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Frances YungView profile

Research Fellow

• Discourse Relations
• Computational Linguistics
• Natural Language Processing
• +4 more

Dr. Frances Yung is a Postdoctoral Researcher at Saarland University's Department of Language Science and Technology within the Department of Computer Science. She has been affiliated with Prof. Vera Demberg's research group since April 2017 and is currently working on the DFG-funded SFB-1102 project "Information Density and Linguistic Encoding," specifically on project B2 "Cognitive modelling of information density for discourse relations." She is pursuing her habilitation, indicating career progression toward a higher academic position in the German university system. Dr. Yung's research focuses on discourse relations at the intersection of NLP, corpus linguistics, and experimental psycholinguistics. Her work explores how information density affects discourse relation marking through cognitive modeling approaches. She has developed expertise in discourse parsing, resource construction, annotation aggregation, and experimental pragmatics, with particular attention to multilingual aspects of discourse phenomena. Her research combines computational modeling with experimental methods to understand how speakers produce and comprehend discourse relations. Analysis of Dr. Yung's recent publications reveals a strong focus on discourse relation resources, particularly multilingual corpora like DiscoGeM 2.0 covering English, German, French, and Czech. Her work increasingly incorporates crowdsourcing methodologies and examines how large language models can be leveraged for discourse annotation tasks. She has made significant contributions to understanding the challenges of implicit discourse relation annotation and the biases introduced by different task designs in crowdsourcing environments. Active reviewer for major computational linguistics conferences (ACL, EMNLP, NAACL, EACL, COLING, IJCNLP) and workshops since 2016 Served as area chair for Sigdial 2024 Regular service on program committees for discourse-related workshops Dr. Yung has supervised multiple Master's theses on topics related to discourse relations, implicit relation identification, and domain adaptation. Her teaching portfolio includes courses on crowdsourcing linguistic annotations, discourse relations from cognitive and NLP perspectives, and recent advances in discourse processing. She has also served as a teaching assistant for data science and AI courses, demonstrating her commitment to interdisciplinary education at the intersection of computer science and linguistics.

Yan ZhangView profile

Lecturer

• Computer Science
• Artificial Intelligence
• Computer Vision
• +5 more

Yan Zhang is a scientific leader at Meshcapade and a guest lecturer at ETH Zurich's Computer Vision and Learning Group (VLG). He previously served as a postdoctoral researcher at ETH Zurich (2020-2023) and research intern at Max Planck Institute for Intelligent Systems (2018-2020). His research focuses on generative human foundation models, human motion and behavior synthesis, 3D human perception, and applications in AR/VR, embodied AI, and interactive avatars. He has pioneered methods for scene-conditioned motion generation, contact-aware reconstruction, and egocentric interaction modeling. His recent publications (2025-2020) span Real-time motor models for avatars (PRIMAL, ICCV'25) Diffusion architectures for motion (RoHM, CVPR'24) Scene-population algorithms (Odysseus, CVPR'22) Physics-aware reconstruction (EgoHMR, ICCV'23) Whole-body grasping models (SAGA, ECCV'22) Multi-modal datasets (EgoBody, ECCV'22) Scientific recognition includes the Qualcomm Innovative Fellowship Europe 2023 . He organized workshops at CVPR'25, ECCV'24, and ECCV'22, and served on senior program committees (AAAI'26) and area chairs (CVPR'25). As co-supervisor, he mentored student projects on diffusion-based hand motion capture, 3D pose estimation, body-scene interaction, and mixed reality navigation at ETH Zurich (2020-2023). His work bridges computer vision, machine learning, and computer graphics to advance human-centric AI systems.

Irwin YouseptView profile

Professor

• Electromagnetics
• Numerical Analysis
• Optimization
• +3 more

Prof. Dr. Irwin Yousept is a Full Professor of Mathematics at Universität Duisburg-Essen, leading the research group AG Optimal Control of Partial Differential Equations. His work focuses on the mathematical analysis and numerical solutions of electromagnetic problems, particularly in superconductivity and inverse problems. He holds a PhD from TU Berlin (2008) and has held academic positions at TU Darmstadt and TU Berlin. His research includes PDE-constrained optimization, numerical analysis, and applications in high-temperature superconductivity and electromagnetic shielding. Affiliations: Universität Duisburg-Essen, Fakultät für Mathematik Education: Diplom (2005), PhD (2008) in Mathematics from TU Berlin Research interests span Maxwell's equations, numerical methods for PDEs, and optimal control, with applications in superconductivity, electromagnetic shielding, and induction heating. He has authored over 40 publications and received awards including the Richard-von-Mises-Preis GAMM (2014). Current grants include DFG-funded projects on inverse problems and superconductivity.

Boris VexlerView profile

Professor

• Numerical Analysis
• Optimal Control
• Partial Differential Equ as Mathematics (summa cum laude)
• +10 more

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
• +12 more

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.

Xian LiaoView profile

Assistant Professor

• Partial Differential Equations
• Fluid Mechanics
• Dispersive Equations
• +2 more

JProf. Dr. Xian Liao is an Assistant Professor in the Faculty of Mathematics at Karlsruhe Institute of Technology (KIT), Germany. He leads the Junior Research Group on Analysis of PDEs and specializes in partial differential equations, fluid mechanics, and dispersive equations. Education: PhD in Mathematics (University Paris-Est Créteil, 2010-2013) Employment: Juniorprofessor at KIT (2018-present), Postdoc positions at University Bonn, AMSS Beijing, Charles University Prague His research focuses on nonlinear PDEs in fluid dynamics, including Navier-Stokes equations, Boussinesq systems, and zero Mach number limits. He teaches courses like Nonlinear PDEs , Fluid Mechanics , and Dispersive Equations . Scientific activities include organizing workshops and summer schools on fluid mechanics and PDEs. He supervises bachelor, master, and doctoral theses, with former students such as Zihui He and Johanna Mödl. Contact: xian.liao@kit.edu

• Arithmetic Geometry
• Algebraic K-Theory
• Non-Archimedean Geometry
• +1 more

Prof. Dr. Georg Tamme is a faculty member at the Institute of Mathematics (Johannes Gutenberg University Mainz). His research focuses on Arithmetic Geometry and Algebraic K-Theory , with significant contributions to non-Archimedean geometry, motivic homotopy theory, and regulator maps. Research Interests: Tamme's work bridges Algebraic K-Theory Arithmetic Geometry Non-Archimedean Geometry Differential Algebraic K-Theory Advising: He supervises doctoral students including Andreas Gieringer Luca Passolunghi

• Constructive Approximation
• Radial Basis Function Networks
• Neural Networks
• +4 more

Prof. Dr. rer. nat. habil. Detlef Hauke Mache is a full Professor of Mathematics and Applied Mathematics at the TH Georg Agricola University of Applied Sciences in Bochum, Germany, within the Faculty of Electrical Engineering, Information Technology, and Industrial Engineering. Since 2003, he has held the chair for Applied Mathematics with a focus on Constructive Approximation. Education: Diploma in Mathematics and Computer Science, University of Dortmund (1988) Doctoral degree (Dr. rer. nat.) in Mathematics, University of Dortmund (1991) Habilitation (Dr. habil.) in Mathematics, University of Dortmund (1997) Research Interests: Prof. Mache's research spans Constructive Approximation Theory , emphasizing the development and analysis of approximation methods. He explores Radial Basis Function (RBF) Networks and their applications in neural networks and fuzzy logic. His work integrates theoretical foundations with practical algorithms, particularly in numerical analysis and approximation techniques. Key areas include: Neural Networks and Fuzzy Logic Systems Quasi-Interpolation Methods Orthogonal Polynomial Expansions Integral Transforms and Convolution Structures Publications and Editorial Contributions: Prof. Mache has authored over 30 peer-reviewed papers and edited several volumes in approximation theory. His research trends indicate a focus on advancing approximation methods through theoretical insights and practical applications in neural networks and computational intelligence. Scientific Awards and Honors: While no specific awards are listed, his extensive editorial roles and habilitation qualification signify recognition in his field. Teaching and Advising: He teaches courses in Higher Mathematics, Applied Mathematics, Differential Equations, and Numerical Analysis. While no specific students are named, his long-standing academic positions suggest significant contributions to graduate education. Laboratories and Teams: As a professor in the Faculty of Electrical Engineering and Information Technology, he likely collaborates with interdisciplinary teams, though no specific labs are mentioned.

• Machine Learning
• Control Systems
• Robotics
• +3 more

Sebastian Trimpe is a Full Professor and Head of the Institute for Data Science in Mechanical Engineering at RWTH Aachen University, concurrently serving as Co-Executive Director of the RWTH Center for Artificial Intelligence since 2023. Previously, he led a Max Planck Research Group at the Max Planck Institute for Intelligent Systems from 2018 to 2022. His educational background includes: Ph.D. in Dynamic Systems and Control from ETH Zurich (2013) Dipl.-Ing. (M.Sc.) in Electrical Engineering from TU Hamburg (2007) MBA in Technology Management from TU Hamburg (2007) B.Sc. in General Engineering from TU Hamburg (2005) Professor Trimpe's research integrates machine learning with control theory to address safety and efficiency challenges in autonomous systems. His work spans theoretical frameworks for robust decision-making under uncertainty and practical implementations in robotics, with particular emphasis on event-triggered control, distributed systems, and data-efficient learning methodologies. Key contributions include novel approaches to safe reinforcement learning and model predictive control with guaranteed stability. Analysis of his recent publications reveals a pronounced focus on bridging machine learning with control engineering, especially in safety-critical robotics applications. Common themes include distribution-aware learning for medical diagnostics, diffusion-based control approximation, and hardware-in-the-loop validation of theoretical frameworks, demonstrating strong alignment between algorithmic innovation and real-world deployment. His scientific achievements have been recognized with prestigious honors: IFAC World Congress Interactive Paper Prize (2011) Klaus Tschira Award for public understanding of science (2014) Best Paper Award at International Conference on Cyber-Physical Systems (2019) Future Prize by Ewald Marquardt Stiftung (2020) As institutional leader, he directs the Institute for Data Science in Mechanical Engineering and co-leads the RWTH AI Center, overseeing strategic research initiatives and industry collaborations. His academic service includes editorial roles for IEEE Control Systems Society conferences and participation in the Cluster of Excellence 'Internet of Production'. The Institute for Data Science in Mechanical Engineering operates as a multidisciplinary hub where fundamental research in learning-based control meets industrial applications. Current projects focus on drone swarm coordination, deformable object manipulation, and medical diagnostics systems, leveraging both simulation environments and physical testbeds like the Mini Wheelbot platform.

• Theoretical Physics
• Cosmology
• String Theory
• +3 more

Professor Ivo Sachs is a distinguished theoretical physicist at the Ludwig-Maximilians-Universität München (LMU), where he holds a position at the Arnold Sommerfeld Center for Theoretical Physics with a Chair on Cosmology. His research spans multiple areas of theoretical physics with a particular focus on string theory, quantum field theory, and cosmological applications. He maintains an active research program with numerous recent publications in prestigious journals. Professor Sachs' research interests center around fundamental theoretical physics, with significant contributions to string field theory, cosmological perturbation theory, and the mathematical structures underlying quantum gravity. His work often bridges abstract mathematical concepts with physical applications, particularly in understanding the early universe and quantum aspects of gravity. He has developed innovative approaches to studying cosmological correlators, spinning particles, and the relationship between quantum field theory and gravitational physics. Analysis of his recent publications reveals a strong focus on the intersection of cosmology and string theory, with particular attention to mathematical structures in quantum gravity. His work demonstrates consistent exploration of how quantum field theory techniques can be applied to cosmological problems, especially regarding correlation functions in the early universe. He frequently collaborates with researchers across Europe, indicating an active international research network. Martín Enríquez Rojo (PhD, 2022): Asymptotic symmetries in FLRW and deformations of gravitational symmetry algebras

Bernhard Schmidt-HerthaView profile

Professor

• Continuing education research
• University research
• Education in the second half of life
• +5 more

Professor Bernhard Schmidt-Hertha is a leading academic at Ludwig Maximilian University of Munich, holding the Chair in Adult Education within the Faculty of Psychology and Education. His extensive expertise spans lifelong learning, digitalization in education, and continuing education research, with a particular focus on older adults and intergenerational learning. Research Focus: Continuing education research University studies and educational transitions Education in the second half of life Informal learning processes Digitalization in educational systems His current research projects include investigating dropout in adult literacy programs (DrAG), integrating AI into vocational training (KIB³), and developing digital skills for refugees and immigrants (DigIT). These initiatives highlight his commitment to addressing contemporary educational challenges through innovative research and practical applications. Publications and Contributions: Professor Schmidt-Hertha has authored numerous influential monographs and scholarly articles. His recent work explores digital pedagogies, adult learning in digital contexts, and the socio-ecological perspectives of educational transitions. His publications appear in top-tier journals, reflecting his significant impact on the field. Consultation and Contact: He holds regular consultation hours on Tuesdays from 10:00-11:00 a.m. by appointment via email: b.schmidt@edu.lmu.de .

• Transmission Electron Microscopy (TEM) Method Development
• Charge Particle Optics and Scattering Theory
• Magnetic Nanotextures
• +3 more

Dr. Axel Lubk is a Group Leader at the Institute for Solid State Research (IFW Dresden) , specializing in advanced electron microscopy techniques for materials science. His research spans four key areas: (1) TEM method development (high-resolution imaging, tomography, holography, and in-situ techniques), (2) charge particle optics and scattering theory , (3) magnetic nanotextures (domain walls, skyrmions), and (4) plasmonics (mode hybridization in heterogeneous structures and semiconductor heterostructures). Dr. Lubk’s work focuses on three-dimensional magnetic texture analysis using electron holography and tomography, particularly in systems like skyrmion tubes , FeGe , and Cr2O3 thin films . He has pioneered techniques for vector-field electron tomography and phase retrieval under varying boundary conditions, advancing nanoscale magnetic imaging. His recent studies include plasmonic properties in AgAu nanosphere chains , thermoelectric multilayer systems , and topological insulators like NiRh2Sb and TaTMTe4 . Dr. Lubk has published extensively in high-impact journals such as Nature Communications and Advanced Materials , with a focus on TEM instrumentation and quantitative analysis . He frequently presents at international conferences like the International Microscopy Congress and European School of Magnetism , emphasizing applications in spintronics , quantum materials , and nanostructured systems . His contributions to holographic vector-field electron tomography and machine learning for spectrum-image data have set new standards in electron microscopy.

Dongyoon LeeView profile

Assistant Professor

• Software Engineering
• Programming Languages
• Security
• +2 more

Dongyoon Lee is an Assistant Professor in the Department of Computer Science at Stony Brook University's College of Engineering and Applied Sciences. His research focuses on software engineering challenges in programming languages and security, with emphasis on regular expression analysis and empirical studies. His research interests include: Software reliability through regex generalizability testing Developer decision-making in security-critical contexts Empirical measurement of programming language features across ecosystems Security implications of common coding patterns His publication trends reveal strong focus on practical security vulnerabilities in developer tools, particularly analyzing regular expression implementations across multiple programming languages. The work combines empirical measurement with human factors analysis to identify systemic risks. Lee actively contributes to the software engineering community through leadership roles in major conferences: LCTES 2025: Program Committee & Steering Committee LCTES 2023: Program Chair & Session Chair CGO 2022: Author of HECATE compiler optimization work ASE 2019: Two regex-related research papers His advising and community engagement is demonstrated through consistent program committee service since 2016 across PLDI, LCTES, CC, CGO, and ASE conferences. He has chaired sessions on storage systems, scheduling, and techniques for specific domains, showing breadth in systems software research.

Markus ZimmermannView profile

Researcher

• Medical Imaging
• Quantitative MRI
• Neuroscience
• +3 more

Dr. Markus Zimmermann is a researcher at the Institute of Neuroscience and Medicine (INM-4: Physics of Medical Imaging) at the Research Center Jülich. His work focuses on advancing quantitative MRI techniques, particularly in water content mapping, multiparametric imaging, and ultrahigh-field MRI applications. He contributes to developing methods for eddy current characterization, multi-exponential relaxometry, and rapid whole-brain protocols. His research addresses neurological and medical imaging challenges, including cerebral pathologies and neurobiological implications. Key areas of expertise include MRI parameter estimation, medical imaging algorithms, and the integration of advanced imaging techniques for clinical and neuroscience applications. His projects often involve collaborations to validate methodologies using in vivo/ex vivo experiments and super-resolution reconstruction. Dr. Zimmermann’s work aims to enhance diagnostic precision and understanding of brain physiology through innovative MRI technologies.

Thomas KoehlerView profile

Researcher

• Tomographic imaging
• Inverse problems
• Computed tomography
• +3 more

Thomas Koehler is a Rudolf Diesel Industry Fellow at the Technical University of Munich (TUM) , affiliated with the Department of Radiology under the Faculty of Medicine. He leads the Focus Group on Phase Contrast Computed Tomography and collaborates with Philips Research Laboratories in Hamburg. His academic work bridges industry and academia, focusing on advanced imaging techniques. Education : Studied physics at the Christian-Albrechts-University in Kiel (1989–1994), earned a Ph.D. from Philips Research Hamburg (1998). His research spans tomographic imaging, inverse problems, and iterative reconstruction algorithms for CT and phase-contrast X-ray imaging. Key contributions include advancements in helical cone-beam CT and grating-based phase-contrast imaging. Research Interests : Koehler’s work centers on tomographic imaging techniques, phase-contrast X-ray imaging, and medical diagnostics. He has pioneered innovations in dark-field chest imaging for conditions like emphysema and COVID-19 pneumonia. Collaborations with TUM’s Radiology Department drive cutting-edge iterative reconstruction methods. Labs/Teams : Leads the Phase Contrast Computed Tomography Focus Group at TUM-IAS, collaborating with industry partners like Philips. His team develops novel algorithms for medical imaging, emphasizing clinical applicability and hardware optimization.