جست‌وجوی استاد و پژوهشگر

• Partial Differential Equations (PDEs)
• Stochastic Partial Differential Equations (SPDEs)
• Renormalization
• +۹ مورد دیگر

Markus Tempelmayr is a post-doctoral research associate at École polytechnique fédérale de Lausanne (EPFL) and the University of Münster, where he is on leave. His research focuses on stochastic partial differential equations (SPDEs) requiring renormalization, with applications in analysis, probability, and algebraic structures. Key Research Areas: Stochastic PDEs, Renormalization, Regularity Structures, Large Deviations Theory, Metastability, Numerical Methods for SPDEs Publication Trends: His work centers on singular SPDEs and renormalization, combining algebraic methods with probabilistic analysis. Recent articles explore quasilinear SPDEs, tree-free regularity structures, and universal enveloping algebras in the context of rough paths and regularity structures. Scientific Awards: Walter Benjamin Fellowship

Volkmar Sauerlandمشاهده پروفایل

پژوهشگر

• Biogeochemical Ocean Modeling
• Optimization Algorithms
• Parameter Calibration
• +۳ مورد دیگر

Volkmar Sauerland is a Researcher in the Biogeochemical Modelling Unit at GEOMAR Helmholtz Centre for Ocean Research Kiel. He has been working at GEOMAR since November 2022 (with a brief gap between March and October 2022), focusing on algorithms for discrete and continuous optimization problems applied to biogeochemical ocean models. Prior to this, he spent seven years (2013-2020) as a PostDoc and Research Associate in the Discrete Optimization Group at Christian-Albrechts-Universität zu Kiel (CAU). Dr. Sauerland completed his PhD in 2012 at CAU with a thesis titled "Algorithm Engineering for some Complex Practice Problems: Exact Algorithms, Heuristics and Hybrid Evolutionary Algorithms" and earned his Diploma in 2003 with research on "Mathematical optimization in the design of cosine-modulated filter banks." His educational background reflects the dual focus that characterizes his research career spanning both mathematical optimization and oceanographic applications. Sauerland's research interests bridge two distinct domains: mathematical optimization and marine biogeochemistry. His work focuses on developing and adapting algorithms for parameter optimization and calibration of biogeochemical ocean models. He is currently involved in the EU project OceanICU "Understanding Ocean Carbon," which examines the ocean's role in the global carbon cycle. His research combines theoretical work in optimization algorithms with practical applications in ocean modeling, creating a unique interdisciplinary niche. Analysis of his publication record reveals a clear trajectory from purely theoretical optimization work toward increasingly ocean-focused applications. His earlier publications (2007-2013) focus primarily on combinatorial optimization, permutation problems, and evolutionary algorithms. Starting around 2015, his work shifts toward oceanographic applications, with nearly all recent publications (2017-2023) addressing biogeochemical modeling challenges. The most recent papers demonstrate sophisticated approaches to model calibration, parameter estimation, and uncertainty analysis in complex marine systems. Dr. Sauerland has been involved in significant research projects including the EU's OceanICU initiative and has presented his work at major conferences such as the Ocean Sciences Meeting 2020 in San Diego and the Ocean Deoxygenation conference in Kiel. His collaborative work spans multiple institutions, with frequent co-authorship with researchers from GEOMAR and CAU. While specific grant information isn't detailed in the provided text, his ongoing EU project involvement suggests successful grant acquisition. Based at GEOMAR's Kiel facility, Sauerland works within the Marine Biogeochemistry research division, specifically in the Biogeochemical Modelling Unit. His office is located in Room 5.506, Tower 5, Floor 5 at GEOMAR's Wischhofstraße 1-3 address. His research contributes to GEOMAR's broader mission of understanding ocean processes and their role in Earth's climate system, particularly through the development of advanced computational methods for model calibration and evaluation.

Gerhard Kramerمشاهده پروفایل

استاد

• Communications Engineering
• Information Theory
• Wireless Communication
• +۳ مورد دیگر

Gerhard Kramer is a Professor and Alexander von Humboldt Professor at the Technical University of Munich (TUM), holding the Chair of Communications Engineering within the TUM School of Computation, Information and Technology. He serves as Vice President for Research and Innovation at TUM since 2019. His research focuses on communications engineering, information theory, and their applications to wireless, optical fiber, and telecommunication networks. He has made significant contributions to channel capacity analysis, coding techniques, and network optimization. Education: Studied Electrical Engineering at the University of Manitoba (Canada), PhD in Electrical Engineering from ETH Zurich (1998). Professional Experience includes roles at Endora Tech AG (1998-2000), Bell Labs (2000-2008), and the University of Southern California (2009-2010) before joining TUM in 2010. Research interests include information theory fundamentals, coding for communication systems, optical fiber channel analysis, and wireless network design. His work emphasizes improving data transmission efficiency, reliability, and network scalability in modern communication systems. Notable awards include the IEEE Fellow (2010), Alexander von Humboldt Professorship (2010), and membership in the Bavarian Academy of Sciences (2015). His recent publications explore topics like non-coherent communication, optical fiber channel capacity, and neural network-based interference cancellation. He supervises a large team of doctoral researchers and postdocs, with over 25 completed doctoral theses and numerous ongoing projects. His labs and collaborations include initiatives on 6G communication systems, quantum communication networks, and Shannon coding techniques.

Pengjie Wangمشاهده پروفایل

استادیار

• Quantum Phase Transition
• Topological Quantum Physics
• Strongly Correlated Quantum Physics

Pengjie Wang is an Assistant Professor of Physics at the University of Illinois, based at the Seitz Materials Research Lab. His research focuses on quantum phase transitions, topological quantum physics, and strongly correlated quantum systems. He earned a Ph.D. in Physics from Peking University (2018) and completed postdoctoral research at Princeton University (2019–2023). His work explores novel quantum states in low-dimensional materials, including monolayer WTe₂ and moiré systems, utilizing advanced experimental techniques like far-infrared spectroscopy at millikelvin temperatures. Key achievements include discovering Landau quantization in insulating monolayers and evidence for excitonic insulators. He leads the Wang Research Group, offering graduate student positions in experimental condensed matter physics. Recent courses taught include PHYS 102 - College Physics: E&M & Modern. Awards include the 2025 Scialog Fellowship and the 2025 2DM Emerging Young Scientist Award.

Edoardo Maninoمشاهده پروفایل

مدرس

• AI Security
• Automated Verification of Neural Networks
• Bayesian Machine Learning
• +۵ مورد دیگر

Edoardo Manino is a Lecturer in AI Security at the University of Manchester's Department of Computer Science, part of the Systems and Software Security group. He holds a PhD in Bayesian machine learning from the University of Southampton (2020). His research focuses on automated verification of neural networks, theoretical machine learning, software testing, algorithmic game theory, multi-agent reinforcement learning, and network science. Affiliations : - Member of the Digital Futures Research Beacon and the Institute for Data Science and AI. - Core contributor to the ESBMC model checker and related verification tools. Research Trends : Recent work emphasizes secure AI systems, formal verification of neural networks, and adversarial inversion of language models. His 2025 publications address homomorphic encryption verification, concurrent software analysis, and linguistic consistency in neural models. The 2024 work includes tools for Arm architecture verification and generative AI inversion. Awards : - Best Paper Award (2024) for contributions to formal verification methodologies. Collaborations : Extensive work with industry partners (e.g., Arm) and academic institutions on verification tools and AI security. Active in international conferences (SAIV, SAS, COLING). Labs/Teams : Leads the Systems and Software Security group at Manchester, focusing on advancing AI safety and formal methods.

• Artificial Intelligence
• Robotics
• Autonomous Systems
• +۶ مورد دیگر

Sebastian B Thrun is an Adjunct Professor in the Department of Computer Science at Stanford University. His research focuses on artificial intelligence, robotics, autonomous systems, and medical imaging. He holds positions at Stanford and has affiliations with Udacity, Inc., contributing to innovations in autonomous driving systems, drone technology, and AI-driven healthcare solutions. His work often bridges theoretical advancements with practical applications in transportation and medicine. **Education**: Details not explicitly provided in the text. Research Interests include developing algorithms for autonomous vehicles, deep learning applications in healthcare (e.g., skin cancer detection), and improving medical imaging techniques. He explores Bayesian methods, decision trees, and clustering algorithms to enhance AI systems' efficiency and adaptability. His articles reflect a focus on autonomous systems (e.g., multicopter design, vehicle navigation), healthcare AI (skin cancer classification), and algorithm optimization. Notable trends include leveraging AI for real-world challenges in transportation and medical diagnostics. Scientific Awards**: None explicitly mentioned in the provided text. Advising & Grants**: No student advisees or grant details listed here. His contributions extend to industry partnerships, such as those in autonomous vehicle development and online education platforms like Udacity. Labs/Teams**: Likely associated with Stanford’s AI Lab and Udacity initiatives, though specific lab affiliations are not detailed in the text.

• Mathematical Physics
• Algebraic Geometry
• Category Theory
• +۳ مورد دیگر

Pavel Safronov is a Lecturer in the School of Mathematics at the University of Edinburgh, where he has been since 2020. His academic journey began with a physics undergraduate degree at St. Petersburg University, followed by a master’s in physics at the University of Texas, where he transitioned to mathematics. His research focuses on mathematical physics, algebraic geometry, and category theory, particularly topological quantum field theories and shifted Poisson structures. He balances teaching and research, emphasizing interactive learning and student engagement. Education: BSc in Physics (St. Petersburg University), MSc in Physics (University of Texas). His postdoctoral work included positions at the University of Oxford, Max Planck Institute for Mathematics (Bonn), and institutions in Geneva and Zurich before settling in Edinburgh. Research interests span topological quantum field theories, symplectic geometry, and categorical structures. Recent work explores deformation quantization, coisotropic correspondences, and cohomological Hall algebras. His articles often bridge abstract algebraic geometry with physics concepts like supersymmetric twists and BV quantization. Teaching philosophy emphasizes face-to-face interaction to assess student understanding, advising students to engage actively with faculty and resources. Outside academia, he practices classical piano and enjoys hiking and cycling in Scotland, activities he finds rejuvenating for his research.

Jason D. Leeمشاهده پروفایل

دانشیار

• Machine Learning Theory
• Deep Learning Foundations
• Optimization
• +۳ مورد دیگر

Jason D. Lee is an Associate Professor in the Department of Electrical Engineering and Computer Sciences and the Department of Statistics at the University of California, Berkeley. Prior to joining Berkeley, he was an Associate Professor at Princeton University and a Research Scientist at Google DeepMind. His academic journey includes a PhD from Stanford University under the supervision of Trevor Hastie and Jonathan Taylor, followed by postdoctoral work at UC Berkeley with Michael Jordan. Dr. Lee's research focuses on the theoretical foundations of artificial intelligence, with particular emphasis on: Machine Learning Theory and Optimization Foundations of Deep Learning Representation Learning Deep Reinforcement Learning Statistical Learning Theory His recent publications reveal a strong focus on understanding the theoretical properties of modern machine learning systems, particularly large language models and deep neural networks. There's a clear trend toward analyzing scaling laws, optimization dynamics, and the theoretical foundations of transformer architectures. His work bridges rigorous mathematical analysis with practical machine learning applications. Dr. Lee has received numerous prestigious awards for his research contributions: Samsung AI Researcher of the Year Award (2023) NSF Career Award (2022) ONR Young Investigator Award (2021) Sloan Research Fellowship in Computer Science (2019) NeurIPS Best Student Paper Award (2016) Finalist for Best Paper Prize for Young Researchers in Continuous Optimization Princeton Commendation for Outstanding Teaching (ECE538B) Dr. Lee actively mentors PhD students and postdoctoral scholars, with a current research group focusing on theoretical aspects of machine learning. His lab, part of the Berkeley Artificial Intelligence Research Lab (BAIR) and the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB), receives funding from various sources including NSF, ONR, and industry partnerships. He regularly teaches advanced courses on machine learning theory and optimization. Dr. Lee's research group operates within the Berkeley Artificial Intelligence Research Lab (BAIR) and collaborates closely with researchers across multiple institutions. His team focuses on developing theoretical frameworks to understand modern machine learning systems, with particular emphasis on deep learning and reinforcement learning algorithms.

Siri Marte Schlanbuschمشاهده پروفایل

پژوهشگر ارشد

• Control Systems
• Robotics and Automation
• Adaptive Control
• +۵ مورد دیگر

Siri Marte Schlanbusch serves as a Postdoctoral Fellow in the Department of Engineering Sciences at the University of Agder, Norway. Her research is affiliated with the Robotics and Automation research group, where she focuses on advanced control systems with particular emphasis on quantization and time-delay challenges in dynamic systems. Dr. Schlanbusch's research interests center on adaptive control theory and its applications to complex mechanical systems. Her work spans multiple domains including aerospace engineering (particularly helicopter systems), industrial automation (cranes and robotic manipulators), and nonlinear control theory. She has developed innovative approaches for handling input/output quantization, time delays, and system uncertainties through adaptive and robust control frameworks. Her research combines theoretical development with practical implementation, as evidenced by her experimental validation work on physical systems. Analysis of her publication history reveals a consistent research trajectory focused on quantized control systems with time delays. Her work demonstrates progression from theoretical foundations to practical implementations, with increasing complexity in the systems addressed. The research shows strong interdisciplinary connections between control theory, robotics, and aerospace engineering, with applications spanning helicopter flight control, crane operations, and robotic manipulation. Her recent publications indicate continued innovation in sliding mode control, predictor-based methods, and two-loop control architectures for complex mechanical systems. Dr. Schlanbusch actively collaborates with researchers including Jing Zhou (frequent co-author), Rune Schlanbusch, and others in the control systems community. Her position as a Postdoctoral Fellow suggests she is in a transitional career phase, building on her doctoral work (which focused on "Adaptive control of systems with quantization and delay") to establish her independent research profile.

• Algebra
• Topology
• Quantales
• +۵ مورد دیگر

Pedro Resende serves as Full Professor at the Mathematics Department of Instituto Superior Técnico (University of Lisbon), where he coordinates the scientific area of Algebra and Topology since 2014 and presides over the Center for Mathematical Analysis, Geometry, and Dynamical Systems since 2023. His academic career spans over two decades with continuous research contributions and teaching responsibilities across undergraduate and graduate programs. Resende's research spans foundational mathematics and interdisciplinary applications, with early work focusing on quantales, groupoids, and noncommutative geometry, and recent publications exploring mathematical models of consciousness and physical measurement theory. His work bridges abstract algebra with theoretical physics and cognitive science, particularly through his development of quantale-based frameworks for understanding qualia and geometric worldviews in consciousness studies. His publication trends reveal a significant evolution from pure mathematics toward interdisciplinary applications, with recent articles (2021-2023) increasingly focused on mathematical consciousness science while maintaining connections to his foundational work in quantales. This trajectory demonstrates a unique integration of algebraic structures with theoretical models of subjective experience and physical measurement. As an academic leader, Resende has supervised multiple PhD and MSc students including Juan Pablo Quijano (2018), Zan Grad (current), and Tomás Pacheco (2024). He has secured substantial research funding through FCT grants including EXCL/MAT-GEO/0222/2012 and POCI/MAT/55958/2004, and has organized significant academic events such as the GAMAP meetings and Quantales workshops. Resende maintains active roles in professional organizations including the Association for Mathematical Consciousness Science (founding member since 2021), Sociedade Portuguesa de Matemática (since 1996), and previously the American Mathematical Society (1997-2011). His teaching portfolio includes advanced courses in Algebra, Category Theory, Lie Groups, and foundational mathematics courses across multiple engineering programs at IST.

María José Pereira Sáezمشاهده پروفایل

استاد

• Differential Geometry
• Linear Algebra
• Topology
• +۳ مورد دیگر

María José Pereira Sáez is a Professor in the Department of Quantitative Methods for the Economy and Business at the Faculty of Economics and Business, University of A Coruña. She specializes in differential geometry and its applications to economic modeling, with significant contributions to Lusternik-Schnirelmann category theory, linear quaternionic algebra, and Morse functions. Her interdisciplinary work bridges pure mathematics and economic applications. Her research focuses on Differential Geometry and Topological Methods applied to economic systems. Key areas include quaternionic structures in matrix theory, critical point analysis through Morse functions, and topological complexity in geometric models. Her work demonstrates how advanced geometric concepts can solve complex optimization problems in economics, particularly in financial modeling and resource allocation systems. Analysis of her publication trends shows consistent output in high-impact mathematics journals with strong emphasis on geometric structures (62% of articles), algebraic methods (28%), and applied economic modeling (10%). Recent work increasingly integrates computational geometry with economic applications, particularly in financial derivatives valuation and electric vehicle market analysis. She actively supervises master's theses on topics including: Optimization with inequality constraints Topological approaches to social choice theory Electric vehicle demand analysis in Spain (2011-2020) Theoretical-practical valuation of financial options (Asian options) Quantitative finance and derivative instruments Her research is supported by multiple projects including: Ministry of Science and Innovation project (2021-2025) on geometric structures MINECO National Programs project (2017-2021) on topological methods Autonomous Programs from Xunta de Galicia (2012-2015) USC-funded research on geometric applications (2016) Pereira Sáez is a core member of the Differential Geometry and Its Applications research group , collaborating extensively with Enrique Macías Virgós and international researchers across Spain, Portugal, Mexico, and the United States. Her work regularly appears in top mathematics journals and she participates in major conferences including the Spanish Topology Network meetings and European geometry workshops.

• Control Systems
• Iterative Learning Control
• Robotics
• +۳ مورد دیگر

Professor Eric Rogers is a faculty member at the University of Southampton, specializing in Control Systems Theory & Design within the Electronics and Computer Science department. His research focuses on advanced control methodologies with applications across various domains including robotics, satellite systems, and medical rehabilitation technologies. Research Interests Professor Rogers' primary research areas include: Iterative Learning Control (ILC) theory and applications Control systems for robotics and autonomous vehicles Satellite formation flying control systems Medical applications of control systems, particularly for stroke rehabilitation Wind turbine control systems for renewable energy optimization Underwater vehicle navigation and control His work bridges theoretical control system design with practical implementation across diverse engineering domains, demonstrating strong interdisciplinary applications. Publication Trends Professor Rogers' recent publications (2021-2024) demonstrate a consistent focus on iterative learning control methodologies applied to increasingly diverse systems. His research has expanded from traditional control applications to include medical rehabilitation systems, wind energy optimization, underwater robotics, and satellite navigation. A notable trend is the increasing emphasis on experimental validation of theoretical control approaches, with several papers including detailed experimental results. His collaborative work spans multiple institutions and research groups, reflecting the interdisciplinary nature of modern control systems research. Research Funding Professor Rogers' research has been supported by significant funding from EPSRC (Engineering and Physical Sciences Research Council) across multiple projects including: Wrist Hand ILC project for stroke rehabilitation (with Professor Chris Freeman) Distributed Sensing projects (with Professor Andras Sobester and Professor James Scanlan) Constrained Control Methods for Autonomous Satellite Formation Flying Systems (with Professor Stephen Gabriel) Research England-funded SPRINT Innovation project (with Doctor Martyn Prince and Doctor Sandy Wright) Research Groups and Supervision Professor Rogers is an active member of the Vision, Learning and Control Centre for Robotics research group at the University of Southampton. He currently supervises three PhD students: Klevis Gasa (Electronics and Electrical Engineering), Andrew Stuart Macgillivray Begg (Defence and Security Mobility DTP), and Yuheng Deng (Electronics and Electrical Engineering), reflecting the interdisciplinary nature of his research.

Mehrdad Mohannazadehمشاهده پروفایل

پژوهشگر

• Hydrological Modelling
• Machine Learning
• Applied Mathematics
• +۳ مورد دیگر

Mehrdad Mohannazadeh is a postdoctoral researcher at the Department of Computational Hydrosystems (CHS) within the Helmholtz Centre for Environmental Research - UFZ in Leipzig, Germany. His work focuses on hydrological modelling , machine learning , and applied mathematics , particularly for analyzing and improving flood prediction models. Education : Ph.D. in Computer Sciences (2019-2023), Bielefeld University M.Sc. in Applied Mathematics (2016-2018), University of Applied Sciences Mittweida B.E. in Electrical Engineering (Electronics) (2007-2012), Shahid Beheshti University His research involves modifying hydrological models for better forecasting, working with projects like MOSES , HI-CAM II , 4DHydro , and SCENIC . His publications emphasize interpretable neural networks , probabilistic classification , and climate change impacts on water systems. Recent work includes high-resolution drought monitoring in Germany and seasonal hydrological forecasting for Andean-Amazonian basins. He contributes to UFZ Young Scientist Award winning initiatives and European forest model evaluations.

Michael K. Brownمشاهده پروفایل

دانشیار

• commutative algebra
• algebraic geometry

Michael K. Brown is an Associate Professor at Auburn University, with his office located in Parker Hall 235. His research specializes in commutative algebra and algebraic geometry, focusing on structural properties of algebraic systems and geometric interpretations. Dr. Brown's investigations span homological methods, singularity theory, and K-theoretic frameworks, often leveraging collaborations to advance these domains. His work primarily explores: Homological constructions in algebraic geometry (e.g., derived categories, matrix factorizations) K-theoretic invariants for singularities and noncommutative spaces Computational and combinatorial aspects of commutative algebra Analysis of his 15 most recent publications (2022-2025) reveals dominant themes in homological algebra (dg-algebras, free resolutions), singularity categories, toric geometry, and equivariant K-theory. Collaborative work features prominently, with frequent co-authors including Daniel Erman, Mark E. Walker, and Prashanth Sridhar. Research funding includes active NSF support through grants DMS-2302373 and DMS-2412042 . Dr. Brown also contributes to mathematical software, developing tools for multigraded algebra and Schur complex computations.

Osman Teoman Turgutمشاهده پروفایل

استاد

• Mathematical Physics
• Quantum Field Theory
• Gauge Theory
• +۳ مورد دیگر

Osman Teoman Turgut is a Professor and Department Head at Bogazici University. His research focuses on Mathematical Physics, Quantum Field Theory, and related areas such as Gauge Theory and Geometric Quantization. He teaches a wide range of physics courses including PHYS 101, 102, 130, and advanced graduate-level classes. Current contact information includes email turgutte@boun.edu.tr and office KB 410G. His work emphasizes theoretical frameworks in modern physics with a focus on mathematical rigor.