جستجوی استاد راهنمای دانشگاهی

• Computational Chemistry
• Molecular Simulations
• Theoretical Chemistry
• +۴ مورد دیگر

Prof. Dr. Matthias Stein is an Associate Professor and Research Group Leader (W2) at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. He holds an honorary professorship in Computational Chemistry and leads the Molecular Simulations and Design group. His research focuses on computational chemistry, molecular simulations, and catalytic processes. Affiliation: Max Planck Institute for Dynamics of Complex Technical Systems (since 2010) Honorary Role: Honorary Professor in Computational Chemistry (since 2010) Education PhD Equivalent: Max-Planck-Research Group Quantum Chemistry (1995) MSc in Theoretical Chemistry, University of Manchester, UK (1993–1994) Diploma in Chemistry, TU Berlin (1995) Undergraduate studies in Chemistry, TU Berlin (1990–1995) Research Interests Stein’s work integrates computational methods with experimental data to study molecular systems, including catalytic mechanisms, enzyme behavior, and material design. Key areas include quantum chemistry, enzyme modeling, and sustainable catalysis. Awards & Honors 2018: Fellow of the Royal Society of Chemistry 2010: EML/HITS Award for best scientific presentation 2008: Wenner-Gren-Foundation Young Investigator Grant 1992: Klaus-Koch Prize from TU Berlin Grants & Projects Stein has led projects on catalytic mechanisms, enzyme design, and materials science. He has secured funding from bodies like the Wenner-Gren Foundation and the Marie-Curie Fellowship. Labs & Teams He directs the Molecular Simulations and Design group, collaborating with institutions like the Heidelberg Institute for Theoretical Studies (HITS) and KTH Stockholm.

• Statistical Inverse Problems
• Regularization Theory
• Uncertainty Quantification
• +۷ مورد دیگر

Prof. Dr. Werner Frank holds the Chair of Mathematics IX (Inverse Problems) at the University of Würzburg. He is a leading researcher in statistical inverse problems, focusing on regularization theory, uncertainty quantification, and applications in biophysics, particularly fluorescence microscopy. His work bridges statistics and inverse problem methodologies with non-Gaussian noise analysis. Education: PhD in Mathematics from Georg-August-Universität Göttingen (2012) Postdoctoral Research (2012–2014) at the Institute for Numerical and Applied Mathematics Group Leader, Statistical Inverse Problems in Biophysics, at the Max Planck Institute for Biophysical Chemistry (2014–2020) Research Interests: Statistical inverse problem theory, multiscale scanning, minimax testing, super-resolution microscopy, and imaging techniques with statistical guarantees. His recent work emphasizes applications in biophysics and nanoscale imaging. Publications: Over 30 peer-reviewed articles in top journals like Inverse Problems , Journal of the Royal Statistical Society , and SIAM Journal on Scientific Computing . Key topics include Poisson data regularization, ill-posed problem analysis, and variational methods for image denoising. Affiliations: Active in professional societies such as the Society for Inverse Problems (GIP), IFIP Working Group 7.4, and the International Photonic Imaging Association (IPIA). Regular speaker at international conferences like Inverse Problems: Modeling and Simulation (IPMS) and Applied Inverse Problems (AIP). Future Work: Expanding research on adaptive regularization methods, spatial statistical resolution in microscopy, and interdisciplinary collaborations in imaging science.

• Computer Vision
• Machine Learning
• Signal Processing
• +۶ مورد دیگر

Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.

Chinmoy Bhattacharjeeمشاهده پروفایل

استاد

• Stochastic Geometry
• Distributional Approximations
• Stein's Method
• +۴ مورد دیگر

Prof. Chinmoy Bhattacharjee is a Professor of Mathematical Stochastics at the University of Hamburg's Department of Mathematics, part of the Faculty of Mathematics, Computer Science and Natural Sciences. His research focuses on stochastic geometry, distributional approximations via Stein's method, stochastic analysis, random graphs, continuum percolation, and nonparametric inference. He holds a PhD from the University of Southern California and previously held postdoctoral positions at the University of Luxembourg and University of Bern. His work is supported by the German Research Foundation (DFG) through Project No. 531540467. Education: PhD in Applied Mathematics, University of Southern California (2013–2018) M.Stat. and B.Stat. (Hons.), Indian Statistical Institute, Kolkata (2008–2013) Teaching: Recent courses include Stochastic Processes, Probability and Statistics, and seminars on percolation theory and probabilistic methods. Research Highlights: Prof. Bhattacharjee's work bridges stochastic geometry and Stein's method, with applications in spatial random structures, non-Gaussian limit theory, and noise sensitivity in percolation models. Key contributions include Gaussian and Dickman approximation techniques, spectral analysis of Poisson functionals, and statistical applications in random forests. Upcoming Events: Co-organizing session on Spatial Stochastics at German Probability and Statistics Days (2025) Invited talks at Aarhus University and workshops in Germany/Croatia (2025)

Ana Djurdjevacمشاهده پروفایل

استادیار

• Numerical Analysis
• Stochastic Processes
• Partial Differential Equations
• +۳ مورد دیگر

Ana Djurdjevac is an Assistant Professor in the Department of Numerical Analysis and Stochastics at the Freie Universität Berlin , within the Department of Mathematics and Computer Science. Her research focuses on numerical analysis, stochastic processes, and partial differential equations, with particular emphasis on uncertainty quantification and mathematical modeling in evolving domains. She teaches advanced courses such as Numerical Methods for Stochastic Differential Equations and Stochastik I , reflecting her expertise in computational methods and probabilistic frameworks. Her work integrates theoretical analysis with practical numerical techniques, addressing challenges in domains such as fluid dynamics, quantum systems, and biological surface fluctuations. Recent contributions include studies on hybrid algorithms for particle systems, rough homogenization in stochastic dynamics, and synchronization mechanisms in conservation laws. Djurdjevac actively participates in academic events, including the 2025 SIAM Conference on Computational Science and Engineering, and collaborates on projects involving quasi-Monte Carlo methods for Bayesian inversion and domain decomposition techniques. Professional activities highlight her role in shaping emerging fields like stochastic PDEs on evolving domains and feedback loops in agent-based models. While no specific awards are listed, her prolific publication record and teaching roles underscore her contributions to computational science and applied mathematics.

• Stochastics
• Probability Theory
• Stochastic Processes
• +۲ مورد دیگر

Dr. Franz Nestmann serves as a Researcher at the Institute of Stochastics within the Department of Mathematics at Karlsruhe Institute of Technology (KIT). His academic profile includes teaching responsibilities across multiple semesters, with current appointments for Sommersemester 2025 including Mathematics 2 for Economics, associated exercises, and a specialized seminar in mathematical economics. His research concentrates on fundamental stochastic processes including Poisson processes and Markov chains, with significant applications in mathematical economics and fractal geometry. This interdisciplinary work bridges theoretical probability frameworks with practical economic modeling, particularly evident through his advanced courses in stochastic systems and geometric analysis of complex structures.

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +۳ مورد دیگر

Lisa Hartung is a Full Professor (W3) at Johannes Gutenberg University Mainz's Department of Mathematics since 2025, leading research in stochastic processes. She previously held Associate (2019-2025) and Assistant Professor (2019) positions at the same institution and served as a Courant Instructor at NYU's Courant Institute (2016-2018). Her educational background includes a PhD from Bonn University (2016) under Prof. Anton Bovier in Probability Theory. Key research interests span branching Brownian motion , Gaussian free fields , random energy models , and stochastic processes on evolving networks , with applications in statistical mechanics and mathematical physics. Her work frequently addresses extremal processes, phase transitions, and complex temperature phenomena. Analysis of her recent publications reveals dominant trends in log-correlated random fields, variable-speed branching processes, and hard-wall boundary conditions. Articles consistently explore asymptotic behavior, maximum processes, and structural properties of stochastic systems, with increasing interdisciplinary connections to computer science through data stream modeling. Scientific recognition includes: Förderpreis der Fachgruppe Stochastik (2018) Hartung secures substantial research funding as PI for multiple high-impact projects: the ANR-DFG grant Random Energy Models (2020), DFG's SPP 2265 Random Geometric Systems (2020), TRR146 Multiscale Simulation Methods (2020), and Carl-Zeiss project TOPML (2022). She also serves on the executive board of DMV Fachgruppe Stochastik (since 2023) and coordinates Jugend trainiert Mathematik for grades 8/9. Her research group participates in the DFG-funded network Stochastic Processes on Evolving Networks and collaborates internationally through workshops on interacting particle systems and stochastic population models.

• Plasma Physics
• Low Temperature Plasmas
• Plasma Modeling and Simulation
• +۵ مورد دیگر

Ralf Peter Brinkmann is a Professor and Chair holder of Theoretical Electrical Engineering at Ruhr University Bochum, Faculty of Electrical Engineering and Information Technology, since 2000. He is actively involved in research as a member of the Center for Plasma Science and Technology and a board member of the Collaborative Research Centre 591 ("Universal Behaviour of Non-Equilibrium Plasmas: Heating, Transport, and Structure Formation") since 2003. His office is located at Room ID 1/127, Universitätsstraße 150, D-44801 Bochum, with contact via brinkmann@tet.rub.de. His educational background includes: Diploma in Physics (1984) from Ruhr-University Bochum Diploma in Electrical Engineering (1985) from Ruhr-University Bochum PhD (Dr. rer. nat.) in theoretical astrophysics (1986) from Ruhr-University Bochum Brinkmann's research centers on low-temperature plasma physics, specifically modeling and simulation of technical plasmas, theory of plasma edge layers, and model-based plasma diagnostics. He investigates electron dynamics, power absorption mechanisms, and sheath behavior in capacitive coupled radio frequency discharges, magnetron plasmas, and atmospheric pressure plasma jets, with focus on kinetic effects and electromagnetic phenomena. Analysis of his recent publications (2023-2025) reveals strong emphasis on electromagnetic particle-in-cell simulations, time-resolved plasma transition studies (E-H, capacitive-inductive), and thermodynamic quasi-equilibria in high-power magnetrons. Key trends include rotational spoke dynamics in ExB plasmas, nonlocal electron transport, and diagnostic tools like the multipole resonance probe. Scientific awards: No scientific awards listed for Brinkmann in the provided text Brinkmann has advised PhD and Master's students (specific names unlisted) and secured major research funding through Collaborative Research Centre 591 under the German Research Foundation. His work includes industry collaborations with Siemens AG/Infineon Technologies and academic partnerships with Stanford University, University of Chicago, and Old Dominion University. He leads research within the Center for Plasma Science and Technology and Collaborative Research Centre 591, developing advanced plasma models and diagnostic probes. His team focuses on computational frameworks for plasma edge layers, magnetron discharge kinetics, and industrial plasma process optimization, particularly for semiconductor and thin-film applications.

Daniel Kuhnerمشاهده پروفایل

پژوهشگر

• Robotics
• Autonomous Intelligent Systems
• Human-Robot Interaction
• +۷ مورد دیگر

Daniel Kuhner is a researcher in the Department of Computer Science at the University of Freiburg, affiliated with the Autonomous Intelligent Systems group under Prof. Dr. Wolfram Burgard. He holds a B.Sc. and M.Sc. in Computer Science from the same institution and has been a PhD student since 2013. Education: Bachelor of Science in Computer Science, University of Freiburg (2007–2010) Master of Science in Computer Science, University of Freiburg (2010–2013) His research centers on robotics and intelligent systems, with key interests in object manipulation, motion planning, human-robot interaction, and brain-computer interfaces. He is involved in the NeuroBots project under the BrainLinks-BrainTools initiative, focusing on assistive technologies for users with communication impairments. His teaching includes organizing proseminars and system design projects on brain signal decoding from 2014 to 2018. Daniel Kuhner's recent publications highlight trends in robot task planning using natural referring expressions, tactile-based object segmentation, and BCI-driven assistive robotics. His work bridges autonomous exploration, real-time interaction, and adaptive systems, emphasizing practical applications in assistive and service robotics. Scientific Awards: No scientific awards mentioned in the provided text. Daniel Kuhner has supervised multiple bachelor's theses, including those of Claas Bollen and Milan Benninger. He has been involved in organizing academic teaching activities and research projects. While no grants are explicitly listed, his participation in BrainLinks-BrainTools indicates involvement in funded interdisciplinary research. He is also associated with the development of autonomous exploration frameworks and assistive robotic systems. Labs and Teams: Autonomous Intelligent Systems Group, University of Freiburg NeuroBots Project, BrainLinks-BrainTools

Tania Kosenkovaمشاهده پروفایل

پژوهشگر

• Probability Theory
• Stochastic Processes
• Lévy Processes
• +۳ مورد دیگر

Tania Kosenkova is a researcher at the University of Potsdam , affiliated with the Department of Mathematics. Her work centers on advanced topics in probability theory and stochastic processes, particularly focusing on Lévy-type processes, statistical inference, and random dynamical systems under Lévy noise. Her research includes functional limit theorems , characterization of Lévy processes , and transportation distances between Lévy measures . She actively teaches courses such as Statistics for Teacher Education , Stochastic Models , and Limit Theorems for School Teaching , reflecting her dual focus on theoretical and pedagogical applications. Her publications reveal a consistent engagement with Lévy-driven SDEs , jump process analysis , and stochastic approximation schemes . While no formal awards are listed, her work has been featured in journals like Journal of Theoretical Probability and Stochastic Processes and their Applications , often in collaboration with researchers such as A. Kulik and J. Gairing.

Tomer Markovichمشاهده پروفایل

پژوهشگر

• Soft Active Matter
• Condensed Matter Physics
• Thermodynamics
• +۵ مورد دیگر

Dr. Tomer Markovich is a Senior Academic Staff member at the School of Mechanical Engineering , part of the Fleischman Faculty of Engineering at Tel Aviv University . His research focuses on the physics of soft active matter, particularly the development of thermodynamic frameworks for non-equilibrium systems and the study of chiral active materials with unique properties such as odd viscosity. He also explores biological contexts like active gels and active polar liquid crystals. Education B.Sc. in Physics and Computer Science (Magna Cum Laude), Tel Aviv University, 2010 Ph.D. in Condensed Matter Physics, Tel Aviv University, 2016 Dr. Markovich's research spans the study of active field theories , mechanical criticality in disordered systems , and non-linear dynamics in biopolymer networks . His work also addresses chiral active fluids , entropy production , and nonreciprocal interactions , with applications in synthetic and biological active materials. Recent Publications Trends: His recent works focus on non-equilibrium thermodynamics, chiral active matter, and the mechanical properties of disordered and biopolymer networks. Topics include odd elasticity , minimal dissipation control , temporal correlations in chromosome dynamics , and nonaffine deformation mechanisms . Scientific Awards CTBP Postdoctoral Fellowship, Rice University (2018–2022) Blavatnik Postdoctoral Fellowships Program for Israeli Scientists (2016–2018) Dothan Scholarship for Academic Achievements (2016) Getti Scholarship for Excellence in Research (2014) Haya Rozet Scholarship for Academic Excellence (2013) Academic Appointments include postdoctoral research at the University of Cambridge (2016–2018) and Rice University (2018–2022). His current role involves theoretical research in soft active matter, with a focus on thermodynamic frameworks and chiral properties, contributing to both fundamental and applied aspects of mechanical engineering and condensed matter physics.

• Bioinformatics
• Genome Informatics
• Computational Biology
• +۳ مورد دیگر

Dr. Kassian Kobert is a researcher at the University of Bielefeld, affiliated with the Faculty of Engineering, the Institute for Bioinformatics Infrastructure (BIBI), and the Center for Biotechnology (CeBiTec). His primary research base is within the Genome Informatics Group, where he focuses on computational approaches to genomic analysis and biological data processing. He maintains an active research presence with recent publications extending to 2022. Dr. Kobert's research interests center on bioinformatics and computational biology, with particular expertise in algorithm development for genomic data analysis. His work bridges computer science and biological applications, focusing on creating efficient computational methods for handling large-scale genomic datasets. His research spans phylogenetic inference, sequence alignment, viral evolution, and the development of specialized software tools for next-generation sequencing data processing. The interdisciplinary nature of his work connects computer science theory with practical biological applications, particularly in evolutionary biology and genomics. Analysis of Dr. Kobert's publication record reveals a consistent focus on developing computational methods for biological data analysis. His work demonstrates a trajectory from fundamental algorithm development to practical software implementation, with notable contributions including the PEAR read merger and ExaBayes phylogenetic analysis tool. His research shows strong emphasis on computational efficiency, particularly for handling the massive datasets generated by modern genomic technologies. The publications indicate expertise in both theoretical computer science aspects (algorithm design, computational complexity) and practical biological applications (viral evolution, phylogenetic analysis). Dr. Kobert appears to have significant involvement in software development for bioinformatics applications, with several publications describing tools that have become standard in genomic analysis workflows. His work on parallel computing approaches suggests engagement with high-performance computing environments necessary for modern genomic research.

Benedikt Wirthمشاهده پروفایل

استاد

• Optimisation and calculus of variations
• image processing
• scientific computing
• +۹ مورد دیگر

Prof. Dr. Benedikt Wirth is a Professor of Mathematics at the University of Münster, Germany, affiliated with the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science. He is an active researcher and educator specializing in optimization and calculus of variations, with significant contributions to mathematical imaging and shape analysis. His research interests include image processing, scientific computing, numerical analysis, optimization, shape spaces, geodesics in shape space, variational methods, elastic deformation, and optimal transport. Wirth has developed innovative mathematical frameworks for shape analysis, particularly focusing on Riemannian metrics for shape spaces and variational approaches to shape comparison and optimization. His recent publications (2023-2025) demonstrate continued leadership in mathematical optimization, with particular focus on PET reconstruction, dimension reduction techniques, manifold embeddings, and branched transport theory. His work bridges theoretical mathematics with practical applications in medical imaging and computer vision, showing particular strength in connecting geometric analysis with computational methods. CRC 1450 - A05: Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling CRC 1450 - A06: Improving intravital microscopy of inflammatory cell response by active motion compensation EXC 2044 - C1: Evolution and asymptotics EXC 2044 - C2: Multi-scale phenomena and macroscopic structures EXC 2044 - C3: Interacting particle systems and phase transitions EXC 2044 - C4: Geometry-based modelling, approximation, and reduction Prof. Wirth actively supervises numerous bachelor's and master's students, with over 40 theses completed under his guidance since 2015. His teaching portfolio includes courses on inverse problems, numerical methods for partial differential equations, shape spaces, optimization, and optimal transport. He has consistently maintained an active research program while contributing significantly to the education of the next generation of mathematicians.