جست‌وجوی استاد و پژوهشگر

Brent Everittمشاهده پروفایل

استاد

• Algebra
• Topology
• Combinatorics
• +۳ مورد دیگر

Brent Everitt is a Professor in the Department of Mathematics at the University of York, where he has been since 1999. He holds a Readership position and has previously held roles such as Senior Lecturer. His academic journey includes undergraduate studies at the University of Auckland, followed by postgraduate degrees at the University of Toronto and the University of Auckland. Before joining York, he held positions at the Universities of Aberdeen, Warwick, and Durham. Everitt’s research focuses on algebra, topology, and combinatorics, with a particular emphasis on homological methods in algebra, partial symmetry through reflection monoids, representation theory of groups and monoids, and geometric manifolds. Collaborators include Paul Turner, John Fountain, and James East. His work on hyperplane arrangements, matroids, and Coxeter groups has led to significant contributions in algebraic topology and geometric group theory. He has supervised eight PhD students, including Majed Albaity and Jim Woodward. His research projects include exploring sheaf homology, minimal hyperbolic manifold volumes, and the interplay between combinatorial structures and algebraic frameworks. He has been involved in grants such as the Heilbronn small grant scheme on diagrammatic intuition and deep learning in mathematics. Everitt is actively engaged in academic outreach, delivering invited talks globally on topics like reflection monoids, Khovanov homology, and hyperbolic manifolds. His teaching contributions include courses on Galois theory and algebraic topology, reflecting his dedication to both research and education.

• Self-Supervised Learning
• Unsupervised Representation Learning
• Computer Vision
• +۱۳ مورد دیگر

Aiden Durrant is a Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen, actively contributing to research and teaching in computing science. He is affiliated with the South China Normal University Joint Institute and is accepting PhD students in Computing Science. His research spans machine learning, computer vision, and AI, with a focus on self-supervised and geometric representation learning. BSc (Hons) and MPhil in Computer Science, University of Lincoln PhD in Computing Science, University of Aberdeen Aiden Durrant’s research centers on self-supervised learning and the geometric structuring of learned representations. He investigates how to learn high-quality image representations without human-annotated labels, focusing on preserving semantic and hierarchical knowledge through hyperbolic and hyperspherical geometries. His work extends to multi-modal learning and has practical applications in environmental monitoring, agriculture, nuclear reactor safety, and climate change. He has developed novel architectures such as HMSN (Hyperbolic Self-Supervised Learning) and S-JEA (Stacked Joint Embedding Architectures) to improve representation quality. His recent publications (2023–2025) demonstrate a strong trend in foundation models, self-supervised learning, and deep learning for environmental and industrial applications. Key themes include crevasse mapping in glaciology, segment anything models for drone imagery, and hyperbolic clustering for semantic hierarchies. His earlier work (2019–2022) focused on nuclear reactor anomaly detection using deep learning and time-series analysis, contributing to the CORTEX Horizon 2020 project. Scientific Awards: PhD Poster Award, FISA - 9th European Commission conference on Euratom research and training in safety of reactor systems (2019) Durrant has secured competitive funding, including a 2024 SUSTAIN CDT Studentship (PI) and an Aberdeen Internal Pump-Priming grant (Co-PI), supporting research in AI for glacier mapping and sustainable crop breeding. He supervises PhD students and has co-led teaching modules in Machine Learning, Data Mining, and Research Methods at both undergraduate and postgraduate levels. He also contributes nationally as Deputy Director of the SICSA Graduate Academy, supporting research training across all 14 Scottish computer science departments. He is actively involved in interdisciplinary collaborations, particularly in agri-food data sharing, environmental sustainability, and healthcare AI, often working with cross-institutional teams. His external memberships include BMVA and leadership in organizing the BMVA 2025 Summer School.

Arnab Kumar-Mondalمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• Dynamical Systems
• +۹ مورد دیگر

Arnab Kumar-Mondal is a Machine Learning Researcher at Apple Inc., with a Ph.D. in Deep Learning from McGill University and Mila – Quebec Artificial Intelligence Institute. His work bridges theoretical and applied research in computer vision, language modeling, robotics, and AI for science. Ph.D. from McGill University (2025 completion) Internships at Microsoft Research and Apple Visiting Researcher at ServiceNow Research and Huawei Noah’s Ark Lab B.Tech in Electronics and Electrical Engineering from IIT Kharagpur His research focuses on equivariant learning , state space modeling , and generative adversarial networks (GANs) , with applications in medical imaging, human motion analysis, and vector graphics generation. Key contributions include canonicalization frameworks for symmetry-aware modeling and spectral analysis of representation quality in self-supervised learning. Collaborations span institutions like ServiceNow, Huawei, and Mila. Recent publications (2023–2025) explore symmetry-aware generative modeling , efficient dynamics modeling in interactive environments, and rotation-invariant visual representation learning. His work on ternary language models at ICLR 2025 demonstrates scalable pretraining techniques. Arnab maintains active contributions to open-source software, including PyTorch implementations for semi-supervised segmentation via CycleGAN. His technical depth extends to VLSI engineering, embedded systems, and free-form lens design from undergraduate research. Professional activities include patents on video-language foundation models, internships at leading tech firms, and cross-institutional research roles.

Erik Bekkersمشاهده پروفایل

دانشیار

• Geometric Machine Learning
• Deep Learning
• Medical Image Analysis
• +۴ مورد دیگر

Erik Bekkers is an Associate Professor at the University of Amsterdam's Informatics Institute, leading research in the Machine Learning Lab (AMLab). His work bridges geometric mathematics and machine learning, focusing on developing robust and efficient deep learning architectures grounded in symmetry, equivariance, and physical principles. Education: PhD in Biomedical Engineering (cum laude) from Eindhoven University of Technology Previous Roles: Postdoctoral researcher in applied differential geometry at TU/e Department of Applied Mathematics His research spans: Group convolutional neural networks Symmetry-preserving representation learning Generative modeling on manifolds Physics-informed neural networks Medical imaging applications Recent publications emphasize geometric latent variable models, equivariant diffusion methods, and applications to molecular generation, medical imaging, and physics-driven AI. His team actively explores structure-preserving and self-supervised learning techniques. Scientific Awards MICCAI Young Scientist Award (2018) Philips Impact Award (MIDL 2018) NWO VENI grant: Context-Aware AI in Medical Imaging (2023) NWO VIDI grant: Neural Ideograms - Geometry-Grounded AI (2024) As co-founder of the ICML'24 GRaM workshop , he promotes geometry-grounded approaches in AI. His lab actively investigates geometric regularization, manifold-based PDE forecasting, and symmetry-aware generative methods.

Pauli Miettinenمشاهده پروفایل

استاد

• Data Science
• Data Mining
• Matrix and Tensor Decompositions
• +۴ مورد دیگر

Pauli Miettinen is a Professor of Data Science at the University of Eastern Finland, affiliated with the School of Computing within the Faculty of Science, Forestry and Technology. His research focuses on data science methodologies, including matrix and tensor decompositions, redescription mining, and social network analysis. Key applications span ecological niche modeling, health data analysis, and parliamentary candidate opinion analysis. He leads the Algorithmic Data Analysis research group and contributed to the Neuro-Innovation project (2021–2026). Recent work includes advancements in differentially private redescription mining and hyperbolic community graph generation. His publications emphasize efficient algorithms for data mining tasks like biclustering and non-negative matrix factorization. Selected achievements include developing the HyGen graph generator and pioneering techniques for interpretable data representation. His research bridges theoretical method development with practical applications in diverse domains.

Tejaswi Kasarlaمشاهده پروفایل

پژوهشگر

• Deep Learning
• Computer Vision
• Multimodal Learning
• +۴ مورد دیگر

Tejaswi Kasarla is a Researcher at the VIS Lab, University of Amsterdam, and a Research Scientist Intern at Meta FAIR in Paris. Their work intersects non-Euclidean representation learning (hyperspherical and hyperbolic deep learning) for open-world understanding and multimodal foundation models. Education : Fourth-year PhD candidate at University of Amsterdam (since Oct 2021). Research Experience : Intern at Bosch (Jun 2018–Oct 2018), then Computer Vision Researcher at Bosch (May 2019–Jun 2022). Currently organizing community initiatives like the Women in Computer Vision (WiCV) Workshop at CVPR 2021–2022. Teaching : Teaching Assistant for Applied Machine Learning (Nov 2022, Nov 2021). Research Focus : Non-Euclidean deep learning (hyperspherical/hyperbolic), multimodal foundation models, active learning, and uncertainty quantification in computer vision. Recent work explores hyperbolic safety-aware vision-language models (CVPR 2025 Highlight) and lightweight uncertainty quantification for terrain traversability (ICRA 2024). Publications : Their research appears in top venues like CVPR, NeurIPS, WACV, and workshops (ECCV Beyond Euclidean, ICRA Resilient Off-road Autonomy). Key themes include class separation , active learning , and hyperbolic embeddings . Community Contributions : Organized WiCV Workshops at CVPR 2021–2022, served as a board member since 2022. Peer-reviewed for ICCV, NeurIPS, ICLR, and WiCV/NeurIPS workshops. Interests Beyond Research : Photography and specialty coffee (home barista).

Alvaro Gonzalez-Jimenezمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Medical Imaging
• +۳ مورد دیگر

Alvaro Gonzalez-Jimenez is a Post-Doc Researcher at University Hospital of Basel and a Research Associate at the Applied Artificial Intelligence (AAI) Research Lab at Lucerne University of Applied Sciences and Arts (HSLU). He completed his PhD in Biomedical Engineering at the University of Basel in 2025 with Summa Cum Laude distinction. His academic journey includes a Master's degree in Computer Science from Grenoble Institute of Technology and a Bachelor's degree from the University of Sevilla. His educational background includes: 2021-2025: PhD in Biomedical Engineering, University of Basel ("Learning Dermatology with Noisy and Limited Data") 2019-2020: MSc in Computer Science, Grenoble Institute of Technology 2014-2015: BSc in Computer Science, Budapest University of Technology and Economics (ERASMUS) 2013-2018: BSc in Computer Science, University of Sevilla Gonzalez-Jimenez's research focuses on the intersection of machine learning and dermatology, with particular emphasis on developing methods that can learn effectively from noisy and limited medical data. His work spans several key areas including: Robustness in AI systems for medical applications Hyperbolic geometry for medical anomaly detection Medical image segmentation with noisy labels Dermatology-focused AI applications His recent publications demonstrate a strong trend toward addressing critical challenges in medical AI, particularly in dermatology. His work frequently explores innovative approaches to handle limited and noisy data in medical contexts, with a growing emphasis on geometric deep learning and robust optimization techniques. His research has significant implications for making AI-assisted dermatological diagnosis more accessible and reliable, especially in resource-limited settings. His scientific achievements include: Best Paper Award at MICCAI 2023 Early Acceptance (Top 9%) at MICCAI 2025 for "Is Hyperbolic Space All You Need for Medical Anomaly Detection?" Early Acceptance (Top 11%) at MICCAI 2024 for "PASSION for Dermatology" Gonzalez-Jimenez is actively involved in several research projects including PASSION (focusing on dermatology in Sub-Saharan Africa), SelfClean (for data cleaning), and T-Loss (for robust medical image segmentation). He co-organizes the first Hyperbolic Learning for Medical Imaging (HyperMI) tutorial at MICCAI 2025, reflecting his leadership in emerging AI methodologies for medical applications.

• Artificial Intelligence
• Medical Imaging
• Data Analysis
• +۳ مورد دیگر

Simone Lionetti is a Lecturer and Co-Head (acting) of the Applied AI Research Lab at the Lucerne School of Computer Science and Information Technology, Lucerne University of Applied Sciences and Arts. His academic journey includes a Ph.D. in Physics from ETH Zürich (2018), a Master's in Physics from Università degli Studi di Milano (2013), and a Bachelor's in Physics from the same institution (2010). He has held postdoctoral and research roles at institutions such as Durham University and ETH Zürich. His research focuses on AI applications in healthcare, including medical anomaly detection, dermatology, and bioaerosol monitoring. He also explores theoretical particle physics and software engineering for scientific computing. Key competencies include data analysis, algorithm design, and self-supervised learning techniques. Recent work emphasizes robust AI systems for medical imaging, data quality audits, and scalable foundation models. His projects bridge technical innovation with societal needs, such as addressing diversity gaps in dermatological AI and improving real-time environmental monitoring. He has contributed to over 20 peer-reviewed articles and holds two notable awards/honors (specific details not disclosed in text). His work integrates cross-disciplinary approaches, combining theoretical physics insights with practical AI solutions for healthcare and environmental challenges.

Stephen Kobourovمشاهده پروفایل

استاد

• Algorithm Design
• Computational Geometry
• Graph Drawing
• +۳ مورد دیگر

Prof. Stephen Kobourov is a Professor in the TUM School of Computation, Information and Technology at Technische Universität München (TUM). Previously, he held positions at the University of Arizona from 2000 to 2024, progressing from Assistant Professor to Full Professor, and served as Deputy Director of the Data Science Institute. His expertise lies in algorithm design, computational geometry, and graph visualization, with over 250 publications and significant funding from NSF, ONR, and USDA grants. Education: BS in Computer Science and Mathematics from Dartmouth College (1995), PhD in Computer Science from Johns Hopkins University (2000). Research focuses on efficient algorithms for graph drawing, visualization techniques for networks, and geometric algorithms. Notable contributions include work on stress metrics in graph layouts, hypergraph representations, and non-Euclidean visualization frameworks. He has co-chaired committees for ALENEX, IEEE PacificVis, and GD, and serves as an editor for JGAA, CGTA, and IEEE TVCG. Awards include the Fulbright Distinguished Chair (2015–2016), Humboldt Research Fellowship (2011–2014), and NSF Career Award (2006–2011). His recent work explores multi-layer network visualization, AI-driven graph analysis (e.g., MLLMs perception), and graph embeddings in non-Euclidean spaces. Advising and grants: Extensive grants from NSF and other agencies support his research. He has advised numerous students and collaborates internationally, contributing to labs like the Data Science Institute and initiatives such as the Institutional Knowledge Map (KMAP). Visualization projects include MetroSets (metro map-style set visualization), Wooly Graphs (knitting pattern frameworks), and tools for interactive network analysis. His work bridges theoretical algorithms with practical applications in science and technology.

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۲ مورد دیگر

Chun-Liang Li is a Research Scientist at Apple MLR and an Affiliate Assistant Professor at the Allen School of Computer Science, University of Washington. He holds a Ph.D. in Machine Learning from Carnegie Mellon University (2014-2019) and a B.S./M.S. in Computer Science and Information Engineering from National Taiwan University (2008-2013). His research spans machine learning, computer vision, and natural language processing, with emphasis on representation learning, efficient model training, and multimodal understanding. Key focus areas include large language model optimization, contrastive learning techniques, document understanding systems, and domain adaptation methods. Recent publications demonstrate strong focus on LLM efficiency (dataset decomposition, distillation techniques), document AI (table reasoning, form extraction), and multimodal alignment (image-text retrieval, prefix conditioning). His work consistently appears in premier venues including NeurIPS, CVPR, ACL, and ICLR. Awards and Honors: IBM Ph.D. Fellowship (2018) Best student paper runner-up, IJCAI (2017) First Place in both tracks of KDD Cup (2011, 2013) He actively mentors students and collaborators, inviting contact for internship opportunities. Current industry-academic position bridges cutting-edge research with practical applications in machine learning systems.

• Geometric Topology
• Low-dimensional Manifolds
• Algebraic Geometry
• +۴ مورد دیگر

Stephan Tillmann is a Professor of Geometric Topology at the University of Sydney , where he leads research in low-dimensional manifolds, geometric and algebraic topology, and computational methods. His work spans hyperbolic geometry, tropical geometry, and group theory, with applications to 3-manifolds and algorithmic topology. Education : PhD (University of Melbourne, 2002), BSc (First Class Honours, University of Melbourne, 1997) His research focuses on understanding the interplay between triangulations, geometric structures, and surfaces in 3-manifolds. Recent work includes algorithmic classifications of manifolds and geometric invariants like the Thurston norm. A key theme is the application of tropical and projective geometry to topological problems. The 15 most recent articles (2021–2025) address computational topology, character varieties, and invariants of 3-manifolds. Keywords include Geometric Topology , Hyperbolic Geometry , Normal Surface Theory , and Algorithmic Methods . Scientific Awards : Bessel Research Award (2021), Teaching and Learning Award (2015), B.H. Neumann Prize (2001) He supervises PhD students William HOBKIRK and James MORGAN, contributes to grants (e.g., ARC Discovery and Future Fellowships), and organizes major conferences like the Geometry and topology in low dimensions workshop. As Executive Director of the Sydney Mathematical Research Institute (2022–2025), he advances mathematical research infrastructure.

Antonio Calà Lesinaمشاهده پروفایل

استاد

• Computational Electromagnetics
• Nanophotonics
• Metamaterials
• +۷ مورد دیگر

Antonio Calà Lesina is a Full Professor in Computational Photonics at the Leibniz University Hannover , leading research at the Hannover Centre for Optical Technologies . His academic journey includes a PhD in Information and Communication Technology from the University of Trento (2013), followed by postdoctoral and associate professor roles at the University of Ottawa (2013–2020). His research spans Nanophotonics , Metamaterials , and Plasmonics , focusing on inverse design, topology optimization, and dynamic control of optical systems via multiphysics simulations. Education : PhD (University of Trento, 2013); MSc in Telecommunications Engineering (University of Catania); BSc in Electronics Engineering (University of Catania) Current Position : Full Professor (Leibniz University Hannover, 2020–2025) His work explores Light-Matter Interactions and Nanostructures , with recent publications on anapole states , multiresonant metasurfaces , and hyperbolic metamaterials . He employs Finite-Difference Time-Domain (FDTD) simulations and deep learning for inverse design in nanophotonics. Collaborations include institutions like the University of Ottawa and research centers in Europe, aligning with the EULiST alliance . Despite no explicit awards mentioned, his contributions to optical beam steering , nonlinear metasurfaces , and machine learning integration highlight his impact in the field.

Marcel Campenمشاهده پروفایل

استاد

• Computer Graphics
• Geometry Processing
• Surface Parametrization
• +۴ مورد دیگر

Marcel Campen is a Professor at Osnabrück University specializing in Computer Graphics and Geometry Processing. His research focuses on surface parametrization, quad mesh generation, and computational geometry. He has made significant contributions to the field of geometry processing, particularly in developing algorithms for quad layout generation, surface mapping, and mesh repair. His research interests span Computer Graphics, Geometry Processing, Surface Parametrization, Quad Mesh Generation, 3D Modeling, and Mesh Repair. Campen's work addresses fundamental challenges in representing and processing complex geometric shapes, with applications ranging from animation and simulation to reverse engineering and meshing. His research often combines theoretical insights with practical implementations, resulting in algorithms that are both mathematically sound and computationally efficient. Campen's publications demonstrate a strong focus on developing robust and efficient methods for geometry processing. His work on quad layout generation, parametrization techniques, and surface mapping has resulted in several award-winning papers, including Best Paper Awards at SGP 2021 and 2022. His research often bridges theoretical concepts with practical implementations, making his contributions highly influential in both academic and industrial settings. Best Paper Award (1st place) at SGP 2022 Best Paper Award at SGP 2021 Campen has made significant contributions to the field through his doctoral thesis on quad layout generation and numerous publications in top-tier conferences including SIGGRAPH, Eurographics, and SGP. His work on directional field synthesis, similarity maps, and bijective mappings has advanced the state of the art in geometry processing. He has also contributed to practical tools like libQEx for robust quad mesh extraction, demonstrating his commitment to making theoretical advances accessible to practitioners.

Armin Iskeمشاهده پروفایل

استاد

• Numerical Approximation
• Applied Mathematics
• Kernel Methods
• +۴ مورد دیگر

Prof. Dr. Armin Iske is a Full Professor of Numerical Approximation at the University of Hamburg's Department of Mathematics, within the Faculty of Mathematics, Computer Science and Natural Sciences. He holds a PhD from the University of Göttingen (1994) and habilitation from TU Munich (2002). His research focuses on numerical approximation, kernel-based methods, computational fluid dynamics, and medical imaging. He has held academic positions globally, including visiting roles at ANU (Australia) and the University of Leicester (UK). Research interests include scattered data approximation, adaptive particle methods for flow simulation, and high-dimensional data analysis. He has authored 118+ publications, including works on kernel interpolation, medical imaging reconstruction, and machine learning applications. He serves on editorial boards for journals like Advances in Computational Mathematics and Sampling Theory . His contributions span interdisciplinary projects, such as SFB/TRR 181 on energy transfer in atmosphere and ocean, and collaborations in nanotechnology for brain interfaces. His work bridges theoretical mathematics with practical applications in engineering and biosciences.

Zhiguang Caoمشاهده پروفایل

استادیار

• Artificial Intelligence
• Data Science
• Decision Making & Optimization
• +۳ مورد دیگر

Dr. Zhiguang Cao is an Assistant Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). With a PhD from Nanyang Technological University (2017), his work bridges Artificial Intelligence , Deep Learning , and Combinatorial Optimization to tackle complex logistics and urban mobility challenges. Research Advisor to PhD/Master's students: XIAO Xiangjie, YI Hang, ZHANG Ni Recipient of the World’s Top 2% Scientist recognition (2024), IEEE Transactions on Industrial Informatics Outstanding Paper Award (2022), and Nobert Wiener Review Award (2022) Completed degrees: PhD (NTU), MSc (NTU), BEng (GDUT, China) Research Interests focus on Learning to Optimize (L2Opt) and Neural Combinatorial Optimization , with applications in: Vehicle Routing Problems (TSP, CVRP, PDP) Job Shop Scheduling and Multimodal Optimization UAV Routing , Drone Delivery , and Stochastic Programming His 15 most recent publications (2024-2025) span top venues like NeurIPS , ICML , IJCAI , and IEEE Transactions journals, covering topics from multi-task vehicle routing to graph domain adaptation and learning with large language models . Scientific Awards : World’s Top 2% Scientist (Stanford, 2024) IEEE Transactions on Industrial Informatics Outstanding Paper (2022) Nobert Wiener Review Award (2022) BMW Summer School Excellent Research Work (2014) IEEE Singapore Chapter Tennis Prize (2012) Dr. Cao advises the L2Opt Research Group , which recruits PhD students and remote interns. He has served as Area Chair for ICLR, SIGKDD, ICML, and Senior PC for AAAI and IJCAI. His ongoing projects include Trustworthy Human-AI Optimization (AISG, 2025-2027) and Mobile Crowdsourcing Emergency Supplies (SMU-UofT, 2025-2026).