جستجوی استاد راهنمای دانشگاهی

• Photogrammetry
• Geoinformation
• Remote Sensing
• +۷ مورد دیگر

Prof. Christian Heipke is a distinguished academic serving as Dean of the Faculty of Civil Engineering and Geodetic Science at Leibniz University Hannover, Germany. He also holds the position of Executive Director at the Institute of Photogrammetry and GeoInformation (IPI), one of the leading research institutions in geospatial sciences within the faculty. His leadership extends across multiple committees including the Curriculum and Teaching Committee, Admissions and Examination Boards for Geodetic Science and Geoinformatics, and Navigation and Environmental Robotics. As a Professor at IPI, he maintains active research while overseeing significant academic and administrative responsibilities at the university. Professor Heipke's research spans multiple domains within geospatial sciences, with particular emphasis on: Advanced photogrammetric techniques and algorithms Remote sensing applications for environmental monitoring Computer vision approaches for geospatial data analysis Urban development monitoring using satellite imagery Machine learning applications in geoinformatics Disaster prediction and management systems His recent scholarly output reveals a strong focus on integrating cutting-edge computer vision and deep learning techniques with traditional photogrammetric methods. Analysis of his 15 most recent publications shows a clear trajectory toward more sophisticated AI-driven approaches for processing geospatial data, with particular attention to time-series analysis, uncertainty quantification, and multi-view systems. His work bridges theoretical advancements with practical applications in flood forecasting, deforestation monitoring, urban planning, and construction materials analysis. The geographic scope of his research has expanded significantly, with recent projects focusing on international case studies in the Philippines and tropical regions. Professor Heipke leads the Institute of Photogrammetry and GeoInformation, a major research hub that has celebrated 75 years of contributions to the field. His leadership extends to the Graduiertenkolleg 2159: "Integrity and Collaboration in Dynamic Sensor Networks," where he serves as a professor overseeing doctoral research. The institute maintains state-of-the-art facilities for processing satellite imagery, aerial photography, and developing novel algorithms for geospatial data analysis. Under his direction, the institute has strengthened its international collaborations and interdisciplinary research approaches, particularly in addressing Sustainable Development Goals through geospatial technologies.

• Optimization for Machine Learning
• Collaborative Learning
• Distributed Learning
• +۷ مورد دیگر

Sebastian U. Stich is a tenured faculty member at the CISPA Helmholtz Center for Information Security , where he has been since December 2021. He is also a member of the European Lab for Learning and Intelligent Systems (ELLIS) since June 2020. His research focuses on optimization methods for machine learning, collaborative learning algorithms, privacy and security in distributed systems, and theoretical foundations of deep learning. Stich received his PhD in Theoretical Computer Science from ETH Zurich (2014), following a Master's in Mathematics at the same institution (2010-2014). Prior to CISPA, he worked as a research scientist at EPFL (2016-2021) and held positions at ETH Zurich and ICTEAM/CORE. He has been awarded the ERC Consolidator Grant 2024 , Google Research Scholar Award (2023), and Meta Privacy-Enhancing Technologies Research Award (2022). His team includes Dr. Anton Rodomanov (since 2023), Dr. Rotem Mulayoff (since 2024), Xiaowen Jiang (2023), Yuan Gao (2023), and notable alumni like Anastasia Koloskova (defended 2023). Stich actively organizes workshops (e.g., NeurIPS OPT 2024) and serves on editorial boards ( Journal of Optimization Theory and Applications , Transactions on Machine Learning Research ). He teaches advanced courses in optimization at Saarland University and has held visiting positions at MIT. Key scientific contributions include: Developing ProgFed for progressive federated learning (2021) Creating ProxSkip to accelerate communication in federated settings (2022) Formalizing SCAFFOLD with control variates for FL (2020) Introducing RelaySum mechanism for decentralized learning (2021) Proposing Lookahead-Minmax for GAN training (2021) His work addresses fundamental challenges in: Decentralized optimization theory Communication-efficient algorithms Privacy-preserving model training Handling heterogeneous data distributions Stochastic gradient dynamics Second-order optimization methods

Michael Ulbrichمشاهده پروفایل

استاد

• Nonlinear Optimization
• Optimal Control
• Numerical Analysis
• +۳ مورد دیگر

Prof. Dr. Michael Ulbrich is a full professor and Chair of Mathematical Optimization at the Technical University of Munich (TUM), within the School of Computation, Information and Technology. He has held this position since 2006 and previously served as Dean of Studies (2007–2010) and Vice Dean of the Faculty of Mathematics (2012–2015). His research focuses on nonlinear optimization, optimal control, and numerical analysis, with applications in fluid dynamics, shape optimization, and PDE-constrained systems. He leads projects in the DFG SPP 1962 and IGDK 1754, and has received prestigious awards including the Howard Rosenbrock Prize (2015) and the Doctoral Award from the TUM Association of Friends (1996). Ulbrich is Editor-in-Chief of Optimization and Engineering and contributes to multiple journals. His work bridges theoretical foundations and practical applications, including CO2 sequestration, fluid-structure interaction, and distributed optimization algorithms. Education: PhD (1996), Habilitation (2002) in Mathematics at TUM. Research stays at Rice University (USA) under DFG funding. Research Areas: Semismooth Newton methods, PDE-constrained optimization, optimal control of Navier-Stokes equations, and distributed parameter systems. Awards: Rosenbrock Prize, Teaching Excellence Awards, and recognition for doctoral work. Leadership Roles: Department Head of Mathematics (2022–), Member of TUM Senate (2019–2022), and Co-Chair of GAMM 2018. Ulbrich has authored influential textbooks like Semismooth Newton Methods for Variational Inequalities and Nichtlineare Optimierung . His recent projects include OptiGeoS (2024–2026) and collaborations on nonsmooth optimization and stochastic algorithms. His academic contributions span over 100 publications, emphasizing both algorithmic innovation and rigorous mathematical analysis.

• Artificial Intelligence
• Machine Learning
• Cognitive Robotics
• +۳ مورد دیگر

Dr. Ulrike Kuhl is a Researcher at the University of Bielefeld, serving as Project Coordinator for the AI Academy OWL at the Research Institute for Cognition and Robotics and as Scientific Project Coordinator within the Faculty of Engineering's Machine Learning Group. Her office is located at CITEC 2-412, and she can be reached at +49 521 106-12125. Dr. Kuhl's research spans several interconnected domains at the forefront of human-centered AI development: Explainable Artificial Intelligence (XAI) frameworks and their psychological impact Cognitive learning enhanced through AI technologies Counterfactual explanation methodologies and user behavior Human-AI interaction design principles Applications of machine learning in environmental monitoring and sports analytics Analysis of Dr. Kuhl's publication trajectory reveals a consistent focus on bridging the gap between sophisticated AI systems and human understanding. Her work particularly examines how different explanation types affect user trust and decision-making, with recent publications exploring counterfactual explanations in contexts ranging from water distribution networks to educational technology. She has developed experimental frameworks like the 'Alien Zoo' methodology for systematically studying explanation usability. Dr. Kuhl actively contributes to the Center for Cognitive Interaction Technology (CITEC) at the University of Bielefeld, an interdisciplinary hub where computer scientists, engineers, and cognitive scientists collaborate on next-generation interactive technologies. Through her coordination of the AI Academy OWL initiative, she facilitates regional collaboration between academic researchers and industry partners to advance artificial intelligence applications in the Ostwestfalen-Lippe region.

• Nonparametric Statistics
• High Dimensional Data Analysis
• Statistical Methods in Finance
• +۷ مورد دیگر

Vladimir Spokoiny is a Professor at the Departments of Mathematics and Economics of the Humboldt University of Berlin and Head of the Research Group "Stochastic Algorithms and Nonparametric Statistics" at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany. His research spans multiple areas of statistics, machine learning, and financial mathematics, with significant contributions to nonparametric statistics, high-dimensional data analysis, and statistical methods in finance. Spokoiny received his M.Sc. in applied mathematics from the Moscow Institute of Railway Engineering in 1981 and his Ph.D. in mathematics from Lomonosov Moscow State University in 1988. He completed his Habilitation at Humboldt University in 1996. His academic career includes positions at the All-Union Institute of Railway Transport in Moscow, the Institute for Information Transmission Problems in Moscow, and the Institute for Applied Analysis and Statistics in Berlin before joining the Weierstrass Institute and Humboldt University where he has been a professor since 2002. Spokoiny's research focuses on adaptive nonparametric smoothing and hypothesis testing, high dimensional data analysis, statistical methods in finance, image analysis with applications to medicine, classification, and nonlinear time series. His work often addresses the challenges of nonstationarity in time series data and develops innovative methods for volatility estimation and risk management. He has made significant contributions to the development of adaptive weights smoothing procedures, which have applications in image processing, community detection, and manifold learning. His recent work has expanded into high-dimensional statistics, Bayesian inference, and optimization methods for machine learning, with publications demonstrating novel approaches to Gaussian approximation, Laplace methods, and statistical inference in non-Euclidean spaces. Spokoiny has supervised numerous PhD students including Oliver Reiss, Danilo Mercurio, Ying Chen, Elmar Diederichs, and Mstislav Elagin, whose research has focused on mathematical finance, time series analysis, and statistical methods. He serves as an Associate Editor for The Annals of Statistics (since 2004) and Statistics and Decisions (since 2002), and has previously served on the editorial board of the Journal of Statistical Planning and Inference. His professional activities include reviewing for major statistical journals including Annals of Statistics, Bernoulli, Econometrica, and Journal of American Statistical Association, as well as reviewing grant proposals for the National Science Foundation (USA), German Research Foundation, and Netherlands Organisation for Scientific Research. Spokoiny is a member of several professional societies including the International Statistical Institute, American Statistical Association, Institute of Mathematical Statistics, and Bernoulli Society. He is fluent in Russian (mother tongue), English, and German, and has good knowledge of French. His research group at WIAS focuses on developing novel statistical methodologies with applications across various scientific domains, particularly emphasizing adaptivity and robustness in complex data environments. The group's work has significant implications for financial risk management, medical imaging, and machine learning applications, with recent publications addressing fundamental questions in high-dimensional statistics and nonparametric inference.

• Sports Science
• Football (Soccer) Analytics
• Cognitive Psychology in Sports
• +۶ مورد دیگر

Prof. Daniel Memmert is a Professor at the German Sport University Cologne, leading research in Sport Informatics and Sports Games within the Institute of Exercise Training and Sport Informatics. His work focuses on cognitive aspects of sports performance, decision-making, and data-driven analysis in football (soccer) and other sports. He has published extensively on topics like penalty kick strategies, home advantage dynamics, artificial intelligence applications in coaching, and route-setting in climbing. Memmert's research bridges sports science, computer science, and psychology, with over 550 publications and 41 projects to his name. He frequently engages with media, explaining complex sports phenomena to the public. Key Research Areas: Football analytics, cognitive psychology in sports, sports technology, decision-making under pressure Media Contributions: Over 50 media features discussing topics such as AI in coaching, referee bias, and athlete creativity Projects: Includes initiatives on sports data visualization, performance metrics, and prevention of sports betting addiction His work emphasizes translating academic findings into practical tools for athletes, coaches, and sports organizations, combining rigorous data analysis with real-world applications.

Anders Rantzerمشاهده پروفایل

استاد

• Control Systems Theory
• Scalable Control
• Adaptive Control
• +۷ مورد دیگر

Anders Rantzer is a Professor of Automatic Control at the Department of Control Engineering, Faculty of Engineering, Lund University, Sweden. He has held visiting positions at Caltech (2004–2005) and the University of Minnesota (2015–2016) as the Taylor Family Distinguished Visiting Professor. His academic journey began with a PhD from KTH Stockholm in 1991, followed by a postdoc at the Institute for Mathematics and its Applications (IMA), University of Minnesota. His research interests center on modeling, analysis, and synthesis of control systems , with a strong focus on scalability, adaptation, and applications in energy networks . He is particularly known for foundational work in positive systems and integral quadratic constraints (IQCs) . These theoretical frameworks are critical in analyzing stability and robustness of large-scale interconnected systems. His work bridges mathematical rigor with practical engineering applications, especially in sustainable energy and networked systems. The recent publications and lecture materials reflect a consistent trajectory in scalable and robust control, optimization, and distributed systems. Themes such as large-scale convex optimization , nonlinear and stochastic control , and network dynamics dominate his scholarly output, indicating a sustained commitment to advancing control theory for complex, real-world systems. Scientific honors include: Fellow of IEEE Member of the Royal Swedish Academy of Engineering Sciences (IVA) Chairman of the Swedish Scientific Council for Natural and Engineering Sciences Chairman of the Royal Physiographic Society of Lund Rantzer has supervised numerous students and contributed extensively to academic leadership and education. He has been involved in major national and international research initiatives such as WASP (Wallenberg AI, Autonomous Systems and Software Program) and ELLIIT. His work includes developing educational tools and courses in control, optimization, and machine learning. He leads and contributes to research projects on autonomous systems, cloud control, and smart energy networks. He is affiliated with the Control Lab at LTH and participates in collaborative efforts such as the Nordic University Hub on Industrial Internet of Things (HI2OT). His work integrates theoretical advances with practical implementations in robotics, biomedical systems, and industrial automation.

• Machine Learning
• Dynamical Systems
• Optimization
• +۳ مورد دیگر

Michael Muehlebach leads the independent Learning and Dynamical Systems research group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. His interdisciplinary work bridges machine learning, dynamical systems theory, and control engineering to develop algorithms for cyber-physical systems with theoretical guarantees and practical implementations. Dr. Muehlebach received his B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich in 2010 and 2013, specializing in robotics and control systems. He completed his Ph.D. at ETH's Institute for Dynamic Systems and Control under Prof. R. D'Andrea in 2018, followed by postdoctoral research with Prof. Michael I. Jordan at UC Berkeley. His research focuses on constrained optimization, reinforcement learning, and control theory with applications in robotics. He pioneered approaches that express constraints in terms of velocities rather than positions, enabling more efficient optimization algorithms. His work spans theoretical foundations to physical implementations, including the One-Wheel Cubli balancing robot and electromagnetic navigation systems. Recent publications reveal a strong trend toward physics-informed machine learning, particularly for robotics applications requiring real-time performance and safety guarantees. Dr. Muehlebach has received numerous prestigious awards: Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellowship (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) He actively mentors doctoral researchers including Hao Ma, Melis Ilayda Bal, and Onno Eberhard, with research supported by multiple grants. His group maintains strong collaborations with Bernhard Schölkopf's Empirical Inference group at the Max Planck Institute. The Learning and Dynamical Systems group develops innovative hardware and software platforms, including Floaty (a wind-harnessing flying robot), advanced electromagnetic navigation systems, and data-efficient learning methods for robotic table tennis. Their approach combines rigorous theoretical analysis with practical validation on physical systems, emphasizing the integration of known physical structure into machine learning algorithms to improve sample efficiency and ensure generalization.

Leo Schwinnمشاهده پروفایل

مدرس

• Robust Machine Learning
• Large Language Models Security
• Data-Efficient Learning
• +۵ مورد دیگر

Leo Schwinn is a Lecturer at the Technical University of Munich (TUM) within the Department of Computer Science (I26), working in the Data Analytics and Machine Learning group supervised by Prof. Stephan Günnemann at the TUM School of Computation, Information and Technology. His research focuses on robust machine learning with particular emphasis on data-efficient learning and robustness vulnerabilities of Large Language Models (LLMs). Dr. Schwinn's research interests span multiple critical areas in contemporary machine learning including: Robustness against adversarial attacks in LLMs Embedding space vulnerabilities and defenses Model unlearning and privacy preservation Efficient training methodologies for large models Time-series forecasting with probabilistic frameworks Graph-based machine learning approaches His work bridges theoretical understanding with practical security implications of modern AI systems. Analysis of his recent publications (2023-2025) reveals a strong focus on LLM security, with multiple papers accepted at premier conferences including ICML, CVPR, ICLR, and NeurIPS. His research demonstrates consistent innovation in identifying novel attack vectors while developing practical defense mechanisms, particularly through embedding space manipulation techniques. The work shows increasing sophistication in handling both theoretical aspects of model robustness and practical deployment concerns. His notable scientific achievements include: Receiving the ATE dissertation price for his PhD work at FAU Securing an oral presentation at ICLR 2025 Organizing the ICLR BlogPost Track Becoming a member of ELLIS (European Laboratory for Learning and Intelligent Systems) Dr. Schwinn has served as review process chair for the 2024 Conference on Lifelong Learning Agents (CoLLAs) and actively collaborates with researchers at Mila Quebec AI Institute. His research group at TUM focuses on addressing fundamental challenges in machine learning robustness, particularly as they apply to real-world deployment scenarios where security and reliability are paramount. He maintains active GitHub repositories related to LLM security research, including circuit-breakers-eval and LLM_Embedding_Attack, demonstrating his commitment to open science and reproducible research in the field of AI security.

• Machine Learning
• Dynamical Systems
• Control Theory
• +۴ مورد دیگر

Michael Mühlebach is a Research Group Leader at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, leading the independent Learning and Dynamical Systems group. His academic journey began at ETH Zurich where he earned his B.Sc. (2010) and M.Sc. (2013) in mechanical engineering, specializing in robotics, systems, and control. He completed his Ph.D. at ETH Zurich in 2018 under Prof. R. D'Andrea, followed by postdoctoral research at UC Berkeley with Prof. Michael I. Jordan. Dr. Mühlebach's research spans machine learning, dynamical systems, control theory, and optimization . His work bridges theoretical foundations with practical applications in robotics, developing methods that incorporate physical constraints and system dynamics into learning frameworks. His group focuses on online learning, physics-informed machine learning, and large-scale optimization for cyber-physical systems, with applications in electromagnetic navigation, robotic table tennis, and energy-efficient flight systems like the shape-changing robot Floaty . His publication record shows a strong focus on constrained optimization, with recent work exploring decision-dependent stochastic optimization, nonlinear feedback, and the theoretical foundations of reinforcement learning. His research integrates perspectives from control theory, dynamical systems, and optimization to develop algorithms with strong theoretical guarantees and practical performance. Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellow (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) Dr. Mühlebach actively mentors doctoral researchers and is seeking talented students for PhD and Master's projects. His research group has received funding from multiple prestigious fellowships and maintains collaborations across institutions including ETH Zurich, UC Berkeley, and various Max Planck research units. The group's work spans theoretical developments to practical implementations on robotic systems, demonstrating strong connections between mathematical theory and physical realization.

• Database Systems
• Data Management
• GPU Acceleration
• +۶ مورد دیگر

Anastasia Ailamaki is a Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), renowned for her work in database systems and data management . Her research focuses on optimizing query processing for modern hardware, particularly GPUs and heterogeneous systems, and advancing cloud data analytics with serverless architectures like PixelDB . She has co-authored influential frameworks for adaptive query optimization , hardware-conscious database engines , and model-relational data management . Key research areas: GPU acceleration , HTAP , query approximation , spatial data processing , and cloud-native databases . Recent work emphasizes cross-task optimizations in distributed environments, efficient sampling , and context-aware joins integrating vector embeddings. In 2023, she contributed to adaptive recursive query optimization and speculative K-means clustering, while 2024 publications addressed proportional caching (HPCache) and model-relational systems . Her collaborations span institutions such as MIT, Microsoft, and ETH Zurich, with publications in top venues like SIGMOD , VLDB , and ICDE .

• Self-organized criticality
• Neuronal network dynamics
• Excitation/Inhibition balance
• +۵ مورد دیگر

Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .

• Urban Ecology
• Landscape Ecology
• Urban Ecosystem Services
• +۷ مورد دیگر

Prof. Dagmar Haase is a Full Professor in Landscape and Urban Ecology at Humboldt Universität zu Berlin, serving as Deputy Director of the Geographical Institute. She holds affiliations with the Helmholtz Centre for Environmental Research (UFZ) and has earned honorary professorships in Sweden and Romania. Her academic journey includes a PhD from the University of Leipzig (1999) and Habilitation from Martin-Luther-University Halle-Wittenberg (2009). Her research focuses on urban ecosystem services, social-ecological systems, and nature-based solutions to urban challenges. Key projects include EU-funded initiatives on green infrastructure (e.g., Horizon Europe’s NaturaConnect) and climate resilience. Awards include the AXA Research Fund Award (2014) and Honorary Wallenberg Professorship (2016). Haase’s work integrates remote sensing, citizen science, and participatory modeling to address urban sustainability. Over 250+ publications and an h-index of 77 reflect her prolific contributions. She advises numerous PhD candidates exploring urban dynamics, biodiversity, and governance. Current grants span climate adaptation, urban densification, and ecological networks.

Stefan Lutherمشاهده پروفایل

استاد

• Biomedical Engineering
• Cardiac Physiology
• Nonlinear Dynamics
• +۳ مورد دیگر

Prof. Dr. Stefan Luther is a Max Planck Research Group leader (W2, tenured since 2013) at the Max Planck Institute for Dynamics and Self-Organization, Göttingen, and an Honorarprofessor at the Faculty of Physics, University of Göttingen. He holds adjunct roles as Adjunct Associate Professor at Cornell University (2009–2012) and Northeastern University (2016–2018), and serves as DZHK-Professor at the Institute of Pharmacology and Toxicology, University Medical Center Göttingen. His research focuses on nonlinear spatiotemporal dynamics in excitable biological media, particularly cardiac arrhythmias. He pioneered 4D imaging of heart function and developed algorithms for optogenetic and electrical control of arrhythmias. Translational efforts span basic research to preclinical and clinical studies. Education includes a Diplom in Physics (1997) and PhD (2000) from Georg-August-University, Göttingen. Postdoctoral training followed at the University of Twente (2001–2004) and Cornell University’s LASSP (2004–2006). His lab, the Biomedical Physics group, explores electromechanical coupling in cardiac systems and develops novel therapeutic approaches. Collaborations include work on computational modeling, uncertainty quantification in dynamical systems, and fluid dynamics of multiphase flows.

Agnes Desolneuxمشاهده پروفایل

استاد

• Image Analysis
• Statistical Methods
• Computer Vision
• +۷ مورد دیگر

Agnes Desolneux is a CNRS Research Director at the Borelli Centre (formerly CMLA) and a Professor attached to the Mathematics Department at ENS Paris-Saclay. Education: PhD in Applied Mathematics (2000) from ENS Cachan Habilitation in Applied Mathematics (2010) from Université Paris Descartes Her research focuses on image analysis via statistical methods , particularly a contrario approaches, image restoration, texture synthesis, Determinantal Point Processes (DPP), optimal transport, Gaussian mixtures, geometry of random field excursions, shot-noise models, and mathematical modeling of visual perception through Gestalt theory. The articles extracted reflect her expertise in applied mathematics and computer vision , with recent works (2025-2020) on optimal transport algorithms, DPP applications, multiscale texture analysis, and stochastic modeling in medical imaging. Keywords span machine learning, probability theory, medical imaging, and computer vision . She has no listed scientific awards but has authored influential works including the book From Gestalt Theory to Image Analysis: A Probabilistic Approach (Springer, 2008) and Pattern Theory: the stochastic analysis of real-world signals (AK Peters, 2010).