جست‌وجوی استاد و پژوهشگر

Jürg Kramerمشاهده پروفایل

استاد

• Arakelov Geometry
• Automorphic Forms
• Modular Forms
• +۵ مورد دیگر

Prof. Jürg Kramer is a Professor of Mathematics at Humboldt University of Berlin, affiliated with the Faculty of Mathematics and Natural Sciences and the Institute of Mathematics. His research focuses on Arakelov geometry, automorphic forms (particularly modular forms), and their intersections. Notable contributions include advancements in arithmetic intersection theory with logarithmic singularities and sup-norm bounds for modular forms. He is also deeply engaged in mathematics education, leading initiatives for teacher training and promoting mathematical talent through networks like the Berlin School Mathematics Network. Active in academic service, he served as EMS Education Committee Chair (2017–2022) and President of the German Mathematical Society (2013/14). His work bridges pure mathematics with pedagogical innovation, emphasizing public understanding through popular science publications. Research: Arakelov geometry, modular forms, L-functions, hyperbolic geometry methods Education: Teacher training programs, math talent promotion, textbook authorship Affiliations: Leibniz Institute for Science and Mathematics Education (IPN), EMS, Deutsche Akademie der Technikwissenschaften Key educational contributions include Felix-Klein teacher training programs and co-authoring standards for mathematics teacher education. His publications span advanced mathematical research and accessible expositions on topics like Fermat’s Last Theorem and Riemann Hypothesis.

Zachary Kincaidمشاهده پروفایل

دانشیار

• Program Analysis
• Formal Methods
• Programming Languages
• +۴ مورد دیگر

Zachary Kincaid is an Associate Professor in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. His research focuses on program analysis, logic, and programming languages, with an emphasis on making program analysis compositional and robust. He received his PhD from the University of Toronto under the supervision of Azadeh Farzan. His work has been implemented in the Duet program analyzer, and he has an Erdős number of 3. Dr. Kincaid's research interests include: Compositional program analysis techniques Algebraic approaches to program analysis Termination analysis and ranking function synthesis Verification of concurrent and parallel programs Automated reasoning and decision procedures Analysis of numerical programs and loops His recent publications show a strong focus on developing novel techniques for program analysis that bridge theoretical computer science with practical verification tools, particularly in nonlinear analysis, quantified reasoning, and compositional verification. Dr. Kincaid has received research support from ONR grant N00014-19-1-2318 for his work on robust program analysis. He has advised graduate students including: Current: Jake Silverman, Nicolas Koh, Nikhil Pimpalkhare Graduated: Shaowei Zhu (PhD 2024, Researcher at Amazon), Charlie Murphy (PhD 2023, Postdoc at University of Wisconsin–Madison) Dr. Kincaid teaches courses including: COS 320 – Compiling Techniques (Spring 2024, 2022, 2020, 2019) COS 516 / ELE 516 – Automated Reasoning about Software (Fall 2025, 2022, 2018) COS 217 – Introduction to Programming Systems (Fall 2024) COS IW – Practical Solutions to Intractable Problems (Fall 2023, Spring 2023, 2018, 2017) COS IW – Little Languages (Spring 2018) COS 597D – Reasoning about concurrent systems (Fall 2016)

Janis Nötzelمشاهده پروفایل

پژوهشگر

• Quantum Information Theory
• Machine Learning
• Sensor Networks
• +۳ مورد دیگر

Dr. Janis Nötzel is a senior researcher at the Chair of Theoretical Information Technology (Technische Universität München) and leads his independent Emmy Noether research group. Previously, he held a postdoctoral position at Universitat Autónoma de Barcelona and contributed to 5G practical implementations at TU Dresden's 5G Lab. His research spans quantum information theory, physical layer security, and machine learning applications. Key focuses include Quantum channel capacities under adversarial conditions Entanglement-assisted communication Quantum software frameworks (QuNetSim, QuReed) Interplay between classical and quantum communication Security analysis for 6G networks Resource optimization in quantum systems Recent publications (2023-2025) showcase innovations in Quantum satellite communication architectures Hybrid quantum-classical clustering algorithms Photonic processor instability modeling Covert capacity of compound channels Quantum key distribution resilience Free-space Bessel beam communication He actively collaborates with 6G-life research hub and contributes to quantum network simulation tools. Grants include funding from DFG (Leibniz Program), BMBF (6G-life, Q.Link.X), and StMWi (6G Zukunftslabor Bayern).

• Mathematical Statistics
• Applied Statistics
• Probability Theory
• +۶ مورد دیگر

Holger Dette is a Professor and Chair Holder of Stochastics (specializing in Statistics) at the Faculty of Mathematics, Ruhr University Bochum. He leads the prominent Group Dette within the Institute of Statistics, overseeing a team of researchers, doctoral students, and administrative staff including Birgit Tormöhlen as team assistant. His research group is deeply integrated within the university's mathematical ecosystem, collaborating with other research groups across algebra, analysis, numerics, and topology. Dette's research spans mathematical statistics with strong applications in real-world problems. His primary interests include optimal experimental design, time series analysis, functional data, change point problems, nonparametric regression, biostatistics, special functions, goodness-of-fit tests, and random matrices . His work bridges theoretical statistics with practical applications, particularly evident in his collaborations with pharmaceutical giants Novartis and Bayer AG in biostatistics, as well as Quasol, a spin-off company from his statistics institute. His recent publications (2024-2025) reveal a research program increasingly focused on high-dimensional and functional data analysis, privacy-preserving statistics, and novel methodological approaches to longstanding statistical problems. Dette's work shows strong interdisciplinary connections, particularly with biomechanics (analyzing joint angles during fatigue phases) and data science (addressing challenges in the era of big data). His research group is actively involved in multiple DFG-funded projects including the newly established 'Small Data' collaborative research center (Sonderforschungsbereich 1597) and the Spatio-temporal Statistics for the Transition of Energy and Transport (Transregio 391). Dette has received significant recognition including the prestigious Humboldt Research Award . His paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors' achieved second place at the CSAW'24 Applied Research Competition MENA. His research group has also secured multiple significant funding awards from the German Research Foundation (DFG). As an advisor, Dette supervises numerous doctoral and master's students including Pascal Quanz, Marius Kroll, and Carina Graw. His group offers statistical consulting services for scientists and students across bachelor's, master's, and doctoral phases. The group maintains strong industrial partnerships, particularly in biostatistics applications, demonstrating Dette's commitment to translating theoretical statistics into practical solutions for real-world challenges.

Matthew K. Tamمشاهده پروفایل

دانشیار

• continuous optimisation and its applications
• monotone operator theory
• variational and convex analysis
• +۳ مورد دیگر

Matthew K. Tam is an Associate Professor at the School of Mathematics and Statistics, The University of Melbourne, specializing in Operations Research. He is also an investigator at the Melbourne Centre for Data Science and an associate investigator in the ARC Training Centre OPTIMA. PhD in Mathematics (2016) from University of Newcastle under Jonathan Borwein Postdoctoral research at University of Göttingen with RTG-2088 and Alexander von Humboldt Foundation Junior Professor at University of Göttingen (2017-2020) His research focuses on continuous optimization, monotone operator theory, and variational analysis, with applications in wavelet construction and inverse problems. Key trends include distributed algorithms, resolvent splitting, and convergence analysis for feasibility problems. Discovery Early Career Researcher Award (DECRA) Alexander von Humboldt Fellowship He collaborates with institutions like ANZIAM, Springer, and IEEE, with publications spanning mathematical optimization, harmonic analysis, and computational mathematics. His work emphasizes algorithmic design for complex data systems and real-world applications in imaging and industrial modeling.

Christian Plesslمشاهده پروفایل

استاد

• High-Performance Computing
• Reconfigurable Computing
• FPGA Acceleration
• +۳ مورد دیگر

Prof. Dr. Christian Plessl is a W3 Professor of High-Performance Computing at the Institute of Computer Science, University of Paderborn. He leads the Paderborn Center for Parallel Computing (PC²), a national HPC center within the NHR alliance. His roles include Director of PC², Board Member of the NHR association, and member of the Sonderforschungsbereich 901. Education: PhD (Dr. sc. ETH) in Computer Engineering, ETH Zürich (2006) MSc in Electrical Engineering, ETH Zürich (2001) Postdoc at ETH Zürich (2007–2011) Research Interests: Architecture and tools for high-performance parallel and reconfigurable computing, FPGA acceleration, quantum chemistry, scientific computing, adaptive systems, and energy-efficient HPC solutions. Key projects include EKI-App (FPGA-based neural networks), FPGA4XPCS (X-ray spectroscopy), and HighPerMeshes (unstructured grid frameworks). Publications: Over 100 peer-reviewed works, focusing on FPGA acceleration, HPC frameworks, and quantum computing. Recent trends emphasize energy-efficient neural networks, FPGA-based quantum computing, and scalable HPC algorithms. Awards: Best Paper Awards at HEART 2023, ReConFig 2012/2014 Paderborn University Research Awards (2018, 2009) SEW-EURODRIVE Student Award (2001) Grants & Projects: Principal investigator in DFG, BMBF, and EU-funded projects. Collaborates with AMD/Xilinx, Intel/Altera, and Fujitsu. Leads initiatives like PerficienCC (custom computing) and HighPerMeshes. Labs/Teams: Directs the High-Performance Computing group at PC², focusing on FPGA supercomputing and HPC infrastructure. Active in the NHR alliance for national HPC coordination.

Anna C. Balazsمشاهده پروفایل

استاد

• Soft matter theory
• Polymer physics
• Smart materials
• +۴ مورد دیگر

Anna C. Balazs is Distinguished Professor and John A. Swanson Chair of Engineering in the Department of Chemical Engineering at the University of Pittsburgh, with an adjunct appointment in Chemistry and visiting professorships at Scripps Research Institute, UT-Austin and Oxford University. In 2025 she receives the €10,000 Gutenberg Research Award from Johannes Gutenberg University Mainz (JGU) for her pioneering theoretical work on smart soft materials. She earned an A.B. in Physics from Bryn Mawr College (1975) and a Ph.D. in Materials Science from MIT (1981), followed by post-doctoral research at Brandeis, MIT and UMass. Research interests span theoretical and computational soft-matter physics, focusing on: Statistical-mechanical modelling of polymer blends and composites Self-oscillating and chemo-responsive hydrogels Active matter, enzyme-powered swimmers and self-propelling sheets Self-healing, shape-morphing and bio-inspired materials Computer simulation of colloidal and interfacial phenomena Recent publications (2023-2025) demonstrate a clear trend toward integrating chemistry, fluid mechanics and elasticity to create life-like, autonomous soft machines. Key contributions include: Harnessing enzyme pumps to drive macroscopic sheet locomotion Designing chemically communicating micro-post arrays Creating dissipative materials with programmable, hierarchical 3-D architectures Scientific awards include: Gutenberg Research Award 2025 Polymer Physics Prize, American Physical Society SF Boys-A. Rahman Award, Royal Society of Chemistry Langmuir Lectureship Award, American Chemical Society Election to the U.S. National Academy of Sciences (2021) She serves on the Advisory Board of the DOE-BES Materials Council and on editorial boards for Langmuir , Soft Matter and Polymer Reviews . Her group collaborates closely with experimental teams world-wide, including the DFG-NSF “Confine” partnership with JGU and the CoM2Life Cluster of Excellence initiative.

Jana Bochertمشاهده پروفایل

استاد

• Construction Informatics
• Construction Mechanics
• Fundamentals of Civil Engineering
• +۴ مورد دیگر

Prof. Dr.-Ing. Jana Bochert is a faculty member at the Faculty of Sustainable Infrastructure at Technische Hochschule Ingolstadt , specializing in Construction Informatics , Construction Mechanics , and Foundations of Civil Engineering . She focuses on seismic analysis, soil-structure interaction, and structural dynamics, particularly for critical infrastructure like nuclear power plants. Email: Jana.Bochert@thi.de Phone: +49 841 9348-2393 Office: Room CN115 Research Interests Her research centers on: Seismic soil-structure interaction Wave propagation in soil under dynamic loads Development of computational methods for structural analysis Dynamic response of nuclear power plant foundations Impact of transportation and demolition on soil vibrations Finite element modeling of civil structures Publications & Research Trends Her work spans 2008–2021 , with a focus on: Seismic safety protocols for nuclear infrastructure Time-domain modeling of soil dynamics Comparison of simulation methods (2D vs 3D) Acoustic and vibration analysis in urban environments Integration of soil nonlinearities into structural design Dynamic load effects from trains and demolition

Thomas Mussenbrockمشاهده پروفایل

استاد

• Low-Temperature Plasmas
• Material Processing
• Nanoelectronics
• +۳ مورد دیگر

Prof. Thomas Mussenbrock is a Full Professor (W3) of Applied Electrodynamics and Plasma Technology at Ruhr University Bochum, leading the Applied Electrodynamics and Plasma Technology department within the Faculty of Electrical Engineering and Information Technology. He holds a PhD from Ruhr University Bochum (2004) and habilitation (2009), previously serving as a professor at Brandenburg University of Technology (2016-2020). His research focuses on low-temperature plasmas, nanoelectronics, and plasma modeling, with applications in material processing and energy systems. Education: Bachelor's (1995) Bielefeld University of Applied Sciences Master's (1999) Ruhr University Bochum PhD (2004) Ruhr University Bochum Habilitation (2009) Ruhr University Bochum His research interests include plasma dynamics in atmospheric pressure jets, memristive devices for neuromorphic computing, and energy-efficient plasma processes. Recent work emphasizes CO₂ conversion via plasma jets, electron dynamics in capacitive discharges, and scalable plasma modeling techniques. He collaborates on projects like the TopING doctoral program and plasma catalysis initiatives. Publications span journals and conferences, with a focus on experimental and computational plasma physics. He advises on graduate studies and chairs faculty committees at Ruhr University Bochum.

Erik Rodnerمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Data Science
• +۵ مورد دیگر

Prof. Dr. Erik Rodner is a faculty member at the University of Applied Sciences Berlin (HTW Berlin), where he serves as a Professor for Machine Learning and Data Science. He also contributes to the School of Engineering Sciences - Technology and Life. His research spans computer vision, machine learning, and biomedical applications, with a focus on learning with limited data, robust visual recognition models, and medical image analysis. He has developed innovative methods for medical diagnostics, industrial classification, and anomaly detection. Recent publications (2025-2016) highlight his expertise in visual in-context learning, semi-weakly segmentation, and active learning frameworks. He has collaborated with institutions such as ZEISS Group, Friedrich Schiller University Jena, and UC Berkeley. Scientific Awards: Award for Excellent Teaching (2023)

• Stochastic Analysis
• Rough Path Theory
• Mathematical Finance
• +۳ مورد دیگر

Peter K. Friz is an Einstein Professor in Mathematics at TU-Berlin, affiliated with the Institute of Mathematics, and associated with the Weierstrass Institute for Applied Analysis and Stochastics. His research focuses on stochastic analysis, rough path theory, and mathematical finance, with particular emphasis on volatility modeling and applications to quantitative finance. He has held prestigious grants, including ERC Starting and Consolidator Grants, and coordinates the DFG research unit 'Rough paths, stochastic partial differential equations, and related topics.' Friz's work bridges theoretical stochastic analysis and practical financial applications, emphasizing rough path theory and its implications for differential equations and stochastic processes. His collaborations include organizing international conferences and courses on rough paths, with invited lectures at institutions like Cambridge, Paris, and Bonn. Supported by DFG, the European Research Council, and the Einstein Foundation, his research explores geometric aspects of pathwise analysis and stochastic volatility dynamics. He has advised numerous PhD students and maintains active roles in academic administration, including coordinating Berlin Mathematical School programs and teaching advanced topics in stochastic calculus. His contributions to rough path theory and stochastic finance are recognized through his academic leadership and influential publications, including co-authoring the seminal book Multidimensional Stochastic Processes as Rough Paths .

Jia-Jie Zhuمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Optimal Transport
• +۵ مورد دیگر

Jia-Jie Zhu is a machine learner and applied mathematician currently serving as head of an independent research group at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin, with an upcoming appointment as tenured associate professor at KTH Royal Institute of Technology in Stockholm. Previously, he conducted postdoctoral research in machine learning at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, following doctoral studies in optimization and numerical analysis at the University of Florida. His research focuses on the mathematical foundations of machine learning and optimization, particularly at the intersection of computational algorithms, dynamical systems, and probability theory. Key areas include: Robust probabilistic machine learning algorithms Kernel methods for distribution manipulation Variational methods for optimization over probability distributions Gradient flows and optimal transport theory Wasserstein and Fisher-Rao geometry Applications in generative modeling and causal inference Dr. Zhu's recent work reveals deep connections between partial differential equations, kernel methods, and machine learning, resulting in theoretically grounded algorithms for handling distribution shifts. His publications demonstrate a consistent trajectory from foundational optimization theory to cutting-edge applications in robust learning and generative modeling, with increasing emphasis on the mathematical structures underlying modern ML systems. He has secured significant research funding, including a DFG Project on 'Optimal Transport and Measure Optimization Foundation for Robust and Causal Machine Learning' within the Priority Program 'Theoretical Foundations of Deep Learning' (SPP 2298), and actively organizes academic events such as the Workshop on Optimal Transport from Theory to Applications (OT-DOM) and upcoming sessions at ICSP 2025 and SwissMAP. As an educator, Dr. Zhu teaches nonparametric statistics at Humboldt University of Berlin and serves as area chair for major conferences including AISTATS 2025. He maintains an active research group with opportunities for master's students, PhD candidates, and postdoctoral researchers interested in the mathematical frontiers of machine learning.

Magdalena Schreter-Fleischhackerمشاهده پروفایل

پژوهشگر

• Computational Mechanics
• Additive Manufacturing
• Multi-phase Flow Modeling
• +۳ مورد دیگر

Dr. Magdalena Schreter-Fleischhacker works at the Technical University of Munich within the Professorship of Simulation for Additive Manufacturing . Her research focuses on physics-based computational modeling of coupled liquid-powder-gas dynamics in metal additive manufacturing, including melt pool dynamics and powder-gas interactions . She specializes in multi-phase flow modeling using cut-element and diffuse interface methods with continuous/discontinuous Galerkin schemes . She also develops constitutive models for quasi-brittle materials like 3D printed concrete and rock, incorporating anisotropy , gradient-enhanced damage mechanics , and micropolar continua . Her computational work leverages matrix-free algorithms and parallel computing , with significant contributions to the deal.II finite element library . Research Interests Physics-based computational modeling of coupled liquid-powder-gas dynamics in additive manufacturing Multi-phase flow simulation using sharp/diffuse interface methods Advanced constitutive modeling for quasi-brittle materials (rock, soils, 3D printed concrete) High-performance computing and matrix-free algorithms Notable Contributions Development of consistent diffuse-interface models for melt-vapor dynamics Improvements to continuum surface flux models in additive manufacturing Formulation of gradient-enhanced damage-plasticity models for geological materials Principal contributor to the deal.II library (version 9.6) Supervised Student Projects Johannes Resch (2024): DG-based thermo-hydrodynamic melt pool simulations Julian Brotz (2024): DEM-FEM coupling for fluid-powder interaction Andreas Ritthaler (2024): Matrix-free cutDG formulation for complex flows Tinh Vo (2023): Laser modeling for melt pool simulations Scientific Awards ERC Starting Grant recipient

Boris Vexlerمشاهده پروفایل

استاد

• Numerical Analysis
• Optimal Control
• Partial Differential Equ as Mathematics (summa cum laude)
• +۱۰ مورد دیگر

Prof. Boris Vexler, born in 1977 in Moscow, Russia, is a Professor of Optimal Control at the TUM Department of Mathematics under the TUM School of Computation, Information and Technology . He has served as Dean of Studies since 2015 and as speaker of the International Research Training Group IGDK 1754 since 2012. Education: Diploma (2000) and Ph.D. (2004) in Mathematics from the University of Heidelberg; Habilitation (2008) from the University of Graz. His research focuses on numerical analysis of partial differential equations (PDEs) , particularly finite element methods for optimal control problems governed by parabolic, elliptic, and hyperbolic PDEs. Key contributions include error estimates , adaptive discretization , and handling state constraints and measure-valued controls . Recent publications emphasize transient Stokes equations , Navier-Stokes control , and sparsity-constrained optimization . His work often integrates scientific computing and uncertainty quantification . Scientific Awards: Award for best supervisor of the elite degree program TopMath (2018) Finalist, ECCOMAS Prize for best dissertation (2005) Leslie Fox Prize in Numerical Analysis, 2nd place (2004)

• Numerical Analysis
• Stochastic Partial Differential Equations
• Computational Physics
• +۱۲ مورد دیگر

Prof. Dr. Lubomir Banas is a full-time Professor at the Faculty of Mathematics , University of Bielefeld. His research focuses on numerical analysis of stochastic partial differential equations (SPDEs) , particularly in micromagnetism, phase field models, and stochastic games. He leads Subproject B03 in the SFB 1283 project 'Taming Uncertainty and Profiting from Randomness and Low Regularity in Analysis, Stochastics and Their Applications.' Research Interests: Numerical methods for SPDEs and singular-degenerate PDEs Adaptive finite element techniques and a posteriori estimates Phase field models (Cahn-Hilliard, obstacle potentials) Stochastic games with asymmetric information Computational micromagnetism and magnetostriction Self-organized criticality and nonlinear stochastic flows Recent work includes: 2025: Numerical approximation of biharmonic wave maps and stochastic games 2024: Sharp interface limits for stochastic Cahn-Hilliard equations 2023: Singular-degenerate SPDEs and a posteriori estimates 2022: Stochastic total variation flow and Hamilton-Jacobi-Bellman equations 2021: Nematic electrolytes and homogenization of two-phase flows He serves on examination boards for Bachelor's and Master's programs in Mathematics and Mathematical Physics, and supervises graduate students within the Bielefeld Graduate School in Theoretical Sciences . His publications (over 30) address convergence analysis, error estimation, and computational modeling in applied mathematics.