جست‌وجوی استاد و پژوهشگر

Stanica Lavinia Justinaمشاهده پروفایل

مدرس

• Software programming
• e-Learning
• Artificial Intelligence
• +۱۰ مورد دیگر

Lavinia Stănică Justina is a university lecturer at the Faculty of Managerial Informatics, Romanian-American University (URA), with 18 years of teaching experience in software programming and e-learning domains. Holding a PhD in Cybernetics and Economic Statistics since 2011, she teaches courses in Romanian and English at both bachelor's and master's degree programs. Her research focuses on ICT's role in innovation, educational technologies, and data analysis for energy consumption, with over 30 publications and multiple coordinated research projects. Faculty: Managerial Informatics University: Romanian-American University Academic Rank: Lecturer Teaching Experience: 18 years PhD: Cybernetics and Economic Statistics (2011) Her research interests include: e-Learning systems and technology integration Artificial Intelligence applications in education ICT's impact on innovation performance Mathematical content modeling Energy consumption data analysis Internet of Things implementations Fractal techniques in imaging Recent publications highlight trends in: Comparative innovation-ICT readiness analysis AI education accessibility Smart energy systems Competitive programming metrics Neural network energy forecasting IoT cloud solutions Modular educational software As Erasmus coordinator since 2013 and Institutional Erasmus Coordinator since 2020, she actively promotes internationalization efforts and academic collaboration with partner universities across Europe.

• Spectral Shape Analysis
• 3D Shape Matching
• Geometric Deep Learning
• +۳ مورد دیگر

Riccardo Marin is a Postdoctoral Researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Computer Vision Group . His research focuses on Spectral Shape Analysis , 3D Shape Matching , Geometric Deep Learning , and Virtual Humans . PhD from University of Verona Postdoctoral experience at GLADIA (Sapienza University of Rome) and Tuebingen University's AI Center His work bridges geometric modeling and deep learning, with notable contributions to neural surface fields, diffusion-based avatar creation, and scalable 3D human registration. He has authored publications in top venues like CVPR, ECCV, and NeurIPS, with a focus on geometric consistency and implicit representations in 3D vision. Scientific awards : Humboldt Research Fellowship Marie-Curie Postdoctoral Fellowship ELLIS Membership His research often involves collaboration with institutions such as MPI-INF and Tuebingen AI Center, with GitHub repositories like NICP, Diff-FMAPs-PyTorch, and FARM demonstrating his technical contributions in spectral analysis and 3D reconstruction.

Ayush Bhartiمشاهده پروفایل

پژوهشگر ارشد

• Probabilistic Machine Learning
• Bayesian Statistics
• Simulation-based Inference
• +۳ مورد دیگر

Ayush Bharti is an Academy Research Fellow (Research Fellow) in the Department of Computer Science at Aalto University, Finland. His primary affiliations include the Probabilistic Machine Learning research group and the Academy Professorship led by Samuel Kaski . His research centers on probabilistic machine learning with dual focus areas: advancing simulation-based inference methodologies and applying statistical techniques to stochastic radio channel modeling. Key interests include Bayesian statistics, robustness under model misspecification, handling missing data, and developing efficient approximate Bayesian computation frameworks. His work bridges theoretical machine learning with practical wireless communication challenges. Analysis of his 15 most recent publications (2021-2025) reveals a consistent trajectory in simulation-based inference innovation, featuring neural processes, diffusion models, and multilevel architectures. Approximately 60% of his work targets general statistical challenges (e.g., robustness, cost-awareness), while 40% addresses radio channel modeling applications. Notable trends include integration of domain expertise in inference loops and development of context-aware optimization techniques. No information is available regarding student supervision or research grants. Bharti operates within Aalto University's Probabilistic Machine Learning ecosystem, contributing to the Academy Professorship project under Samuel Kaski. This collaborative environment focuses on developing scalable inference algorithms for complex real-world systems, with particular emphasis on uncertainty quantification in simulation-driven scientific discovery.

Lauren E. Gillespieمشاهده پروفایل

استادیار

• Machine Learning
• Global Ecology
• Genomics
• +۵ مورد دیگر

Dr. Lauren E. Gillespie is an Assistant Professor in the Department of Geospatial Data Sciences at the University of Michigan, specializing in AI-integrated approaches for large-scale ecosystem monitoring. With a background in computer science and biology, her research focuses on developing foundation models to analyze noisy environmental data from remote sensing and citizen science platforms, aiming to enhance ecological forecasting under rapid climate change. PhD : Computer Science, Stanford University BS : Computer Science & Chemistry, Southwestern University Her work bridges machine learning , global ecology , and remote sensing , with recent publications on biodiversity mapping using deep learning, domain adaptation in citizen science datasets, and genetic diversity loss in the Anthropocene. She actively explores ethical frameworks for scalable environmental AI applications. Key scientific awards include the Best Paper Award at AAAI 2025, Fulbright Program support (2024), and NSF Graduate Research Fellowship (2019). Her projects have received grants such as the Climate Change AI Innovation Grant for ForestBench (2022) and the Ethics, Society and Technology Hub grant for Scenes from the Anthropocene (2022).

• Numerical Analysis
• Applied Mathematics
• Scientific Computing
• +۴ مورد دیگر

Virginie Ehrlacher is a Professor at CERMICS, École des Ponts ParisTech (ENPC), France. She specializes in applied mathematics with a focus on high-dimensional problems, numerical analysis, and computational modeling. Her work bridges quantum chemistry, materials science, and machine learning through innovative mathematical frameworks. Education includes: PhD in Mathematics (2012) from ENPC: Mathematical models in quantum chemistry and uncertainty quantification Habilitation (2020) from Université Paris-Dauphine: Mathematical and numerical analysis of high-dimensional and multiscale problems in materials science Research spans multiscale modeling, tensor decompositions for high-dimensional systems, cross-diffusion equations, and scientific machine learning. Her work frequently addresses challenges in quantum mechanics, materials science, and computational physics using advanced numerical techniques. Publications emphasize: Algorithms for high-dimensional PDEs and eigenvalue problems Model reduction techniques (tensor networks, reduced basis methods) Cross-diffusion systems with biological/physical applications Neural networks for scientific computing Awards and distinctions: Irène Joliot-Curie Prize (2023) Chevalier de l’Ordre National du Mérite (2025) Leadership includes: ERC Starting Grant HighLEAP (2023–2028) ERC Synergy project EMC2 (2020–2026) ANR JCJC project COMODO (2019–2023) She co-leads the EMS Topical Activity Group on Scientific Machine Learning. Affiliated with the CERMICS laboratory, she collaborates on interdisciplinary teams tackling multiscale and data-driven modeling challenges.

• Neuro-oncology
• Minimally Invasive Surgery
• Awake Brain Surgery
• +۶ مورد دیگر

Puneet Plaha is an Associate Professor and Consultant Neuro-Oncology Surgeon at the University of Oxford, based at the John Radcliffe Oxford University Hospitals since August 2012. He serves as Lead of the Oxford Neuro-oncology Multidisciplinary Team (MDT) and Lead for the Thames Valley Cancer Network for Brain Tumours, while chairing the Neuro-oncology section of the Society of British Neurological Surgeons. His educational qualifications include MBBS, MS, MD, FRCS, and FRCS (SN), reflecting extensive surgical and neurological training. Plaha's research centers on minimally invasive endoscopic techniques for brain tumor resection and advanced brain imaging to develop individualized treatments. He pioneered Oxford's Awake Surgery Brain Tumor Programme using intraoperative stimulation for supramaximal tumor resection. His work investigates neural network interactions with tumors, novel surgical modalities for safer procedures, immunotherapy applications, and clinical trials for brain cancer. Analysis of his 15 most recent publications (2023-2004) reveals dominant themes in glioblastoma treatment optimization, awake surgery refinement, and diffusion tractography applications. Key trends include immunotherapy integration (Ipi-Glio trial), pandemic impact on neuro-oncology care, and ultra-high-definition intraoperative imaging for low-grade glioma resection. As Chief Investigator for the UK NIHR-funded FUTURE-GB trial, Plaha leads multi-center glioblastoma research. His group also develops fast-track recovery protocols after minimally invasive tumor surgery and investigates neural network preservation during resection. No formal advisees are listed in available materials. He directs the Oxford Surgical Neuro-Oncology Research Group, which integrates clinical practice with translational research on tumor-immune interactions and surgical innovation, maintaining close collaboration with the Thames Valley Cancer Network for brain tumor management.

René Vidalمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Biomedical Data Science
• +۳ مورد دیگر

René Vidal is the Rachleff & Penn Integrates Knowledge (PIK) University Professor at the University of Pennsylvania, with appointments in the Departments of Electrical and Systems Engineering, Radiology, Computer and Information Science, and Statistics and Data Science. He also serves as Director of the Center for Innovation in Data Engineering and Science (IDEAS) and the NSF-Simons Collaboration on the Mathematical Foundations of Deep Learning (THEORINET). A dual faculty member at Johns Hopkins University in Biomedical Engineering, Computer Science, and other departments, Vidal is an Amazon Scholar and Affiliated Chief Scientist at NORCE. PhD, Electrical Engineering and Computer Sciences, University of California, Berkeley (2003) M.S., Electrical Engineering, University of California, Berkeley (2000) B.S. (valedictorian), Electrical Engineering, Pontificia Universidad Catolica de Chile (1997) His research spans the mathematical foundations of deep learning, focusing on non-convex optimization , learning dynamics , and overparametrization . Key contributions include Sparse Subspace Clustering , Kernel GPCA , and Low-Rank Matrix Factorization , with applications in autism diagnosis , robotic surgery , and cardiac imaging . Recent work explores continual learning , adversarial robustness , and trustworthy AI in biomedical contexts. His 15 most recent publications highlight advances in medical imaging , language models , and robust computer vision , spanning topics from chest X-ray analysis to motor imitation tasks in autism . Articles like Geometric Analysis of Nonlinear Manifold Clustering underscore his theoretical contributions, while works on KDA: Knowledge-Distilled Attacker and Conformal Information Pursuit address practical AI safety and interpretability. Scientific accolades include: 2022 ACM Fellow 2021 IEEE McCluskey Technical Achievement Award 2017 Jean D’Alembert Fellowship 2012 J.K. Aggarwal Prize 2009 Sloan Research Fellow 2005 NSF CAREER Award His lab mentors 11 current PhD students across Johns Hopkins and University of Pennsylvania , with alumni contributing to institutions like Meta , Amazon , and GE Research . Vidal’s interdisciplinary work bridges mathematics , engineering , and healthcare , supported by grants from the DoD , NSF , and ONR .

Nicolas Duchateauمشاهده پروفایل

دانشیار

• Medical Imaging
• Cardiac Function Analysis
• Statistical Atlases
• +۵ مورد دیگر

Nicolas Duchateau is an Associate Professor at Université Lyon 1 and researcher at the CREATIS lab in Lyon, France. He is also a Junior member of the prestigious Institut Universitaire de France (IUF) and serves as Associate Editor for the Neurocomputing journal. His academic career includes positions at Universitat Pompeu Fabra in Barcelona and INRIA Epione in Sophia-Antipolis, with his current role at Polytech Lyon's Biomedical Engineering department since 2016. His research focuses on characterizing diseases from medical imaging populations, with methodological development centered on statistical atlases and machine learning approaches to represent populations. On the applicative side, he concentrates on cardiac function and imaging modalities such as echocardiography and magnetic resonance. His work spans computational anatomy, pattern statistics, representation learning, manifold learning, auto-encoders, information fusion, cardiac imaging, shape and deformation analysis, risk stratification, and image synthesis. Duchateau's recent publications reveal strong trends in multimodal data fusion, particularly combining echocardiography with clinical records for patient stratification. He has made significant contributions to representation learning for cardiac population analysis, with increasing emphasis on diffusion models, uncertainty estimation, and domain adaptation techniques. His work bridges deep learning methodologies with clinical cardiology applications, focusing on interpretable AI solutions for cardiac function assessment. Junior Member of Institut Universitaire de France (2021) PhD prize for knowledge transfer from Universitat Pompeu Fabra (2014) Young Investigator Award at Euroecho conference (2010) Duchateau actively supervises numerous PhD and Master's students, including Anita Salvador, Thierry Judge, Pierre-Elliott Thiboud, and Romain Deleat-Besson. He has secured major research funding including a €75k grant from the Institut Universitaire de France (2021-2026), a €251k French ANR Young Researchers grant for the "MIC-MAC" project (2019-2024), and a €139k grant from the Fédération Française de Cardiologie for the "MI-MIX" project (2020-2023). He leads a vibrant research team at CREATIS lab focused on medical image analysis, where his group develops computational approaches to characterize cardiac diseases through population analysis. The team works at the intersection of machine learning, medical physics, and clinical cardiology, with ongoing projects spanning echocardiography analysis, MRI processing, synthetic data generation, and clinical decision support systems.

• Computer Vision
• Pattern Recognition
• Machine Learning
• +۷ مورد دیگر

Prof. Dr.-Ing. Joachim Denzler is a Professor leading the Chair for Digital Image Processing (Lehrstuhl für Digitale Bildverarbeitung) at Friedrich-Schiller-Universität Jena. His research spans computer vision, machine learning, medical imaging, and remote sensing applications across diverse domains including biomedical analysis, environmental monitoring, and facial recognition systems. His research interests focus on explainable AI, causal discovery, physics-informed neural networks, and bias mitigation in deep learning models. He has made significant contributions to fine-grained classification, concept activation vectors, and privacy-preserving techniques in facial recognition. His work often bridges theoretical computer vision with practical applications in medical diagnostics, environmental science, and infrastructure monitoring. His recent publications demonstrate a strong trend toward causal discovery methods, physics-informed neural networks, and addressing bias in machine learning systems. His work spans both theoretical advancements in model interpretability and practical applications in medical imaging, environmental monitoring, and infrastructure safety. The interdisciplinary nature of his research is evident from collaborations with ecologists, neuroscientists, and civil engineers. Best Paper Award at International Conference on Pattern Recognition (ICPR) 2024 Prof. Denzler actively mentors numerous PhD students and postdoctoral researchers, with frequent co-authors including Maha Shadaydeh, Niklas Penzel, Gideon Stein, and Tim Büchner appearing as first authors on significant publications. His laboratory has secured research funding across multiple domains including medical imaging, environmental monitoring, and AI safety. His work on CausalRivers represents a major contribution to benchmarking causal discovery methods with real-world time series data. The Chair for Digital Image Processing maintains strong industry and clinical partnerships, particularly in medical imaging applications for facial palsy analysis and neonatal care. Prof. Denzler's team has developed novel techniques for dam deformation monitoring using remote sensing data and created advanced methods for analyzing plant diversity effects on ecosystem functioning.

Jie Songمشاهده پروفایل

پژوهشگر

• Deep Learning
• Computer Vision
• Human Pose Estimation
• +۴ مورد دیگر

Jie Song is a postdoctoral researcher at ETH Zurich affiliated with the Advanced Interactive Technologies lab. Their work bridges structured information and deep learning pipelines, with applications in hand/body-pose estimation, 3D human reconstruction, and view synthesis. Research Interests: Deep Learning, Computer Vision, 3D Reconstruction, Human Pose Estimation, Motion Capture, 6D Pose Estimation Affiliation: ETH Zurich, Advanced Interactive Technologies lab Jie's recent publications (2023-2025) focus on monocular video-based 3D human modeling, Gaussian rendering, and motion synthesis. Collaborations span institutions like ETH Zurich and MPI Tuebingen, with applications in robotics, augmented reality, and sports analytics. Scientific Awards: 3DV Best Paper Award (2017), Qualcomm Innovation Fellowship Finalist (2015), Swisscom Innovation Award (2014), Birkigt Scholarship (2013), National Scholarship (2009/2010) Jie has supervised multiple student projects, including personalized neural avatars and skeleton-based motion modeling. They serve as a Teaching Assistant for courses like Visual Computing and Machine Perception at ETH Zurich.

• Magnetic Resonance Imaging
• Diffusion Neuroscience
• Functional Magnetic Resonance
• +۵ مورد دیگر

Professor Martin Koch is a distinguished academic at the Institute of Medical Engineering at the University of Lübeck, where he serves as Head of Magnetic Resonance Imaging. Born in Dinslaken, Germany in 1968, he completed his physics education at Göttingen University, including studies at the University of Edinburgh, followed by a PhD at the University of Leipzig. After over a decade as a Research Associate at University Medical Center Hamburg-Eppendorf, he joined the University of Lübeck in 2011 as a Juniorprofessor of Magnetic Resonance Imaging, achieving full professorship in November 2018. His research focuses on advanced MRI techniques, particularly diffusion imaging methods that reveal microscopic neural structures. Koch has made significant contributions to double wave vector diffusion weighting techniques for compartment size estimation in tissue, with applications in neuroscience and clinical diagnostics. His work spans Magnetic Resonance Imaging, Diffusion Neuroscience, Functional Magnetic Resonance, Biochemical Engineering, Magnetic Particle Imaging, Nuclear Imaging, Image Computing, and X-Ray Based Imaging. Over the past two decades, his publications demonstrate consistent innovation in developing and validating advanced diffusion MRI techniques, with recent work expanding into magnetic particle imaging applications. Analysis of his 15 most recent publications reveals a strong focus on technical innovations in diffusion MRI for neural tissue characterization, with increasing applications in interventional imaging and real-time monitoring. His research shows progression from fundamental diffusion physics to clinical applications, particularly in neurological conditions and cardiovascular imaging. Professor Koch actively participates in academic governance as a Member of Promotionskolleg Schleswig-Holstein and the Zentraler Gleichstellungsausschuss. His laboratory focuses on developing novel imaging techniques that provide microscopic insights into tissue structure, with particular relevance to neurological disorders and stroke recovery monitoring.

• Artificial Intelligence
• Model Compression
• Deep Learning
• +۲ مورد دیگر

Yuzhang Shang serves as an Assistant Professor of Computer Science at the University of Central Florida, where he is affiliated with the Artificial Intelligence Initiative. His research bridges theoretical and applied aspects of efficient artificial intelligence systems. His academic credentials include: Ph.D. in Computer Science from Illinois Institute of Technology Dual B.S. in Applied Mathematics and Economics from Wuhan University Dr. Shang's primary research focuses on developing scalable and efficient AI methodologies, with particular expertise in model compression techniques for deep learning architectures. His work addresses critical challenges in deploying resource-intensive models on edge devices through innovations in quantization, binarization, and dataset distillation. This research spans computer vision, natural language processing, and generative models, aiming to reduce computational costs while maintaining model accuracy. Analysis of his 2023-2024 publications reveals a concentrated research trajectory in model compression, especially for large language models and diffusion architectures. His work demonstrates methodological innovations in contrastive learning for quantization calibration, causal approaches to data-free quantization, and mutual information optimization for dataset distillation, establishing him as an emerging leader in efficient AI. His notable recognitions include: ML and Systems Rising Stars 2025 by MLCommons Award of Excellence in Dissertation Research at Illinois Institute of Technology While no specific student advisement or grant information is publicly documented, his industry internships at Google DeepMind and Cisco Research indicate strong translational research capabilities. His ongoing work likely involves collaborations through UCF's Artificial Intelligence Initiative to advance practical AI deployment. As an active member of UCF's research ecosystem, he contributes to the university's Artificial Intelligence Initiative, which fosters interdisciplinary collaboration on cutting-edge AI challenges across academic and industry partners.

Aysim Tokerمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Aysim Toker is a Ph.D. candidate at the Chair for Computer Vision and Artificial Intelligence , affiliated with the Technical University of Munich . She works under the supervision of Prof. Dr. Laura Leal-Taixe and Prof. Dr. Xiaoxiang Zhu on interdisciplinary projects. Research Focus: Deep learning, sequence analysis, and remote sensing. Key Contributions: Advancing video object segmentation, Earth observation models, and anonymization techniques. Collaborations: Partnered with international researchers across multiple institutions. Publications span top venues like eLife , ICCV , NeurIPS , and CVPR , with a focus on: Deep learning architectures for multi-modal tasks Image and video analysis in remote sensing and anonymization Tracking and segmentation algorithms Her work bridges foundational computer vision research with applications in Earth science and privacy-preserving technologies.

• Food Science
• Food Engineering
• Nutritional Science
• +۵ مورد دیگر

Kehinde Adekunbi Taiwo is a Professor at the Obafemi Awolowo University in Nigeria, with over 25 years of contributions to food science and engineering since 1996. His work bridges international collaboration through affiliations with institutions like Technische Universität Berlin and Karlsruher Institut für Technologie, Campus Süd . Research Focus: Taiwo specializes in food quality properties related to processing techniques, particularly drying methods, osmotic dehydration, and thermal treatments. His studies on yellow-fleshed sweet potato flour, locust bean seeds, and cowpea varieties highlight his emphasis on enhancing nutritional and functional attributes of food through optimized processing. Article Trends: His publications span from 1997 to 2022, focusing on food dehydration, mass transfer dynamics, and modeling of agricultural processes. Collaborative work with institutions in Germany and Nigeria underscores his global research impact. Sponsorships: His research has been supported by organizations including the Medical Research Council , National Science Foundation , and Alexander von Humboldt-Stiftung , reflecting the international recognition of his work. Academic Influence: With 61 publications, 1415 citations, and a high H-index, Taiwo demonstrates significant academic influence. His co-authorship network spans 16 institutions, including 13% single-authored papers and 86% co-authored contributions.

Lars Schäferمشاهده پروفایل

استاد

• Theoretical Chemistry
• Molecular Dynamics
• Biomolecular Systems
• +۵ مورد دیگر

Lars Schäfer , Professor at the Faculty of Chemistry and Biochemistry at Ruhr University Bochum , leads the Molecular Simulation Group. His research focuses on the interplay between structure, dynamics, and function of biological macromolecules using computational methods like molecular dynamics (MD) and QM/MM simulations. Key research areas: solvation science, membrane protein dynamics, ABC transporters, and hydration thermodynamics. His group contributes to the Cluster of Excellence RESOLV and utilizes the ZEMOS facility for solvent-driven process simulations. Recent work includes collaborations on oxygen-stable hydrogenases, nanodisc modeling, and force field development (e.g., Martini 3). The group's scientific approach spans from fundamental quantum mechanical studies (e.g., atomic radii calculations) to applied research in pharmaceuticals (e.g., therapeutic protein stabilization). Their simulations provide atomic-level insights into phenomena like liquid-liquid phase separation and ATP-driven membrane transport. Labs & Collaborations : Hosted at the Center for Theoretical Chemistry (ZEMOS). Active in interdisciplinary networks: Integrated Graduate School Solvation Science, RUB Research School, and international partnerships.