satellite remote sensing. He has received funding from NSF, NASA, NOAA, USDA, Sandia National Laboratory, and Jet Propulsion Laboratory. His scientific achievements include awards such as the NSF Career Award, a Fulbright Scholarship, and the Early Investigator Award from the American Statistical Association’s Section on Statistics and the Environment. His work bridges theoretical advancements and real-world applications, particularly in environmental monitoring and large-scale data fusion. Recent research trends include scalable methods for high-dimensional spatial data, nonstationary covariance modeling, and Bayesian transport maps. His publications address challenges in remote sensing, climate science, and machine learning, emphasizing computational efficiency and methodological innovation. Matthias collaborates extensively with institutions like JPL and NASA, contributing to global-scale environmental studies. His lab focuses on developing open-source software tools, such as the GPgp package, to enable reproducible research in spatial statistics.

• Scheduling
• Supply Chain Management
• Operational Research
• +۴ مورد دیگر

Ali Skaf is an Associate Professor at CESI LINEACT, affiliated with the Engineering and Numerical Tools Research team. His research focuses on scheduling problems, supply chain resilience, multi-objective optimization, and port logistics. He holds a PhD in Automatics from Belfort Montbéliard University of Technology (2020) and a Master's in Computer Science from the Lebanese-French University (2012). His educational activities span Computer Science, Operational Research, and Mathematics across engineering cycles and preparatory courses. Skaf's work emphasizes port terminal operations, including quay crane scheduling, yard truck coordination, and mixed-integer programming models for container handling. His recent publications (2018–2022) highlight contributions to NP-completeness analysis of scheduling problems and optimization algorithms for maritime logistics. His research integrates exact methods, heuristics, and computational complexity analysis to enhance supply chain efficiency. No scientific awards are explicitly mentioned, though his active publication record reflects ongoing scholarly engagement. His advising and grants details are not documented here, but his team's focus on numerical tools and engineering applications suggests collaborative projects in industrial automation and logistics systems. Labs/Teams: Member of the Engineering and Numerical Tools Research team at CESI LINEACT, focusing on applied operational research and port logistics solutions.

• Calculus of Variations
• Nonlinear Partial Differential Equations
• Optimal Transport
• +۴ مورد دیگر

Emanuela Radici is an Assistant Professor at the Università degli Studi dell'Aquila, specializing in advanced mathematical analysis with a focus on Calculus of Variations, Nonlinear PDEs, and Deterministic Particle Approximation. Her research explores topics such as optimal transport, aggregation-diffusion equations, and traffic models. She has organized workshops on Calculus of Variations and PDEs, demonstrating leadership in academic collaboration. Her work bridges theoretical analysis with applied problems, including opinion dynamics and congestion modeling. Key contributions include particle approximation methods for conservation laws and studies on BV homeomorphisms. She actively participates in international conferences and research events, contributing to the advancement of geometric methods in calculus of variations. Dr. Radici’s research output includes 22 published papers, with recent works addressing nonlinear mobilities, degenerate traffic models, and minimal extensions in geometric norms. Her interdisciplinary approach integrates functional analysis, differential geometry, and numerical methods.

• Calculus of Variations
• Partial Differential Equations
• Free-Discontinuity Problems
• +۵ مورد دیگر

Filippo Cagnetti is an Associate Professor at the Università di Parma. His research focuses on advanced topics in mathematical analysis, including calculus of variations, partial differential equations, and geometric measure theory. He specializes in free-discontinuity problems, homogenization, fracture mechanics, and optimal transport, with applications to material science and continuum mechanics. His work bridges theoretical rigor and practical models in cohesive zone theory and fracture evolution. Key research interests include the analysis of BV functions, rigidity phenomena in geometric inequalities, and stochastic homogenization. He has developed methods for studying free boundary problems, symmetrization techniques, and Γ-convergence in free-discontinuity contexts. Recent contributions address the stability of Steiner symmetrization and optimal regularity in cohesive zone models. He has organized several international conferences, including the 2025 Perspectives in the Calculus of Variations and the 2023 Variational Analysis of Complex Systems. His work often involves collaborations with leading figures such as G. Dal Maso, L. Scardia, and A. Chambolle.

• Machine Learning
• Big Data Analytics
• Smart Cities
• +۴ مورد دیگر

Luis Francisco Sánchez Merchante is an Assistant Professor at Comillas Pontifical University's School of Engineering (ICAI), affiliated with the Institute for Research in Technology (IIT). He holds a Ph.D. in Information Technology from the Technological University of Compiègne, specializing in Machine Learning, and degrees from UPM and UC3M. His expertise spans Big Data analytics, smart cities, and AI applications in transportation. He has 12+ years in the private sector, including roles at Alstom, Bankia, and Amadeus, and has taught at multiple institutions since 2004. Education : Ph.D. in Information Technology (Machine Learning), Technological University of Compiègne Master's in Multimedia and Communications, UC3M Telecommunications Engineering, UPM Research Interests : Advanced analytics on Big Data platforms Machine Learning applications in smart cities and transportation Unsupervised learning (clustering), supervised learning (neural networks/random forests), and reinforcement learning Image/video/audio processing for AI-driven solutions Key Projects : EU-funded Injury Mitigation for Vision-Zero (2024-2028) WHO Global Road Safety Data Analysis (2023) AI for E-Scooter Safety (Comillas, 2023-2024) Grants & Awards : No explicit awards noted, but active in competitive EU/WHO grants. Advising & Labs : Supervising PhD candidate P. López-Adeva Fernández-Layos (Rigid Body Simulation). Active in IIT's AI and transportation research groups.

Ehsan Ardjmandمشاهده پروفایل

دانشیار

• Business analytics
• Operations research
• Machine learning
• +۳ مورد دیگر

Ehsan Ardjmand is an Associate Professor of Analytics & Information Systems and Director of the Master of Business Analytics program at Ohio University's College of Business. He holds a Ph.D. in Systems Engineering from Ohio University. His research focuses on business analytics, machine learning, operations research, and network science, addressing complex decision-making problems in logistics, supply chain management, and viral marketing. He has collaborated on industry solutions involving AI, optimization, and network analysis. Education: Ph.D. in Industrial and Systems Engineering from Ohio University. Research Interests: Business analytics, large-scale optimization, operations research, and machine learning applications in supply chain and network systems. He explores intersections between AI, networks, and information systems to enhance decision-making in industries like manufacturing, retail, and healthcare. Publications highlight contributions to reinforcement learning for vaccine distribution, multilayer network optimization, and viral marketing strategies. His work includes patented decision support systems for inventory management and cash logistics optimization. Ardjmand also serves on Observea Inc.'s advisory board, guiding causal machine learning implementations. Advising and Grants: Advises students on advanced analytics projects and has secured grants for industry partnerships. Leads the M.B.A. program, emphasizing practical data-driven solutions. Collaborates with organizations to implement AI-driven logistics and inventory systems. Professional Roles: Director of M.B.A. program, faculty in College of Business, and advisory board member. His work bridges academic research with real-world applications in decision support systems and operational efficiency.

• Electronics, sensors and digital hardware
• Nanotechnology
• Electrical engineering
• +۷ مورد دیگر

Dr. Nam Ha is an Associate Professor in the School of Engineering at RMIT University. His research focuses on Electronics, sensors, nanotechnology, electrical engineering, environmental management, distributed computing, and materials engineering. He has contributed to numerous high-impact publications in optimization, logistics, and algorithm design. His work spans theoretical advancements and practical applications, including stochastic optimization and supply chain management. Dr. Ha's research also involves collaborations in computational physics and music technology, showcasing interdisciplinary interests. His email is nam.ha@rmit.edu.au, and he holds an ORCID: 0000-0001-9571-5348. Recent articles highlight contributions to optimization in chemical tanker logistics, ant colony algorithms for convoy problems, and stochastic methods in event generator tuning. His work often bridges theoretical models and real-world applications, emphasizing efficient algorithms and robust solutions.

Marios Kogiasمشاهده پروفایل

استادیار

• Operating Systems
• Networking
• Distributed Systems
• +۴ مورد دیگر

Marios Kogias is an Assistant Professor in the Department of Computing at Imperial College London, conducting research at the intersection of operating systems, networking, and distributed systems with a focus on tail-tolerance, fault-tolerance, and confidentiality using emerging datacenter hardware. He holds a PhD in Computer Science from EPFL (2020) supervised by Ed Bugnion, where his thesis won the Dennis M. Ritchie Award and received an Honourable Mention for the Eurosys Roger Needham PhD Award. His undergraduate degree in Electrical and Computer Engineering is from NTUA. His research spans operating systems, networking, distributed systems, cloud computing, and confidential computing, with significant contributions to microsecond-scale computing, serverless architectures, and eBPF applications. Current work emphasizes tail-latency optimization in datacenter environments and confidential computing security. Recent publications reveal strong trends in microsecond-scale systems, confidential computing, and serverless workflows, demonstrating expertise in leveraging hardware innovations for performance and security guarantees across distributed environments. His scientific awards include: Dennis M. Ritchie Award (2021) Honourable Mention for the Eurosys Roger Needham PhD Award (2021) IBM Fellowship Microsoft Swiss JRC Fellowship Distinguished Artifact Award (ASPLOS 2021) Best Student Paper Award (Eurosys 2020) Dr. Kogias actively mentors PhD students including Konstantinos Prasopoulos (co-advised with Ed Bugnion), Scofield Liu, Farzad Mohammadi, and Adel Sefiane. He teaches Networked Systems, Computer Networks and Distributed Systems, and Object-Oriented Design at Imperial, supported by fellowships from IBM and Microsoft during his doctoral research. His collaborative work spans EPFL, Microsoft Research Cambridge, and Imperial College London, focusing on systems architecture for next-generation datacenter applications.

• Nonlinear Filtering
• Particle Flow
• Bayesian Inference
• +۳ مورد دیگر

Fred Daum is an IEEE Fellow, Principal Fellow at Raytheon, and Distinguished Lecturer for the IEEE. He holds a degree from Harvard University and maintains affiliations with the Department of Electrical and Computer Engineering (ECE) at Stony Brook University, where he delivered a seminar on particle flow filters based on Gromov’s method. Education: Harvard University Research Interests: Fred Daum specializes in nonlinear filtering and Bayesian inference, with a focus on particle flow algorithms inspired by Gromov’s mathematical theories. His work addresses high-dimensional and nonlinear problems in stochastic computation, offering significant improvements in speed and accuracy over traditional methods like extended Kalman filters and standard particle filters. Key subfields include optimal transport, probabilistic modeling, and deterministic particle flow solutions for real-time applications. Scientific Awards & Honors: IEEE Fellow IEEE Distinguished Lecturer Contributions & Recognition: Fred has published over 100 technical papers and delivered invited lectures at leading institutions, including MIT, Harvard, Caltech, and international universities. His research emphasizes scalable, robust solutions for complex computational challenges in filtering and data assimilation.

• Active Nematics
• Fluid Dynamics
• Microbial Motion
• +۳ مورد دیگر

Kevin Mitchell is a Professor in the Department of Physics at the University of California Merced. His research focuses on the dynamics of active nematics, fluid mechanics, and the behavior of microswimmers in complex flows. He employs methods from nonlinear dynamics, topological analysis, and computational modeling to study systems ranging from biological soft corals to chaotic fluid systems. Key research areas include: Active matter and nematic fluids Chaotic advection and transport barriers Swimmer dynamics in laminar and vortex flows Interdisciplinary applications in biophysics and marine biology His recent work explores optimal mixing mechanisms in confined active systems, topological entropy in three-dimensional flows, and the interplay between noise and deterministic motion in microbial swarming. Notable contributions include studies on pulsating soft corals, fractal dynamics in BECs, and the development of ensemble-based entropy calculation methods for fluid systems. No scientific awards are explicitly mentioned in the provided texts. His research has been supported by collaborations in experimental biophysics and computational fluid dynamics, with a focus on real-world applications in marine ecology and material science.