جست‌وجوی استاد و پژوهشگر

Caren Tischendorfمشاهده پروفایل

استاد

• Differential-Algebraic Equations
• Numerical Analysis
• Mathematical Modeling
• +۲ مورد دیگر

Prof. Dr. Caren Tischendorf is a Professor of Applied Mathematics at Humboldt University of Berlin's Institute of Mathematics. Her research focuses on differential-algebraic equations, numerical analysis, and mathematical modeling with applications in electrical circuits, energy networks, and physical systems. She investigates modeling, numerical analysis, and solution methods for differential equations with algebraic constraints, including applications in circuit simulation, gas network optimization, and biological systems. Her work spans theoretical foundations and practical implementations. Recent publications cover numerical methods for differential equations, machine learning foundations, gas network modeling, and uncertainty quantification. Her work demonstrates consistent innovation in computational mathematics with applications across engineering and physics domains. As Dean of the Faculty of Mathematics and Natural Sciences (2022-2025), she provides institutional leadership. She serves as Chair of the MATH+ Council, represents DMV in ICIAM, and contributes to scientific committees for SCEE Foundation and KoMSO.

David Bromanمشاهده پروفایل

استاد

• Programming Languages and Compilers
• Real-Time Systems and Cyber-Physical Systems
• Probabilistic Machine Learning
• +۲ مورد دیگر

David Broman is a Professor at KTH Royal Institute of Technology in Sweden, leading the Division of Software and Computer Systems (SCS) and serving as Associate Director Faculty for Digital Futures. He holds a Ph.D. in Computer Science from Linköping University (2010), with visiting roles at UC Berkeley (2012–2016) and Stanford University (2023–2024). His research spans programming languages, real-time systems, and probabilistic machine learning, emphasizing secure and efficient compilation techniques. Education: Ph.D. in Computer Science (Linköping University, 2010) Visiting Roles: UC Berkeley (2012–2014 part-time), Stanford University (2023–2024) Research Interests: David focuses on advancing programming language theory, compilers, and probabilistic methods. Key areas include transformer-based compilation, cyber-physical systems, and formal verification. His work bridges theory and practice, addressing challenges in software reliability and efficiency. Awards: Notable recognitions include the Best ETAPS Paper Award (2023), Distinguished Artifact Award (ESOP 2022), and Teacher of the Year (KTH 2017). His contributions also include grants from the Swedish Foundation for Strategic Research and Wallenberg AI, Autonomous Systems and Software Program (WASP). Advising & Grants: David mentors graduate students (e.g., Gizem Caylak, Viktor Palmkvist) and leads projects funded by WASP, focusing on next-generation compilation techniques. He also contributed to tools like Miking, Timed C, and FlexPRET. Labs & Teams: His team develops frameworks for probabilistic programming and real-time systems, emphasizing collaboration with industry and academia through initiatives like Digital Futures at KTH.

Valentin Robuمشاهده پروفایل

استاد

• Artificial Intelligence
• Decentralized Energy Systems
• Multi-Agent Systems
• +۴ مورد دیگر

Valentin Robu is a Full Professor of Artificial Intelligence for Decentralized Energy Systems at Eindhoven University of Technology (TU/e). He is a Senior Researcher at CWI (National Research Institute for Mathematics and Computer Science, Amsterdam) and holds a visiting appointment at Princeton University's ECE department. His core expertise lies in multi-agent systems and distributed AI, with a focus on applying AI to energy challenges like smart grids, electric vehicle coordination, and renewable integration. He has authored over 150 peer-reviewed publications and won awards such as the 2019 UK Innovation of the Year Award and the 2018 Low Carbon Transport Award. Education and Career: Robu earned his PhD from TU Eindhoven and CWI (2009). Before his current roles, he was an Associate Professor at Heriot-Watt University (UK), a Senior Research Fellow at the University of Southampton, and held visiting roles at Harvard and MIT. He collaborates globally with institutions like MIT, Harvard, and TU Delft. Research Interests: His work addresses challenges in decentralized energy systems, including AI-driven grid optimization, game-theoretic models for renewable integration, and blockchain applications in energy trading. He focuses on ensuring grid stability, fair resource sharing, and sustainable energy access. Awards: In addition to his Innovation and Low Carbon Transport awards, he has been recognized as a 'Best Reviewer' in 2018. His research impacts UN Sustainable Development Goals related to affordable energy and climate action. Grants and Projects: He leads projects like CESI (UK Energy Systems Integration), CEDRI (India Demand Reduction), and ORCA (Offshore Robotics Hub). His work is featured in media outlets like the BBC, Economist, and World Economic Forum. Labs and Teams: He contributes to TU/e's EAISI (Eindhoven AI Systems Institute) and CWI's Intelligent and Autonomous Systems Group. Collaborations span academia and industry, including Scottish Power Energy Networks and Microsoft Research.

Michael Hörnquistمشاهده پروفایل

استاد

• Systems Biology
• Gene Regulatory Networks
• Computational Biology
• +۴ مورد دیگر

Michael Hörnquist serves as Professor and Deputy Head of the Department of Science and Technology (ITN) at Linköping University, concurrently leading the Physics, Electronics and Mathematics (FEM) Division. His responsibilities encompass undergraduate education oversight and management of research activities within FEM, which focuses on communication electronics, mathematics, engineering didactics, physical electronics, and nanotechnology. Hörnquist's research bridges systems biology and computational physics, specializing in gene regulatory network analysis through machine learning techniques including Elastic Net and LASSO regression. His work extends to nonlinear dynamics in aperiodic electronic structures, reflecting FEM's interdisciplinary approach to physics, electronics, and mathematics. This dual focus enables innovative cross-domain applications in biological modeling and electronic systems. Publication trends since 2003 reveal consistent contributions to systems biology, particularly in reverse-engineering biological networks through integrated data approaches. His 2010 DREAM3 challenge-winning work on gene expression prediction exemplifies practical applications of computational methods, while later research (2016) connects molecular oncology with network dynamics. This trajectory demonstrates sustained innovation in translating theoretical models to biological insights across diverse systems. As an academic leader, Hörnquist emphasizes learning as the university's core mission: 'Learning is the essence of what we do as a university, whether it involves teaching, research, or collaboration. It is learning that allows us to grow and mature as individuals.' He actively fosters this philosophy through mentorship and institutional leadership.

• Materials Science
• Superconductivity
• Environmental Engineering
• +۳ مورد دیگر

Hajo Molegraaf is a Researcher at the University of Twente. His work spans interdisciplinary research in materials science and environmental engineering, focusing on superconductivity, oxide materials, and infrastructure sustainability. He has contributed to UN Sustainable Development Goals through studies on carbon footprint reduction in civil infrastructure systems. Research interests include quantum phenomena in oxide heterostructures, deterioration modeling of sewer systems, and application of reinforcement learning for infrastructure maintenance optimization. His work bridges fundamental materials science with applied engineering solutions. Key collaborations include studies in the Netherlands involving large-scale infrastructure analysis and quantum interface research. His contributions have been published in journals like APL Materials and Advanced Functional Materials , with notable citations in both materials science and civil engineering domains.

Daniel Valesinمشاهده پروفایل

استاد

• Probability Theory
• Interacting Particle Systems
• Percolation Theory
• +۳ مورد دیگر

Dr. Daniel Valesin is a Professor in the Department of Statistics at the University of Warwick. His research focuses on probability theory, with a particular emphasis on interacting particle systems, percolation theory, and random graphs. He has contributed to understanding critical phenomena in dynamic networks and stochastic processes on complex structures. Key research interests include contact processes on dynamic graphs, phase transitions in percolation models, and spatial Gibbs random graphs. His work combines rigorous mathematical analysis with probabilistic methods to explore metastability, extinction times, and scaling limits. Recent publications highlight advancements in first-passage percolation on random geometric graphs, adaptive contact processes, and the behavior of multirange percolation on oriented trees. His research often intersects with statistical mechanics and network science, addressing both theoretical and applied stochastic systems. No awards or grants are explicitly listed in the provided materials. He advises no listed students but collaborates extensively with researchers in probability and related fields.

Thomas Frameمشاهده پروفایل

مدرس

• Predictability & Ensemble Forecasting
• Atmospheric Dynamics
• Statistical Methods
• +۳ مورد دیگر

Dr. Thomas Frame is a Lecturer in the Department of Meteorology at the University of Reading, serving as MSc Programme Director and Director of Postgraduate Taught for the School of Mathematical, Computational and Physical Sciences. His research focuses on predictability, ensemble forecasting, atmospheric dynamics, and statistical methods. He leads the FORecasting for SouthEast Asia (FORSEA) project and supervises postdoctoral researchers and PhD students. He holds an editorial role at the Quarterly Journal of the Royal Meteorological Society. His recent work includes studies on North Pacific jet trends, Southeast Asian weather patterns, and probabilistic precipitation forecasting. Frame collaborates internationally on projects like DIAMET, investigating intense cyclones and wind dynamics. His contributions span over 20 peer-reviewed articles, emphasizing advancements in weather prediction accuracy and climate dynamics.

Leonard J. Schulmanمشاهده پروفایل

استاد

• Algorithms
• Coding Theory
• Communication Protocols
• +۵ مورد دیگر

Leonard J. Schulman is a Professor of Computer Science at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science. He holds a B.Sc. (1988) and Ph.D. (1992) in Applied Mathematics from MIT. His research spans algorithms, coding theory, communication protocols, combinatorics, probability, quantum computation, and causal inference. He has directed the Caltech Center for the Mathematics of Information and served as Editor-in-Chief of the SIAM Journal on Computing. His academic roles include Graduate Option Representative for Computer Science and leadership in interdisciplinary initiatives involving quantum computing, information theory, and complexity science. Schulman has held visiting positions at UC Berkeley, the Weizmann Institute, and Georgia Tech. Research interests emphasize causal inference, non-reversible Markovian dynamics, and algorithmic foundations. Notable awards include the FOCS Test of Time Award (2022), UAI Best Paper (2016), and SIAM Fellowship. His work integrates theoretical computer science with applications in economics, biology, and physics. Key contributions include tree code constructions, mixture model identification, and quantum algorithms. Active in education, he teaches advanced courses like CS 150b (Probability and Algorithms). Collaborations span Caltech’s IQIM, CMI, and CMS groups, with postdocs and students advancing interdisciplinary research.

Daniel Cremersمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Deep Learning
• +۱۰ مورد دیگر

Prof. Daniel Cremers is a Professor of Informatics and Mathematics at the Technical University of Munich (TUM), leading the Chair of Computer Vision & Artificial Intelligence within the TUM School of Computation, Information and Technology. His work spans computer vision, machine learning, and mathematical image analysis, with contributions to autonomous systems, SLAM, and self-driving cars. Affiliations: Chair of Computer Vision & AI, TUM School of Computation, TUM. Education: Bachelor's in Mathematics and Physics (University of Heidelberg), Master's in Theoretical Physics (University of Heidelberg), PhD in Computer Science (University of Mannheim). Research Interests: Focuses on computer vision fundamentals, deep learning, variational methods, and real-world applications like autonomous systems. Key areas include 3D reconstruction, SLAM, and geometric processing. Recent Contributions: Over 12 papers accepted to CVPR 2025, including work on probabilistic bundle adjustment and dynamic scene reconstruction. Notable achievements include the ECCV 2024 Koenderink Award for LSD SLAM and the Leibniz Prize (2016). Awards: Leibniz Prize, ERC grants (Starting, Consolidator, Advanced), multiple best paper awards. Teaching: Offers courses like Computer Vision II: Multiple View Geometry and Introduction to Deep Learning. Supervises practical courses on deep learning methods and autonomous systems. Labs/Teams: Leads the Computer Vision Group, actively organizing conferences (e.g., GCPR/VMV 2024) and contributing to open datasets like DeepScenario. Engages in industry through startups and advisory roles.

• Nuclear Engineering
• Computational Mechanics
• Fusion Engineering
• +۳ مورد دیگر

Dr. Michael Bluck is an Associate Professor in Nuclear Engineering at Imperial College London's Department of Mechanical Engineering. He serves as Director of the Centre for Nuclear Engineering and the Rolls-Royce Nuclear University Technology Centre, coordinating research, teaching, and policy advice across nuclear fission and fusion. His expertise spans advanced computational methods applied to nuclear reactor design, including spherical tokamaks and small modular reactors (SMRs). He holds a BSc (Hons) in Mathematics (Mathematical Physics) from the University of Kent and a PhD from Imperial College on integral equation methods for wave propagation. Bluck's research focuses on integrating computational mechanics with nuclear engineering challenges, such as thermal mixing in reactor components and uncertainty quantification in fusion economics. He leads interdisciplinary projects involving Bayesian networks for decision support, CFD simulations for fluid dynamics, and collaboration with industry partners like Rolls-Royce. His work addresses both technical and policy aspects of nuclear energy, including cost-driver analysis and government advisory roles. Key affiliations include the Applied Mechanics and Nuclear Engineering groups at Imperial. His research outputs span computational methods (e.g., POD/SPOD analysis, Gaussian process emulation) and socio-economic studies on fusion commercialization. Though no explicit awards are listed, his contributions to nuclear innovation are reflected in his leadership roles and collaborative industry projects. Bluck’s advising and grants involve advancing computational tools for nuclear systems, with notable projects on SMRs and submarine reactor technologies. His lab, the Centre for Nuclear Engineering, fosters cross-disciplinary research and education in nuclear science and technology.

Roni Rosenfeldمشاهده پروفایل

استاد

• Epidemic Forecasting
• Computational Biology
• Machine Learning
• +۴ مورد دیگر

Roni Rosenfeld is a Professor in the School of Computer Science at Carnegie Mellon University, holding joint appointments in Machine Learning, Language Technologies, Computer Science, and Computational Biology. He has served as Head of the Machine Learning Department from 2018 to 2024. His education includes a BSc in Mathematics and Physics from Tel Aviv University (1985) and a PhD in Computer Science from Carnegie Mellon University (1994). His research focuses on epidemic forecasting via the Delphi Group, named a National Center for Epidemic Forecasting by the U.S. CDC. Past work includes speech recognition, SLT4D (Spoken Language Technologies for Development), and ML4SG (Machine Learning for Social Good). He has advised over a dozen PhD students and mentored postdocs in epidemiology, computational biology, and speech technologies. Rosenfeld has published 150+ peer-reviewed articles and received the Spira Teaching Excellence Award (2017) and Allen Newell Medal for Research Excellence (1992, 2022). His current projects emphasize real-time epidemic tracking, public health data quality, and collaborative forecasting frameworks. Key collaborations include the CDC-funded Delphi Group’s work on influenza, dengue, and pandemic surveillance systems. He also pioneered viral spread analysis in low-literacy regions through speech-based technologies, addressing global development challenges.

• Numerical Linear Algebra
• High Performance Computing
• Machine Learning
• +۵ مورد دیگر

Ren-Cang Li is a Professor of Mathematics at The University of Texas at Arlington, with affiliations including past roles as Chair Professor at Hong Kong Baptist University and positions at University of Kentucky and Oak Ridge National Laboratory. He earned his BS from Xiamen University (1985), MS from Chinese Academy of Sciences (1988), and PhD in Applied Mathematics from UC Berkeley (1995). His research focuses on numerical linear algebra, eigenvalue problems, high-performance computing, and machine learning, with impactful contributions like secular equation solvers in LAPACK and work on matrix equations. Awards include the NSF CAREER Award (1999) and multiple distinguished paper awards. Education: PhD in Applied Mathematics, UC Berkeley, 1995 MS in Computational Mathematics, Chinese Academy of Sciences, 1988 BS in Computational Mathematics, Xiamen University, 1985 Research interests span numerical algorithms, eigenvalue computations, optimization, and applications in machine learning. Notable contributions include methods for large-scale eigenvalue problems, matrix equations, and compressed sensing. His work on the secular equation solver is foundational in scientific computing libraries. Key Awards: Friedman Memorial Prize in Applied Mathematics (1996) NSF CAREER Award (1999) Three Distinguished Paper Awards from ICCM (2018-2020) INFORMS Data Mining Best General Paper (2019) Advising and Grants: Supervised numerous PhD students and led federally funded projects totaling over $5M, including work on probabilistic graph learning and nonlinear matrix equations. Active in editorial roles for journals like SIAM Journal on Matrix Analysis and Mathematical Communications .

Pierre-Simon Laplaceمشاهده پروفایل

استاد

• Celestial Mechanics
• Mathematical Physics
• Probability Theory
• +۳ مورد دیگر

Pierre-Simon Laplace (1749-1827) was a French mathematician and astronomer known as the 'lawgiver of French science.' He held key roles at the École Militaire and the Académie des Sciences (Institut de France). His work spanned celestial mechanics, probability theory, and mathematical physics. Notable works include Traité de Mécanique Céleste (1799–1825) and Théorie analytique des probabilités (1812). He developed the nebular hypothesis for solar system formation and pioneered statistical methods. Education: Studied at Benedictine College in Beaumont-en-Auge and the University of Caen (1766–1768). Left without a degree to pursue work in Paris with Jean d’Alembert. Research focused on planetary motion, gravitational interactions, and probabilistic reasoning. His political adaptability allowed him to retain influence across the French Revolution, Napoleonic era, and Bourbon Restoration. Knighted as Marquis de Laplace by the monarchy in 1817. Legacy: The Académie des Sciences suspended meetings upon his death (1827) as a rare honor. His contributions remain foundational in astronomy and statistics.

Sokratis Makrogiannisمشاهده پروفایل

استاد

• Medical Image Computing
• Machine Learning
• Biomedical Imaging
• +۳ مورد دیگر

Sokratis Makrogiannis is a Professor in the Division of Physics, Engineering, Mathematics, and Computer Science at Delaware State University, where he directs the Mathematical Imaging and Visual Computing Group. His research focuses on medical image computing, machine learning, and visualization, with applications in biomedical imaging and computer vision. He holds a PhD in Electronics and Informatics from the University of Patras, Greece, and completed postdoctoral fellowships at the University of Pennsylvania and Wright State University. His work bridges industrial collaborations (e.g., GlaxoSmithKline, General Electric) and academic research, emphasizing computational neuroanatomy, image analysis algorithms, and medical imaging techniques. He has secured funding from NIH, NSF, DoD, and the State of Delaware. His teaching spans advanced topics like image processing, pattern recognition, and discrete mathematics. Collaborators include leading institutions such as the University of Delaware, Lund University, and the National Institute on Aging (NIH). Research interests include image analysis, machine learning applications in healthcare, and automated tissue quantification. His publications address cell segmentation, osteoporosis classification, and MRI/CT segmentation techniques. He serves as editor for journals like BioMedical Engineering OnLine and Frontiers in Computer Science, contributing to academic dissemination and peer review. The MIVIC Lab under his leadership develops innovative imaging solutions for clinical and environmental challenges.

Keivan Mallahi Karaiمشاهده پروفایل

استادیار

• Group Theory
• Probability Theory
• Harmonic Analysis
• +۵ مورد دیگر

Prof. Keivan Mallahi Karai is an Assistant Professor of Mathematics at the School of Computer Science & Engineering, Constructor University Bremen. His work focuses on group theory, probability, harmonic analysis, and their applications in decision-making and cryptography. He holds a Ph.D. in Mathematics from Yale University (2006) and a B.Sc. from Sharif University of Technology (1999). His research explores dynamics of group actions, stochastic models, and geometric decision frameworks, with notable contributions to the Erdős–Ko–Rado property and applications of arithmetic groups in cryptography. Key research areas include analysis on groups, harmonic analysis applications in wave phenomena, and probabilistic methods in decision theory. Recent publications address spectral independence in random systems, geometric decision models (cube/disk models), and sofic approximations of groups. His work bridges pure mathematics with applied domains like financial modeling and cryptography. Professional experience includes a postdoctoral role at Universität Bonn (2006). Collaborations span stochastic decision-making frameworks, entropy in hypergroups, and extreme value laws for unipotent flows. Research trends emphasize interdisciplinary applications of abstract algebra and probability theory.