جستجوی استاد راهنمای دانشگاهی

Gerhard Huisken is a Professor at the University of Tübingen and Director of the Mathematisches Forschungsinstitut Oberwolfach . His work spans Differential Geometry , Geometric Flows , and Mathematical Relativity . Education : Diploma (1982), PhD (1983), and Habilitation (1986) in Mathematics from Heidelberg University. His research focuses on geometric evolution equations, particularly mean curvature flow and inverse mean curvature flow , with applications to mathematical relativity and geometric inequalities . He has contributed to the understanding of singularities in curvature flows and developed surgical techniques for their analysis. His work on the Riemannian Penrose inequality and center of mass in isolated systems bridges geometry and physics. Selected publications highlight trends in geometric flows (mean curvature flow, Ricci flow), mathematical relativity (Penrose inequality, center of mass), and singularities in geometric PDEs. His collaborations with leading mathematicians like Simon Brendle and Tom Ilmanen reflect interdisciplinary impact. Scientific Awards and Honors : Fellow of the American Mathematical Society (2013) Clay Foundation Senior Fellowship (2013, 2007) Leibniz Preis from German Research Foundation (2003) Medal of the Australian Mathematical Society (1991) Member of the German Academy of Sciences Leopoldina (2004) He has held leadership roles including Dean of the Faculty of Mathematics at Tübingen University and directed major institutes like the Max Planck Institute for Gravitational Physics (2002-2013).

• Analysis
• Differential Geometry
• Mathematical Relativity
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Dr. Yunjie Yang is an Associate Professor at the University of Edinburgh's School of Engineering, with affiliations at the Edinburgh Futures Institute (EFI), the Edinburgh Generative AI Laboratory (GAIL), and the Edinburgh Centre for Robotics. He previously held the Chancellor's Fellow in Data Driven Innovation (2018-2023) and Bayes Innovation Fellow (2023-2024) positions. His research focuses on AI-powered sensing and imaging, machine learning, and soft sensors & electronics for robotics. Yang received his PhD in Engineering Electronics from the University of Edinburgh, MSc in Control Science & Engineering from Tsinghua University, and BEng in Measurement & Control Engineering from Anhui University. After his PhD, he worked as a Postdoctoral Research Associate in Chemical Species Tomography before securing his lectureship. His research interests center on developing intelligent sensing systems that replicate human perception capabilities for robotics and intelligent systems. He pioneers flexible sensing and imaging technologies across various scales through innovative multi-modal sensors, soft electronics, and their modeling using machine learning approaches. His work aims to enable autonomous physical artificial intelligence by bridging the gap between robotic systems and human-like perception. Analysis of his recent publications reveals a strong focus on soft robotics perception, particularly through electrical impedance tomography (EIT) and transformer-based architectures. His research spans medical imaging applications, digital twin modeling for industrial processes, and machine learning approaches for sensor data interpretation. The trend shows increasing integration of physics-informed deep learning with traditional tomographic techniques to achieve higher accuracy and efficiency. European Research Council (ERC) Starting Grant (2024) IEEE J. Barry Oakes Advancement Award (2024) IEEE I&M Society Graduate Fellowship Award (2015) Multiple Best Paper Awards Senior Member of IEEE Fellow of the International Society for Industrial Process Tomography Fellow of the Higher Education Academy ESI highly cited papers Dr. Yang serves as Associate Editor for IEEE Transactions on Instrumentation and Measurement and holds editorial positions with Scientific Reports and IEEE Sensors Journal. His research has been licensed to overseas research institutes and industry partners and received wide media coverage including BBC, EFE, USA Today, and STV. He has secured significant grant funding including the prestigious ERC Starting Grant. He leads the Edinburgh SMART Lab (Sensing/imaging + Machine Learning + Robotics), which aims to replicate human perception capabilities for robotics and advance flexible sensing technologies through innovative multi-modal sensors and machine learning approaches. The lab focuses on enabling autonomous physical artificial intelligence with applications spanning medical diagnostics, industrial monitoring, and advanced robotics systems.

• AI-powered sensing and imaging
• Machine learning
• Soft sensors & electronics for robotics
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Zhou Zhi-Hua is a Professor at Nanjing University's Department of Computer Science & Technology, serving as Standing Deputy Director of the National Key Lab for Novel Software Technology and Founding Director of LAMDA (Institute of Machine Learning and Data Mining). He holds simultaneous fellowships from ACM, AAAI, AAAS, IEEE, IAPR, IET/IEE, and CCF, reflecting his exceptional contributions to computational intelligence. His educational background includes: B.Sc. in Computer Science from Nanjing University (1996) M.Sc. in Computer Science from Nanjing University (1998) Ph.D. in Computer Science from Nanjing University (2000) Zhou's research pioneers fundamental advances in machine learning theory and applications. His seminal work on ensemble methods established new frameworks for classifier combination, while innovations in multi-label learning and anomaly detection addressed critical challenges in complex data analysis. His research bridges theoretical rigor with practical implementations across diverse domains including biometrics, data mining, and computer vision, resulting in over 150 publications and 18 patents. His textbooks "Ensemble Methods" (2012) and "Machine Learning" (2016) have become standard references in the field. Analysis of his publication trajectory reveals sustained leadership in core machine learning challenges: evolving from neural network ensembles (2002) through semi-supervised learning breakthroughs (2005) to foundational work on multi-instance learning (2012) and theoretical margin analysis (2013). His recent focus demonstrates increasing sophistication in handling complex data structures while maintaining theoretical soundness. His scientific excellence is recognized through: National Natural Science Award of China (2013) PAKDD Distinguished Contribution Award (2016) IEEE ICDM Outstanding Service Award (2016) IEEE CIS Outstanding Early Career Award (2013) Microsoft Professorship Award (2006) Simultaneous fellowships from 7 major international societies Zhou provides extraordinary service to the academic community as Executive Editor-in-Chief of Frontiers of Computer Science and Associate Editor-in-Chief of Science China Information Science. He founded the ACML conference and has chaired premier events including ICDM'16 and PAKDD'14. His leadership extends to serving as General Chair for ICDM'16, Program Chair for IJCAI'15 Machine Learning Track, and Area Chair for multiple top conferences. The available text does not specify student advising details or research grants. He directs LAMDA research group at Nanjing University, which has established itself as a global powerhouse in machine learning research, and contributes significantly to the National Key Lab for Novel Software Technology's mission of developing next-generation intelligent systems.

• Artificial Intelligence
• Machine Learning
• Data Mining
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Christian Bargetz is a Professor of Functional Analysis at the University of Innsbruck, Austria, affiliated with the Faculty of Mathematics, Computer Science, and Physics (MIP). His primary research focuses on nonlinear functional analysis, Banach space theory, and distribution theory. He teaches advanced courses such as Optimization, Distribution Theory, and Functional Analysis, demonstrating his expertise in both theoretical and applied aspects of his field. Education: Completed his PhD in 2012 at the University of Innsbruck under the supervision of Norbert Ortner. His diploma thesis (2008) explored differential behaviors with Ulrich Oberst. Bargetz has held continuous academic positions since 2008, including roles as a lecturer and researcher. Research Interests: Specializes in iterative projection methods, generic properties of nonexpansive mappings, Fréchet spaces, and vector-valued distributions. His work bridges functional analysis with geometric measure theory and optimization, with applications in metric geometry and topological tensor products. Publications: Over 30 peer-reviewed articles in prestigious journals such as Canadian Journal of Mathematics , Journal of Mathematical Analysis and Applications , and Proceedings of the American Mathematical Society . Recent work includes studies on extremal nonexpansive mappings and Lipschitz function spaces. Grants & Projects: Principal investigator in FWF-funded projects on nonexpansive mappings and Banach spaces. Collaborates internationally, including with institutions in Israel, Poland, and Serbia. Teaching: Leads advanced courses in functional analysis, optimization, and distribution theory. Supervises bachelor's theses and master's projects on topics like extension operators for Lipschitz functions. Affiliations: Active member of the Functional Analysis working group and regularly participates in international conferences such as the Banach Afternoon, Winter School in Abstract Analysis, and DMV-ÖMG Annual Conferences.

• Nonlinear Functional Analysis
• Banach Spaces
• Locally Convex Spaces
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Amie Wilkinson is a Professor of Mathematics at the University of Chicago since 2012, previously holding positions at Northwestern University. She specializes in dynamical systems, ergodic theory, and geometry. Her research explores actions of discrete groups, smooth dynamics, and geometric systems. She holds a Ph.D. from UC Berkeley (1995) and an A.B. from Harvard (1989). Wilkinson has received prestigious awards including the Levi L. Conant Prize (2020) and the Ruth Lyttle Satter Prize (2011). She led the NSF-funded 'Robust and Generic Mechanisms in Smooth Dynamics' ($600,000, 2014-2019). Her work spans geodesic flows, Lyapunov exponents, and rigidity phenomena in dynamical systems. Education: Ph.D. UC Berkeley (1995), A.B. Harvard (1989) Key Positions: Boas Assistant Professor (Northwestern, 1996–1999), Associate/Full Professor roles at Northwestern (1999–2011) Awards: AMS Fellow (2013), Invited Speaker at International Congress of Mathematicians (2010) Her research emphasizes the interplay between geometry and dynamics, with contributions on ergodicity, hyperbolic systems, and foliation structures.

• Ergodic Theory
• Smooth Dynamical Systems
• Dynamical Systems of Geometric Origin
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François Peeters is a Full Professor of Physics at the University of Antwerp, Belgium, holding the position since 2000 (with Dutch title 'gewoon hoogleraar' since 2003). He previously served as Research Director (FWO-VI) at the University of Antwerp (1996-1999), Research Leader (NFWO) (1992-1996), and Senior Research Assistant (NFWO) (1988-1992), establishing a distinguished academic career spanning over three decades. His educational background includes a Ph.D. in Physics from the University of Antwerp (1982), followed by a Habilitation (Hoger aggregaat) from the same institution (1987), and a postdoctoral fellowship at Bell Laboratories in Murray Hill, New Jersey (1982-1983). His academic journey also featured research periods at prestigious institutions including the High Magnetic Field Laboratory in Grenoble, University of California Berkeley, Oxford University, and several Brazilian and Australian universities. Peeters' research focuses on theoretical condensed matter physics , specializing in the electronic, optical, and magnetic properties of nanostructured systems. His work encompasses semiconductors , superconductors , graphene , and hybrid quantum systems , with particular emphasis on strong correlations in both classical (colloids, dusty plasma) and quantum (quantum dots) environments. His theoretical frameworks bridge fundamental quantum mechanics with practical nanotechnology applications, driving innovations in spintronics and quantum device design. Analysis of his publication record reveals a clear evolution from foundational work on polaron physics and quantum Hall systems in the 1980s-1990s toward contemporary research on graphene, topological materials, and programmable quantum nanodevices. His most cited works demonstrate consistent leadership in mesoscopic physics, with recent publications showing increased focus on spin-dependent transport phenomena and two-dimensional material systems. His scientific recognition includes: Fellowship in the American Physical Society (2005) APS Outstanding Referee award (2008) Doctor Honoris Causa from University of Szeged, Hungary (2009) Peeters has supervised 26 completed PhD theses and currently leads the Condensed Matter Theory research group comprising 3 ZAP researchers, 16 PhD students, and 8 postdocs. His grant portfolio includes coordination of an EU Marie Curie Training site on 'Electrons on helium', participation in multiple EU projects, COST actions, and ESF networks, demonstrating sustained success in securing competitive international funding. The Condensed Matter Theory group maintains extensive international collaborations, evidenced by Peeters' research visits to over 10 institutions worldwide and regular hosting of 3-4 international visitors at postdoc or professorial levels. The group's output of over 770 refereed publications with 12,000+ citations reflects its position at the forefront of theoretical condensed matter physics research.

• Condensed Matter Physics
• Nanoscience
• Theoretical Physics
• +۴ مورد دیگر

Martin Holler is a Professor at the Institute of Mathematics and Scientific Computing at the University of Graz, Austria, where he leads the research group Applied Mathematics and Machine Learning . His work bridges theoretical mathematics with practical applications in imaging and machine learning. Research Focus: His primary research areas include the mathematics of data science, variational methods in imaging, dynamic and multi-modality inverse problems, and biomedical imaging. He has made significant contributions to model-based regularization techniques, particularly with Total Generalized Variation (TGV) approaches for image and video reconstruction. Publication Trends: Over the past decade, Holler's research has evolved from traditional variational methods for image reconstruction toward increasingly sophisticated machine learning approaches. His recent work (2021-2023) focuses on integrating deep learning with variational methods, particularly for motion separation in medical imaging and learning-informed parameter identification in partial differential equations. His publications demonstrate a consistent thread of applying rigorous mathematical frameworks to solve practical problems in medical imaging and computer vision. Mathematics of data science and machine learning Generative models in machine learning Variational methods in imaging Dynamic and multi-modality inverse problems Model-based regularization Biomedical imaging Image and video decompression Technical Leadership: Holler has developed several open-source software packages implementing advanced reconstruction algorithms, particularly for multi-modal imaging problems. His GitHub repositories show active maintenance and development of these tools, which have been cited in the medical imaging community.

• Mathematics of data science
• Machine learning
• Generative models
• +۷ مورد دیگر

Mihyun Kang is a Professor in the Institute of Discrete Mathematics at Graz University of Technology (TU Graz), leading the Combinatorics Group. She holds significant academic positions and has been recognized with a Heisenberg Fellowship (German Research Foundation) and a Friedrich Wilhelm Bessel Research Award (Alexander von Humboldt Foundation). Her research focuses on combinatorics, discrete probability, and algorithms, with a specialization in random graph theory. She contributes to editorial boards, including Random Structures & Algorithms . Her research interests emphasize high-dimensional graphs, percolation processes, and structural properties of random graphs. She explores topics such as phase transitions, graph enumeration, and algorithmic applications in combinatorial problems. Her work bridges theoretical foundations with practical applications in algorithm design and probabilistic modeling. Dr. Kang’s scientific achievements include pioneering studies on hypergraphs, bootstrap percolation, and the evolution of random graph processes. Her recent publications highlight advancements in understanding graph components, connectivity thresholds, and universality phenomena in random structures. She collaborates widely, contributing to conferences and international initiatives like the SFB “Discrete random structures: enumeration and scaling limits.” Her professional activities include advising doctoral students and supervising research projects at TU Graz. She leads the Combinatorics Group, which hosts the Graz Combinatorics Seminar and actively engages in academic outreach. Her contributions extend to textbook authorship, including Diskrete Mathematik für die Informatik , and she maintains an active presence in discrete mathematics education and research.

• Combinatorics
• Discrete Probability
• Algorithms
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Barbara Kaltenbacher is a Professor at the Institute of Mathematics, University of Klagenfurt. She serves as Deputy Head of the Institute and is actively involved in academic governance through roles in curricular commissions for Information Technology and Mathematics. Her research focuses on nonlinear acoustics , inverse problems , and partial differential equations , particularly in modeling wave propagation and parameter identification. Her work spans theoretical and applied domains, including Acoustic nonlinearity parameter tomography Fractional regularization techniques Optimization of imaging and ultrasound models Well-posedness of nonlinear PDEs Her recent publications address advanced mathematical challenges in nonlinear acoustics and inverse problems, with applications in medical imaging and materials science. She contributes to academic leadership through committee memberships and maintains active research collaborations across disciplines.

• Numerical mathematics
• Technical mathematics
• Analysis
• +۶ مورد دیگر

Radu Ioan Bot is a Professor and Dean of the Faculty of Mathematics at the University of Vienna, where he also serves as Head of the Department of Mathematics. His primary affiliations include the Department of Mathematics (Oskar-Morgenstern-Platz 1, 1090 Wien) and the Research Network Data Science (Währinger Straße 29, 1090 Wien). Bot's research centers on optimization theory with emphasis on convex/nonconvex optimization, monotone operators, and dynamical systems. He develops fast algorithms for variational inequalities and monotone inclusions by bridging continuous-time dynamics with discrete optimization methods. His work frequently addresses bilevel optimization, Tikhonov regularization, and second-order dynamics, yielding accelerated convergence rates for complex problems. Analysis of his 15 most recent publications (2023-2025) reveals dominant trends in time-scaling techniques, vanishing damping dynamics, and structured splitting methods. Key contributions include unifying Nesterov acceleration with Heavy Ball dynamics, developing reflected forward-backward algorithms for constrained optimization, and establishing strong convergence guarantees for monotone operator flows. These advances demonstrate consistent innovation in accelerating optimization while maintaining theoretical rigor. No scientific awards were mentioned in the provided source material. Details regarding student advising and research grants were not specified in the available information, though his leadership roles as Dean and Department Head indicate significant administrative responsibilities alongside active research. Bot participates in the University of Vienna's Research Network Data Science, suggesting interdisciplinary engagement in data-driven methodologies with potential applications in machine learning and computational mathematics.

• Optimization
• Convex Analysis
• Nonlinear Analysis
• +۳ مورد دیگر

Martin Hairer is a Professor of Pure Mathematics at Imperial College London and EPFL (École Polytechnique Fédérale de Lausanne). He has held significant academic positions, including Regius Professor at the University of Warwick and Professor at the Courant Institute, NYU. His research focuses on stochastic analysis, probability theory, and partial differential equations. Education: B.Sc., M.Sc., and Ph.D. in Physics from the University of Geneva (1998-2001) Martin Hairer’s research interests span Stochastic Partial Differential Equations (SPDEs), Rough Path Theory, and Regularity Structures, with applications in mathematical physics and statistical mechanics. His recent publications emphasize stochastic analysis and SPDEs, particularly the development of Regularity Structures and Rough Path Theory, enabling the rigorous understanding of singular SPDEs and nonlinear dynamics. Scientific Awards: Breakthrough Prize in Mathematics (2021) Fields Medal (2014) Fermat Prize (2013) Royal Society Wolfson Research Merit Award (2009) LMS Whitehead Prize (2008) Martin Hairer actively contributes to education through lecture notes and seminars, including courses on SPDEs and stochastic analysis. He also develops mathematical software.

• Stochastic analysis
• PDE theory
• Probability theory
• +۵ مورد دیگر

Markus Haltmeier is a Professor in the Department of Mathematics at the University of Innsbruck. His research focuses on inverse problems, image reconstruction, and deep learning with applications in medical imaging, photoacoustics, and computational mathematics. He leads a group dedicated to advancing theoretical and practical solutions for challenges in non-destructive testing and medical diagnostics. His work integrates mathematical analysis with machine learning, addressing issues such as high-resolution imaging in scattering media and automated segmentation of cardiac structures. Key research areas include regularization techniques for inverse problems, self-supervised learning approaches for limited data scenarios, and computational methods for photoacoustic tomography. His contributions span both theoretical developments (e.g., inversion formulas for Radon transforms) and applied solutions (e.g., algorithms for cylinder liner wear assessment and myocardial infarct segmentation). Publications highlight advancements in neural network-based regularization, 3D medical image synthesis, and unsupervised learning frameworks for segmentation and registration. His research emphasizes bridging the gap between mathematical theory and real-world applications in healthcare and engineering.

• Image Reconstruction
• Inverse Problems
• Deep Learning
• +۴ مورد دیگر

Jennifer Culbertson is an Associate Professor at the University of Edinburgh, where she has been a faculty member since 2014 when she took up a Chancellor's Fellowship, later being promoted to Reader (equivalent to Associate Professor) in 2018. She is a founding member of the Centre for Language Evolution at Edinburgh and maintains an active research program in experimental linguistics and cognitive science. Dr. Culbertson obtained her PhD in Cognitive Science from Johns Hopkins University in 2010, with her dissertation receiving the Robert J. Glushko Prize for Outstanding Dissertations in Cognitive Science in 2011. Her primary research investigates how languages are shaped by learning and use, with particular focus on grammatical structures (like word order) and morphological categories (gender and person systems). She employs experimental and computational methods to explore how typological universals arise from properties of the human cognitive system. Her work bridges theoretical linguistics, cognitive science, and language evolution, examining the cognitive constraints that influence linguistic patterns across diverse languages and learning contexts. She frequently uses artificial language learning paradigms to isolate specific cognitive mechanisms underlying language structure. Analysis of her recent publications reveals a consistent focus on cognitive biases in linguistic structure, particularly in word order and morphological systems. Her research increasingly incorporates cross-population studies (including work with autistic learners) and developmental perspectives to understand how language patterns emerge across different populations and throughout the lifespan. She examines how simplicity principles, communicative efficiency, and statistical learning shape linguistic structure. Dr. Culbertson's scientific contributions have been recognized with several prestigious awards: Robert J. Glushko Prize for Outstanding Dissertations in Cognitive Science (2011) ERC Starting Grant Young Academy of Europe membership (2019) Her research has been supported by over €2 million in funding from the ESRC and ERC. While specific advisees aren't mentioned in the provided text, her active publication record with numerous co-authors suggests she mentors graduate students and early-career researchers. Her work has significant implications for understanding language universals, language acquisition, and the cognitive foundations of linguistic structure. As a founding member of the Centre for Language Evolution at Edinburgh, Dr. Culbertson collaborates with an interdisciplinary team investigating the biological, cognitive, and cultural foundations of human language. Her research often intersects with psychology, cognitive science, and computational linguistics, contributing to a comprehensive understanding of language as both a cognitive and social phenomenon.

• Experimental linguistics
• Syntax
• Morphology
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Klemens Fellner is a Professor of Mathematics/Computational Sciences and Group Leader of the Applied Analysis Group at the Institute of Mathematics and Scientific Computing, University of Graz. His research focuses on the analysis of partial differential equations and mathematical modeling across physics, chemistry, and biology. Research Interests: Prof. Fellner's work spans theoretical analysis of nonlinear PDEs (reaction-diffusion, kinetic, and non-local equations) using entropy/duality methods, with applications to: Biological systems (lipolysis, protein localization, stem-cell division) Physical processes (organic photovoltaics, semiconductor modeling) Collective behavior (swarming micro-organisms, aggregation dynamics) Interdisciplinary Mathematics-Arts collaborations Publication Trends: His recent articles (2018-2021) demonstrate strong focus on: Global existence and regularity for reaction-diffusion systems Convergence to equilibrium via entropy methods Drift-diffusion models in semiconductor physics Mathematical biology applications (prion dynamics, lipolysis) Novel approaches for non-local aggregation and hysteresis phenomena Research Leadership: Currently leads the Applied Analysis Group with members including postdocs and PhD students. Key projects: Doctoral School IGDK (International Graduate School) SFB Lipid Hydrolysis (Special Research Program) Mathematics and Arts collaborations Colibri research platform Supervises PhD candidate Reymart Lagunero studying generalized reaction-diffusion systems.

• Partial Differential Equations
• Reaction-Diffusion Systems
• Kinetic Equations
• +۶ مورد دیگر

Dr. Bernhard Lamel is a Professor at the Department of Mathematics , Faculty of Mathematics , University of Vienna . He specializes in Several Complex Variables , CR Geometry , and Differential Geometry , with a focus on CR maps, hypersurfaces, and geometric analysis. His work spans formal and analytic equivalence, jet determination, and convergence of normal forms. Key research areas: CR Geometry Differential Geometry Several Complex Variables Partial Differential Equations in CR Context Recent publications (2024–2018) analyze degenerate CR maps Borel mappings for compact sets regularity in CR vector bundles finite jet determination problems equivalence theory for infinite type hypersurfaces

• Several Complex Variables
• CR Geometry
• Differential Geometry
• +۳ مورد دیگر