جست‌وجوی استاد و پژوهشگر

Nadine Aburummanمشاهده پروفایل

مدرس

• Computer Graphics and Animation
• Real-time Computer Graphics
• Extended Reality (XR)
• +۸ مورد دیگر

Nadine Aburumman is an Associate Dean for Equality, Diversity and Inclusion (EDI) and Lecturer in Computer Science at Brunel University London, within the College of Engineering, Design and Physical Sciences. She leads the Graphics and Extended Reality Team (GERT) and is a member of the Centre for AI: Social and Digital Innovation and the Interactive Multimedia System (IMS) research group. Her academic journey includes postdoctoral positions at University College London, the Computer Science Research Institute of Toulouse, and Friedrich-Alexander University Erlangen-Nürnberg. Dr. Aburumman holds a BSc and MSc in Computer Science, with her MSc specializing in computer graphics, vision and imaging, and a PhD in computer science focusing on digital human and shape simulation. She was awarded a PhD Erasmus Mundus Scholarship for her doctoral studies at Sapienza University of Rome. She also holds FHEA status (PgCAP in academic practice) and is a Professional Member of the British Computer Society (BCS). Her research spans real-time computer graphics , extended reality , and AI-powered immersive technologies . She investigates interactive character animation, physically based simulations, and the application of VR/AR/XR in interdisciplinary contexts including neuroscience, education, and social interaction. Her work bridges computer science with practical applications in healthcare, training, and human-computer interaction, with a particular focus on making technology accessible and inclusive. Dr. Aburumman's publication record shows increasing interdisciplinary work, with recent publications focusing on collaborative virtual environments, haptic feedback systems, and the integration of neuroscientific methods like fNIRS to study human responses in virtual environments, particularly with neurodiverse populations. Her work demonstrates a clear trajectory from foundational graphics research toward applied, socially relevant technology development. 2024: Runner-Up: Lecturer of the Year (Student Led Award-University Level) 2023: The Recognition of Excellence (RoE) Award 2022: Brunel Research Initiative And Enterprise Fund (BRIEF) Award 2016-2018: CIMI Post-doctoral Fellowships 2014: Best paper and best presentation awards at the 30th SCCG conference 2012: EU Erasmus Mundus PhD Scholarship Dr. Aburumman has supervised over 140 undergraduate students (including 50 Final Year Projects) and 16 successful MSc dissertations. Notable students include Mariama Kebbeh Suko who won the 2021 BCS prize for her AR project on African & Black History, and Brandon Michael whose work on VR language pedagogy was accepted for publication. She currently supervises PhD students Mingzhao Zhou (real-time visual and haptic feedback) and Rania Xanthidou (haptic feedback in VR learning environments). Her research is supported by multiple grants including Royal Society Partnership Grants, RCIF Grants, and projects with the RESPECT4Neurodevelopment initiative. As leader of the Graphics and Extended Reality Team (GERT), Dr. Aburumman fosters interdisciplinary collaboration across computer science, neuroscience, and education. Her team works on projects ranging from droplet-solid interaction simulations to VR systems for neurodiverse children, reflecting her commitment to applying graphics research to real-world challenges. She co-coordinated the AI Centre Thought Leadership series for 2023-2024 and is actively involved in outreach through programs like Code First Girls and Made In Brunel.

Dietmar Weinmannمشاهده پروفایل

پژوهشگر

• Theoretical Solid State Physics
• Quantum Effects in Electronic Properties
• Mesoscopic Physics
• +۱ مورد دیگر

Dietmar Weinmann is a Senior Researcher at the CNRS (Centre National de la Recherche Scientifique) affiliated with the IPCMS (Institut de Physique et Chimie des Matériaux de Strasbourg) and the University of Strasbourg. His work focuses on theoretical solid-state physics, particularly quantum effects in electronic properties, mesoscopic physics, and quantum transport phenomena. His research explores non-local heating in quantum thermoelectrics, scanning gate microscopy applications in graphene and semiconductor heterostructures, power dissipation asymmetry in quantum point contacts, and orbital magnetization mechanisms in mesoscopic systems. Key themes include electron correlations, spin-orbit interactions, and disorder effects in nanoscale devices. Scientific awards include the Marie Curie Fellowship during his postdoctoral work at SPEC Saclay. He teaches an elective course on Electronics for Quantum Science and Technology at the University of Strasbourg. As a member of the Mesoscopic Quantum Physics team, his research combines theoretical modeling with experimental collaborations on quantum transport imaging and inverse problem solving via machine learning.

• Computer Vision
• Document Analysis
• Optical Character Recognition
• +۳ مورد دیگر

Alexander Hartelt is a researcher at the Institute of Computer Science, Faculty of Mathematics and Computer Science, University of Würzburg. He works at the Chair of Artificial Intelligence and Knowledge Systems (Computer Science VI) focusing on computer vision applications for historical document digitization. His research interests include: Computer Vision for historical document analysis Layout recognition and segmentation algorithms Optical character recognition (specializing in handwritten documents) Deep learning-based information extraction Digital preservation of cultural heritage materials Hartelt's recent publications demonstrate strong focus on medieval music manuscripts and historical print digitization. His work combines contour-based segmentation, deep learning networks, and open-source tool development to address challenges in historical document processing. Key application areas include music notation transcription and OCR for aged printed materials. Hartelt has been actively teaching since Winter Semester 2020/21, supervising: Artificial Intelligence I exercises Current Trends in Artificial Intelligence seminars Software internship topics His primary research projects include: Corpus Monodicum : Researching, transcribing and editing historically significant monodic music collections using the Ommr4all transcription tool Segmentation of Old Prints : Developing algorithms for segmentation and transcription of historical printed materials using pixel classifiers, contour-based approaches, and baseline detection methods

Jason Cantarellaمشاهده پروفایل

استاد

• Geometry
• Topology
• Geometric Knot Theory
• +۳ مورد دیگر

Jason Cantarella is a Professor in the Mathematics Department at the University of Georgia. His research focuses on geometric knot theory, particularly the shapes of random curves and polygons. He teaches Differential Geometry of Curves and Surfaces (MATH 4250) every spring and specializes in the Multivariable Mathematics (MATH 3500-3510) course sequence. His research interests center on geometric properties of random polygons and knots, using differential, algebraic, and symplectic geometry of polygon spaces. Dr. Cantarella is particularly interested in questions like how likely it is that a random curve is knotted or what can be expected from a random diagram of a random space curve. His work bridges pure mathematics with applications in polymer physics and molecular biology. His recent publications show a strong focus on random polygons, topological polymers, geometric probability, and computational methods for analyzing knotting phenomena. There's a clear progression from theoretical foundations to algorithm development and practical applications, particularly in understanding the statistical properties of knotted structures in physical systems. Dr. Cantarella leads a research group that includes graduate students Tom Needham, Michael Berglund, and Harrison Chapman, as well as postdoctoral fellow Clayton Shonkwiler. He collaborates with faculty members Jason Parsley and Matt Mastin at UGA, and internationally with researchers including Tetsuo Deguchi at Ochanomizu University, the Uehara group at Kyoto University, and Clayton Shonkwiler of Colorado State University. He has developed several computational tools including Octrope (for polygonal tube radius calculation), plCurve (polygon library), Ridgerunner (knot tightening software), and Tsnnls (sparse non-negative least squares solver). These tools have enabled significant advances in the computational study of geometric knot theory and random polygon spaces.

Saar Hersonskyمشاهده پروفایل

استاد

• Geometry
• Topology
• Geometric Group Theory
• +۷ مورد دیگر

Saar Hersonsky serves as Professor of Mathematics at the University of Georgia's Department of Mathematics in Athens, with office location at 408 Boyd GSRC. Contact details include email saarh@uga.edu, phone (706) 542-2111, and fax (706) 542-2573. His research bridges two primary disciplines: Mathematics : Geometric and Non-Smooth Analysis, Topology, Conformal Geometry, Geometric Structures in Low Dimensional Topology, and Geometry of Negatively Curved Spaces Computer Science : Computer Networks, Image Science, Machine Learning, and Data Science No scientific awards or honors were documented in the source material. Information regarding student advising, research grants, laboratory facilities, or collaborative teams was not provided in the available content. Academic profiles are accessible via Google Scholar, Research Gate, and LinkedIn.

• Biodiversity
• Evolutionary Biology
• Zoology
• +۳ مورد دیگر

Gabriele Gerlach is a Professor at the Carl von Ossietzky University of Oldenburg since 2007, affiliated with the Institute of Biology and Environmental Sciences and leading the Department of Biodiversity and Evolution of Animals. She also serves as an Adjunct Senior Scientist at the Marine Biological Laboratory (USA) and an Adjunct Professor at the Boston University Marine Program. Education : Ph.D. (1990) and Habilitation (2000) in Zoology and Ecology at the University of Constance, Germany. Research Interests : Biodiversity, evolutionary biology, and ecology combined with mathematical approaches. Her work focuses on operator theory, spectral analysis, and dynamical systems applied to biological and environmental questions. Publications : Recent contributions include mathematical frameworks for kernel operators, transition semigroups, and spectral convergence, bridging abstract analysis with computational biology.

Emily A. Larsenمشاهده پروفایل

استادیار

• Engineering education
• Continuum robot modeling
• Soft robotic medical devices
• +۱ مورد دیگر

Emily A. Larsen serves as an Assistant Professor (Career Track) in the Department of Mechanical and Materials Engineering at Washington State University. Her academic career is rooted at WSU, where she earned all her degrees, and she actively contributes to both research and education in mechanical engineering. Dr. Larsen's educational background is exclusively from Washington State University: Ph.D. in Mechanical Engineering (2022) M.S. in Mechanical Engineering (2020) B.S. in Mechanical Engineering (2018) Her research program bridges engineering education and advanced robotics. She pioneers methods for integrating agile project management into capstone design courses, enhancing student learning. In robotics, she develops continuum models for soft robots and creates medical devices using soft robotics, with a focus on shape memory alloy actuators for precise control. Dr. Larsen's publications reveal a trajectory from materials characterization (2018-2019) to robotics applications (2019-2020) and now engineering education (2024). This evolution underscores her commitment to both technological innovation and pedagogical excellence in mechanical engineering. Her scholarly impact is recognized through competitive awards: ADVANCE at WSU Grantee (2025) Koerner Family Fellowship (2020) GANN Fellowship (2020-2022) Dr. Larsen has successfully obtained research and fellowship funding, including multi-year support from the GANN Fellowship and the upcoming ADVANCE grant. Although specific advisees are not listed, her active research in robotics and education suggests engagement with graduate students. Her work continues to advance both the science of smart materials and the practice of engineering education.

Arik Wilbertمشاهده پروفایل

استادیار

• Geometric Representation Theory
• Combinatorial Representation Theory
• Categorification
• +۲ مورد دیگر

Arik Wilbert is an Assistant Professor in the Department of Math and Statistics at the University of South Alabama, part of the College of Arts and Sciences. His office is located in MSPB 474, and he can be reached at (251) 460-6752 or wilbert@southalabama.edu. Dr. Wilbert obtained his Ph.D. at the University of Bonn and the Max Planck Institute for Mathematics in Bonn under the supervision of Catharina Stroppel in July 2017. Before joining the University of South Alabama in August 2021, he held positions as a Limited Term Assistant Professor at the University of Georgia and as a Research Fellow at the University of Melbourne. Dr. Wilbert's research focuses on the intersection of algebra, geometry, and topology. His primary interests include: Geometric & Combinatorial Representation Theory Categorification Low-Dimensional Topology & Topological Quantum Field Theory His scholarly work demonstrates a consistent focus on Springer theory, quiver varieties, and diagrammatic approaches to representation theory. Dr. Wilbert frequently collaborates with researchers across institutions, with many papers co-authored with mathematicians from various universities. His research shows a progression from foundational work on Springer fibers to more complex structures involving categorical frameworks and topological applications. Dr. Wilbert's research is supported by an AMS-Simons PUI Grant, which enables his continued investigation into geometric and algebraic structures. His teaching portfolio at South Alabama includes advanced courses such as Algebra 1 and 2, Real Analysis 1 and 2, as well as undergraduate calculus sequences. Previously at UGA and Melbourne, he taught specialized graduate courses in representation theory and algebraic geometry. Dr. Wilbert has been actively involved in academic community building, including organizing seminars such as the Graduate Student Learning Seminar on "Cohomology operations and the Steenrod algebra" at the University of Melbourne, where he co-organized and presented on various aspects of algebraic topology.

Jian-Guo Liuمشاهده پروفایل

استاد

• Applied Mathematics
• Partial Differential Equations
• Kinetic Theory
• +۷ مورد دیگر

Jian-Guo Liu is a Professor of Mathematics and Physics at Duke University, with primary affiliations in the Departments of Mathematics and Physics. His research encompasses applied mathematics, partial differential equations, kinetic theory, computational fluid dynamics, and stochastic algorithms. Professor Liu's work bridges theoretical modeling and numerical methods, particularly in complex systems involving nonlinear dynamics, fluid behavior, and emergent phenomena. Research interests focus on multiscale modeling of physical systems, including stochastic processes in chemical reactions, fluid-structure interactions, and materials science. Recent publications demonstrate strong emphasis on mathematical foundations of biological and physical systems, with recurring themes in Fokker-Planck dynamics, mean-field games, tumor growth modeling, and computational methods for interfacial phenomena. Publications showcase consistent focus on analytical and numerical solutions to high-dimensional problems, with applications ranging from medical imaging to electrochemistry. The work exhibits advanced techniques in asymptotic analysis, stochastic approximations, and geometric evolution equations.

Dionissios T. Hristopulosمشاهده پروفایل

استاد

• Geostatistics
• Spatial Random Fields
• Spartan Spatial Random Fields
• +۱۵ مورد دیگر

Dionissios T. Hristopulos is a Professor and Head of the Geostatistics Laboratory at the School of Mineral Resources Engineering, Technical University of Crete, Greece. His research spans geostatistics, spatial random fields, environmental modeling, stochastic hydrology, and porous media mechanics. He has developed innovative Spartan Spatial Random Field (SSRF) models rooted in statistical physics, enabling efficient spatial interpolation and simulation. PhD in Physics, Princeton University (1991) MA in Physics, Princeton University (1988) Diploma in Electrical Engineering, National Technical University of Athens (1985) Hristopulos' research interests focus on the development and application of geostatistical methods in mineral resources, environmental monitoring, petroleum reservoirs, and GIS. He investigates spatial anisotropy, groundwater dynamics, earthquake return times, and the mechanical properties of heterogeneous materials. His work bridges statistical physics and geostatistics, particularly through SSRF models and renormalization group methods for upscaling transport properties in porous media. The most recent publications highlight his focus on geometric anisotropy detection in environmental data, non-parametric estimation methods, and applications of Spartan random fields in environmental time series and spatial data interpolation. His work increasingly integrates machine learning concepts with geostatistical modeling, especially for automatic mapping and ecological monitoring using remote sensing. Marie Curie success story (European Commission, 2010) for SPATSTAT project Hristopulos has secured research funding from national and EU programs, including the Marie Curie Transfer of Knowledge (SPATSTAT) and the INTAMAP STREP project. He has mentored several Master’s and PhD students, including Manos Varouchakis (PhD candidate on groundwater monitoring) and Manolis Petrakis (Master’s on anisotropy characterization). He collaborates with researchers in the US, France, Slovakia, and the UK. He serves on the editorial board of Stochastic Environmental Research and Risk Assessment and has contributed software tools for anisotropy detection in MATLAB and R. His research group, the Geostatistics Laboratory at TUC, focuses on machine learning and geostatistics, developing computational tools for environmental data analysis, spatial interpolation, and simulation. The lab emphasizes practical applications in hydrology, ecology, and mineral resources, supported by strong theoretical foundations in statistical physics and stochastic modeling.

Vladimir Golkovمشاهده پروفایل

پژوهشگر

• Deep Learning
• High-Dimensional Data
• Geometric Data
• +۶ مورد دیگر

Dr. Vladimir Golkov is a Postdoctoral Researcher at the Technical University of Munich (TUM) within the School of Computation, Information and Technology, specifically in Informatics 9 (Computer Vision Group). He works under the supervision of Prof. Dr. Daniel Cremers and maintains an active research profile in deep learning applications for medical imaging and biomedical data analysis. His primary research interests focus on deep learning since 2014, with specialization in high-dimensional and geometric data structures, data-processing goals beyond supervised learning (including clustering and anomaly detection), and applications in biomedicine and physics. Dr. Golkov has successfully mentored students who have gone on to pursue PhD studies at prestigious institutions including TUM, LMU, Mila, ETHZ, Cambridge, and Stanford. Analysis of his recent publications reveals a strong trend toward medical imaging applications, particularly in MRI technology, where he combines deep learning approaches with traditional physics-based methods. His work frequently bridges the gap between theoretical machine learning advances and practical medical applications, with significant contributions to diffusion MRI, sequence optimization, and 3D data processing. He has also expanded into emerging areas including large language models for medical applications and equivariant deep learning architectures. Dr. Golkov receives grant support from the Deutsche Telekom Foundation and maintains an active publication record with numerous contributions to leading conferences including ISMRM, MICCAI, and NeurIPS workshops. His research demonstrates a consistent trajectory of innovation at the intersection of computer vision, deep learning, and biomedical applications. He is actively involved in teaching and mentoring, with students from his projects advancing to top PhD programs worldwide. His office is located at Boltzmannstrasse 3, 85748 Garching, Germany (Office: 02.09.061), and he can be contacted at vladimir.golkov@tum.de.

Hao Xingمشاهده پروفایل

پژوهشگر

• Robot Vision
• Human Action Recognition
• Human-Object Interaction Recognition
• +۴ مورد دیگر

Hao Xing is a scientific researcher and post-doctoral fellow at the Institute for Cognitive Systems (ICS), Technical University of Munich (TUM), working with Prof. Gordon Cheng since May 2024. Previously, he served as a research assistant at the Munich Institute of Robotics and Machine Intelligence (MIRMI) and completed his PhD under Prof. Darius Burschka from 2019. His educational background includes: Master of Science in Mechanical Engineering from Technical University of Munich Bachelor of Engineering in Mechanical Engineering from Hefei University of Technology, China Xing's research focuses on Robot Vision , Human Action Recognition , and Graph Convolutional Networks , with significant contributions to Human-Object Interaction Recognition , Scene Understanding , and Visual Depth Estimation . His work leverages machine learning to develop robust algorithms for human activity analysis and robotic perception, emphasizing spatio-temporal modeling and uncertainty handling in real-world scenarios. Analysis of his 13 recent publications (2019-2025) reveals a dominant focus on graph-based approaches for action recognition and segmentation, with increasing emphasis on open-world applications, uncertainty modeling, and healthcare integration. His research spans computer vision, robotics, and medical applications, demonstrating strong interdisciplinary impact through collaborations in surgical robotics and patient monitoring systems. Xing actively mentors students through master's thesis projects in Stereo Matching, Human Activity Segmentation, and Monocular Depth Estimation, requiring expertise in deep learning frameworks and computer vision algorithms. While no major grants are explicitly mentioned, his thesis topics indicate active research funding in geometric vision and human activity analysis. He operates within the Institute for Cognitive Systems (ICS) at TUM, a key unit in MIRMI's robotics ecosystem focused on advancing human-robot interaction through vision-based scene understanding and motion generation capabilities.

• Statistical Machine Learning
• High-Dimensional Data Analysis
• Computer Vision
• +۳ مورد دیگر

Guy Lebanon joined Purdue University in August 2005 as an Assistant Professor with joint appointments in the Department of Statistics and the School of Electrical and Computer Engineering. His research focuses on statistical machine learning, high-dimensional structured data analysis, and geometric methods in statistics. Education : PhD in Computer Science from Carnegie Mellon University Industry Experience : 1 year at RAFAEL in automatic target recognition for infra-red images Research Interests : Geometry and ranking in statistical machine learning, computer vision

• Mathematical Statistics
• Data Science
• Algebra
• +۳ مورد دیگر

Jukka Mikael Kohonen is a University Lecturer in the Department of Mathematics and Systems Analysis at Aalto University, Finland. He is affiliated with the Mathematical Statistics and Data Science, as well as Algebra and Discrete Mathematics research groups. His research interests span lattice theory and combinatorics, with recent work focusing on modular lattice reduction and enumeration techniques. He has contributed to algorithmic optimization and outlier correlation detection, collaborating across disciplines such as biomedical signal processing and computational mathematics. In his publications since 2017, Kohonen has explored topics ranging from symmetry reduction algorithms to additive number theory, with a notable emphasis on computational methods in discrete mathematics. Recent work (2025) advances techniques for simplifying modular lattices through elimination of irreducible elements. Research groups: Mathematical Statistics and Data Science, Algebra and Discrete Mathematics Email: jukka.kohonen@aalto.fi

Sándor Kisfaludi-Bakمشاهده پروفایل

استادیار

• Algorithms
• Computational Geometry
• Geometric Optimization
• +۴ مورد دیگر

Sándor Kisfaludi-Bak is an Assistant Professor in the Department of Computer Science at Aalto University, specializing in theoretical computer science with a focus on computational geometry. He develops algorithms for geometric problems involving points, curves, shapes, and spatial networks. His research interests include Algorithm design for geometric optimization Computational geometry fundamentals Spatial network analysis Hyperbolic and planar graph algorithms Parameterized complexity in geometric contexts Recent publications demonstrate expertise in Traveling Salesman Problem optimization, Steiner network construction, and hyperbolic graph analysis. Key research trends span computational geometry, graph theory, and algorithmic complexity in spatial domains. He has no listed scientific awards in the provided materials. No information about student advising or organizational affiliations beyond Aalto University was found.