جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Dr. Tryphon Lambrou is a Senior Lecturer in Computing Science at the School of Natural and Computing Sciences, University of Aberdeen, where he joined in January 2022. He previously served as a Senior Lecturer at the School of Computer Science, University of Lincoln, and holds an Honorary Lecturer position at the Department of Medical Physics & Bioengineering, University College London. He has held leadership roles including Interim Vice-Dean for the Joint Institute of Data Sciences and Artificial Intelligence (June 2022–January 2023) and Programme Director at the University of Lincoln. Senior Lecturer, University of Aberdeen (2022–present) Senior Lecturer, University of Lincoln (prior to 2022) Honorary Lecturer, UCL Medical Physics & Bioengineering External Examiner, MSc AI & Data Science, University of Hull Dr. Lambrou's research spans artificial intelligence, machine learning, and computer vision with strong applications in medical imaging and diagnostics. His work focuses on brain tumor segmentation, fetal ultrasound analysis, radiomics, and physiological monitoring using thermal imaging and robotics. He has made significant contributions to multimodal MRI analysis, automated biometry, and deep learning architectures such as MDA-Unet. His recent publications (2022–2024) emphasize deep learning for medical segmentation, out-of-distribution detection in gastrointestinal imaging, and weakly supervised pre-training in neuroimaging. Earlier works (2000–2018) highlight arterial blood flow modeling, PET-CT motion correction, and fetal heart segmentation, demonstrating a long-standing expertise in biomedical signal and image processing. Member, IEEE Professional Member, ACM Full Member, Institute of Physics and Engineering in Medicine (IPEM) Dr. Lambrou actively supervises PhD students in Computing Science and has led research degrees (PGR) at Lincoln. His research has been funded through EPSRC and MRC via the Interdisciplinary Research Consortium. He has contributed to major conferences and journals in medical imaging, AI, and biomedical engineering. He is affiliated with the Centre for Medical Image Computing (CMIC) at UCL and has collaborated extensively on projects bridging computer science and clinical applications, particularly in oncology, neurology, and obstetrics.

Jyoti Joshi Dhallمشاهده پروفایل

مدرس ارشد

• Affective Computing
• Multimodal Behaviour Analytics
• Digital Health
• +۴ مورد دیگر

Jyoti Joshi Dhall is a Senior Lecturer in the Department of Human Centred Computing at Monash University, where she conducts interdisciplinary research at the intersection of artificial intelligence, behavioral analytics, and digital health. Her work contributes to UN Sustainable Development Goals, particularly in health and well-being. Her research focuses on affective computing , multimodal behavior analytics , and human-centered AI , with applications in mental and physical health monitoring. She employs advanced machine learning techniques to analyze behavioral signals such as facial expressions, speech, and body movements for detecting conditions like pain, apathy, and depression. Recent publications show a strong trend in applying deep learning models—such as LSTM-DNN and anomaly detection networks—to clinical and assistive technologies. Her work appears in top-tier venues including IEEE conferences and the ACM Handbook series, reflecting impactful contributions to multimodal user interfaces and emotion-aware systems. She has collaborated with international researchers and her work has been cited in Scopus and referenced in patents. Although no specific awards are listed, her research has attracted attention from news outlets and academic platforms like Mendeley. Jyoti Joshi Dhall advises students and likely supervises research projects in human-centered computing, though specific advisees are not listed. Her research is supported by institutional and possibly external grants, given the scope and publication record. She is part of a broader research network focused on AI for healthcare, contributing to both theoretical and applied advancements in the field. She is affiliated with a research team or lab working on multimodal interaction and affective computing, though the specific lab name is not mentioned in the provided text.

• Medical Image Processing
• Computational Anatomy
• Deep Learning
• +۵ مورد دیگر

HONTANI Hidekata is a Professor at Nagoya Institute of Technology's Department of Information Engineering, Media Informatics Field, and Graduate School of Engineering, Media Informatics Program. He is also affiliated with the Advanced Medical Physics and IT Research Center and the Center for Research and Development in Higher Engineering-Education. His career includes academic roles at institutions like Yamagata University and The University of Tokyo. Doctorate in Engineering from The University of Tokyo (2000) Professional memberships in IEEE, SICE, IEICE, and IPSJ Research focuses on Medical Image Processing and Computational Anatomy , with recent advancements in deep learning applications for pathology image analysis, TMS electromagnetic modeling, and spatiotemporal cancer dynamics. He pioneered techniques for counterfactual image generation in lymphoma pathology and adaptive sparse regularization for signal processing. Notable trends in his publications (2017-2024) include AI-driven histopathology , generative models for medical imaging, and tumor microenvironment analysis using multi-scale MRI-pathology fusion. His work bridges machine learning , computational anatomy , and clinical applications . Scientific Awards : Multiple Japan Society of Medical Imaging and Information Sciences awards (2017-2024), Cum Laude Poster Award at SPIE Medical Imaging (2018) Grants : Principal Investigator for JSPS KAKENHI projects on lymphoma subtyping (2022-2025), 3D tumor modeling (2018-2021), and computational anatomy (2014-2019) He leads the Advanced Medical Physics and IT Research Center and contributes to academic societies as a committee member in organizations including IEICE and Japan Society of Medical Imaging and Information Sciences.

Nikos Komodakisمشاهده پروفایل

استاد

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

Nikos Komodakis is a Professor in the Computer Science Department at the University of Crete, Greece, where he develops efficient, scalable and mathematically well-grounded algorithms for analyzing visual data including static natural images, video, and medical image data. His research spans deep learning, computer vision, machine learning, and artificial intelligence with significant contributions to self-supervised learning, few-shot learning, and knowledge distillation techniques. His work demonstrates a strong theoretical foundation combined with practical applications, particularly in medical imaging. Komodakis has published extensively in top-tier computer vision venues including CVPR, ICCV, ECCV, and IEEE Transactions on Image Processing. His recent publications (2022-2025) show a growing emphasis on medical image analysis applications while maintaining strong contributions to fundamental computer vision problems. Notable contributions include novel approaches for unsupervised representation learning that surpass state-of-the-art methods, effective techniques for knowledge distillation (such as the QUEST framework), and innovative frameworks for few-shot visual learning. Komodakis serves on the editorial boards of prestigious journals including the International Journal of Computer Vision, Computer Vision and Image Understanding Journal, and Computational Intelligence Journal. He has been a frequent area chair for major computer vision conferences including CVPR, ICCV, ECCV, and BMVC. Spyros Gidaris received the Ponts Foundation Best Thesis Prize and the University Paris-Est Best Thesis prize under Komodakis' supervision Sergey Zagoruyko received the AFRIF 2018 Thesis Prize for his PhD work supervised by Komodakis His research group has developed influential techniques including Online Bag-of-Visual-Words Generation for Unsupervised Representation Learning, which surpassed previous state-of-the-art methods. The group maintains active GitHub repositories for many of their publications, demonstrating commitment to reproducible research. Current research directions include advancing medical image analysis through deep learning, improving self-supervised learning frameworks, and developing more efficient neural network architectures.

Carola-Bibiane Schönliebمشاهده پروفایل

استاد

• Image Analysis
• Image Processing
• Inverse Problems
• +۳ مورد دیگر

Carola-Bibiane Schönlieb is a Professor of Applied Mathematics and head of the Cambridge Image Analysis (CIA) group at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge. She concurrently serves as co-director of the Cambridge Mathematics of Information in Healthcare (CMIH) Hub, leading interdisciplinary initiatives at the intersection of mathematics, healthcare, and data science. Her research centers on variational methods, partial differential equations, and machine learning for image analysis, processing, and inverse problems. She maintains active collaborations with clinicians, biologists, physicists, chemical engineers, plant scientists, artists, and art conservators, driving innovations in biomedical imaging, image sensing, and digital art restoration. This interdisciplinary approach bridges theoretical mathematics with real-world applications across healthcare and cultural heritage domains. Analysis of her recent publications reveals a dominant focus on deep learning applications for medical imaging challenges, particularly in cardiology, oncology, and neuroimaging. Her work consistently addresses inverse problems in reconstruction and segmentation while emphasizing robustness against artifacts, model efficiency, and integration of physical constraints. A clear trend emerges toward foundation models and transfer learning techniques specifically adapted for medical image analysis with limited annotated data. Prof. Schönlieb leads the Cambridge Image Analysis research group and co-directs the CMIH Hub, which unites mathematicians, computer scientists, and clinicians to translate advanced data science into clinical practice through collaborative healthcare innovation.

Jeroen van der Laakمشاهده پروفایل

استاد مهمان

• Computational Pathology
• Deep Learning
• Medical Image Analysis
• +۵ مورد دیگر

Jeroen van der Laak is a Visiting Professor at Linköping University's Faculty of Medicine, affiliated with the Department of Health, Medicine and Care (HMV) and the Department of Diagnostics and Specialist Medicine (DISP). He is a key member of the Computational Pathology Group and contributes to the Wallenberg Centre for Molecular Medicine (WCMM), focusing on advancing digital pathology through artificial intelligence and deep learning methodologies. Dr. van der Laak's research centers on computational pathology, with specific expertise in developing deep learning algorithms to improve cancer diagnostics and prognosis. His work spans multiple domains including histopathological image analysis, tumor detection, biomarker identification, and computer-aided diagnosis systems. He investigates how machine learning techniques can enhance pathologist efficiency while reducing observer bias and identifying novel prognostic indicators for personalized cancer treatment. His research addresses critical challenges in computational pathology, including the need for high-quality, large-scale histopathological datasets and clinical validation of AI models. Analysis of Dr. van der Laak's recent publications reveals a strong focus on applying deep learning to diverse pathology challenges across multiple organ systems. His work demonstrates expertise in whole-slide image analysis, tumor segmentation, cancer grading, and biomarker discovery. Notably, his research spans breast cancer, prostate cancer, colorectal cancer, kidney transplant pathology, and gynecological pathology, showing both depth in specific applications and breadth across medical specialties. Key themes include attention-based image compression, human-in-the-loop refinement of AI models, and automated lesion scoring systems. Dr. van der Laak actively collaborates with international pathologists and participates in major European initiatives like the BIGPICTURE project, which aims to build an EU-scale digital pathology repository. His research emphasizes clinical validation of AI models to ensure safety and utility in routine practice, with contributions to consensus studies including recommendations for diagnosing serous tubal intraepithelial carcinoma (STIC) and standardization of molecular pathology education. As part of the Computational Pathology Group, Dr. van der Laak contributes to developing, validating, and deploying novel medical image analysis methods based on deep learning technology, with a focus on computer-aided diagnosis systems that directly support pathologists in their daily work while identifying potential new biomarkers for individualized treatment approaches.

Luwen Huangfuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Business Analytics
• Public Health
• +۴ مورد دیگر

Luwen (Vivian) Huangfu serves as an Assistant Professor in the Management Information Systems Department at San Diego State University's Fowler College of Business. Holding a PhD from the University of Arizona (2019), she has published over 30 peer-reviewed articles in premier AI venues including IEEE Transactions and ACM conferences, accumulating 300+ citations. Her research bridges artificial intelligence with business analytics, public health, and transportation systems. Her educational foundation includes: PhD in Management Information Systems, University of Arizona (2019) Dr. Huangfu's research pioneers few-shot learning and generalized zero-shot recognition with applications spanning pavement distress detection , mental health intervention , and code search optimization . She develops novel architectures for multi-label image classification and document clustering using large language models, with significant contributions to weakly supervised learning and metric network design. Her work consistently addresses real-world challenges in transportation infrastructure, cybersecurity, and healthcare analytics. Recent publications (2023-2025) reveal a strategic focus on efficient deep learning for resource-constrained environments, particularly in transportation and medical imaging. She integrates spatial contextual awareness in multiple instance learning while advancing prompt-based refinement for long-tailed distributions. Her growing emphasis on LLM-driven security intelligence and social media mental health analysis demonstrates cross-domain impact. Her scientific accolades include: Corporation for Education Network Initiatives in California (CENIC) Award (2024) NIH-supported Summer Institute 2024/2025 Cohort NSF-supported University of Maryland Travel Award (2024) Department of Energy (DOE) Award (2022, 2023) NSF-DOE-Jointly-Supported Travel Award (2023) Management Information Systems Quarterly (MISQ) Scholarly Development Academy (2022) 24 total awards including multiple NIH/NSF grants and teaching fellowships As a principal investigator, she has secured funding from DOE, NIH, and NSA while serving as advisor for undergraduate and graduate research scholarships. Her service includes NSF ACCESS Program advisory, NIH/NSF grant review panels, and editorial roles for IEEE Transactions and Journal of Medical Internet Research. She actively mentors students through SDSU scholarship committees and DEI initiatives while maintaining rigorous peer-review commitments across 15+ journals and conferences.

• Computational Biology
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Yixin Chen serves as Chair and Professor in the Department of Computer and Information Science at the University of Mississippi, holding dual Ph.D. credentials in Electrical Engineering and Computer Science. His educational background includes: B.S. in Electrical Engineering from Beijing Polytechnic University (1995) M.S. in Electrical Engineering from Tsinghua University (1998) M.S. in Electrical Engineering from the University of Wyoming (1999) Ph.D. in Electrical Engineering from the University of Wyoming (2001) Ph.D. in Computer Science from Pennsylvania State University (2003) Professor Chen's research demonstrates exceptional interdisciplinary breadth, with Computational Biology forming the dominant theme in recent work—particularly protein structure analysis, antibody interactions, and cancer genomics. His Machine Learning contributions span feature selection, classification algorithms, and deep learning optimization, while Computer Vision applications focus on medical imaging and industrial defect detection. Methodologically, he integrates statistical learning, graph-based models, and spatial relationship analysis to solve complex biomedical problems. Analysis of his publication trajectory reveals a strategic pivot toward bioinformatics since 2020, with protein structure methods (TSR-based approaches) dominating his highest-impact recent work. This complements sustained contributions to statistical learning (Gini correlation/distance methods) and computer vision (Faster R-CNN applications), creating a cohesive research program bridging theoretical algorithms and biomedical applications. As department chair, Professor Chen provides academic leadership. While specific advising details and grant information aren't provided in source materials, his prolific publication record across top venues indicates an active research program with significant real-world impact in medical diagnostics and industrial automation.

Carlos Ramischمشاهده پروفایل

استادیار

• Computational Linguistics
• Natural Language Processing (NLP)
• Multiword Expressions (MWEs)
• +۱۵ مورد دیگر

Carlos Ramisch is an Assistant Professor in Computer Science at Aix Marseille University, France, affiliated with the TALEP research group at LIS (Laboratoire d'Informatique et Systèmes). His work centers on computational linguistics and natural language processing, with a primary focus on multiword expressions (MWEs), language models, and semantic analysis. He actively contributes to the PARSEME community, organizing shared tasks on verbal MWE identification. His research includes developing the mwetoolkit for MWE discovery and SLICE for interpretable contextual embeddings. He has led funded projects such as SELEXINI (2022-2026) and PARSEME-FR (2016-2021). His methodological innovations span cross-lingual dependency parsing via typological features (NAACL 2019), compositionality prediction for nominal compounds (Computational Linguistics 2022), and lexical substitution datasets (IWCS 2017). He supervises students in NLP internships and co-authored the educational comic strip La grande aventure du TAL . His editorial roles include the LSP series on Phraseology and MWEs, and he has chaired multiple MWE workshops (2010-2022).

Bruno Gaujalمشاهده پروفایل

استاد پژوهشی

• Performance Evaluation
• Optimization
• Discrete Event Dynamic Systems
• +۵ مورد دیگر

Bruno Gaujal is a Research Professor at Inria Grenoble-Rhône-Alpes, affiliated with Université Grenoble Alpes. He obtained his PhD from the University of Nice in 1994 under François Baccelli's supervision and has held positions at AT&T Bell Labs, INRIA, and École Normale Supérieure de Lyon. He previously led the MESCAL (now POLARIS) research group focused on large-scale computing until 2015. His research interests center on performance evaluation, optimization, and control of discrete event dynamic systems with stochastic inputs. Specific areas include: Markov Chains and Markov Decision Processes Reinforcement Learning and stochastic optimization Queueing theory and scheduling algorithms Energy-efficient computing in distributed systems Game-theoretic approaches in network optimization Gaujal's recent publications show strong emphasis on reinforcement learning applications in queueing networks, energy optimization for real-time systems, and scalable algorithms for Markov Decision Processes. His work bridges theoretical frameworks like Whittle indices with practical implementations in cloud computing and distributed systems. He has supervised numerous PhD students including Nicolas Gast (now Inria researcher), Anne Bouillard (Huawei researcher), and Emmanuel Hyon (Paris Nanterre professor). Current students include Hélène Arvis and Romain Cravic. Gaujal co-founded RTaW, a startup specializing in real-time network design tools. At Inria, he leads research in the POLARIS group, focusing on optimization methods for large-scale distributed computing infrastructures. His work involves collaborations with 85+ co-authors across institutions globally.

Valentin Patileaمشاهده پروفایل

استاد

• Semi and nonparametric statistics
• Survival analysis
• Time Series
• +۲ مورد دیگر

Valentin Patilea is a Full Professor of Statistics at the National School of Statistics and Information Analysis (ENSAI) in France and serves as Head of the PhD program. He is a Permanent Member of CREST (Center for Research in Economics and Statistics), a prominent research center in economics and statistics. His academic career spans institutions across Europe, with significant contributions to statistical methodology and applications. Professor Patilea's educational background includes a Habilitation à diriger des recherches in Mathematics from the University of Rennes 1 (2006), a PhD in Statistics from Université catholique de Louvain (1997), an MSc in Mathematical Economics and Econometrics from Université Toulouse I (1993), and an MSc in Mathematics from the University of Bucharest (1989). His research focuses on advanced statistical methodologies, particularly in semi and nonparametric statistics, survival analysis, time series analysis, econometrics, and functional data analysis. Patilea's work bridges theoretical statistics with practical applications across various domains, developing innovative methods for complex data structures and dependencies. His research has significantly advanced methodologies for functional data, cure models, and weakly dependent time series. Analysis of Patilea's recent publications reveals a strong emphasis on functional data analysis, with particular attention to adaptive estimation methods, irregular data structures, and computational efficiency. His work spans theoretical developments in statistical methodology while maintaining connections to practical applications in economics and other fields. The publications demonstrate increasing sophistication in handling complex data structures, particularly multivariate functional data and dependent observations. Professor Patilea actively contributes to the academic community through editorial service, currently serving as Associate Editor for Bernoulli Journal (since 2022) and Statistical Methods and Applications (since 2025). Previously, he served on the editorial boards of the Journal of the Royal Statistical Society: Series B and the Journal of the American Statistical Association. He has supervised numerous PhD students, including current candidates Omar Kassi, Hassan Maissoro, and Daphne Aurouet, and has previously guided successful dissertations by Sunny Wang, Guillaume Flament, Edouard Genetay, and others. His supervision spans theoretical statistics, functional data analysis, and econometric applications. Professor Patilea leads the FunStatMath research initiative focused on Functional Data Analysis, which addresses mathematical challenges posed by data that naturally occur as curves or surfaces rather than vectors. This network connects researchers working on theoretical developments and applications across neuroscience, environmental sciences, and biology.

Mehmet Can Yavuzمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Human-Computer Interaction
• +۳ مورد دیگر

Mehmet Can Yavuz is an Assistant Professor at Işık University's Faculty of Engineering and Natural Sciences, Department of Computer Engineering. As Principal Investigator of the Multimedia Lab, he bridges machine learning with artistic expression through projects like Arky Multimedia, ConvergedMachine, and Duyukoru. His research spans biomedical imaging, human-computer interaction, and cross-modal analysis of multimedia storytelling. 2019-2023: PhD in Computer Science & Engineering, Sabancı University 2010-2016: MS in Physics, Boğaziçi University 2006-2010: BS in Physics, Işık University 2004-2010: BS in Electrical-Electronics Engineering, Işık University Current research explores: Advanced machine learning architectures (Variational Contrastive Learning, Cross-D Convolution) Biomedical imaging applications for disease detection Computational analysis of dramatic/literary works through graph theory and sentiment analysis AI-driven threat detection systems using sensor fusion Creative technology intersections in multimedia production His lab develops frameworks for: Noisy data processing in semi-supervised learning Cross-dimensional knowledge transfer Ensemble approaches in 2D/3D medical imaging Temporal-sentiment analysis of urban events Document embedding-based character analysis Projects include: ARKY MULTIMEDIA - Combining creative exploration with ML DUYUKORU - Machine learning-enhanced sensor threat detection CONVERGEDMACHINE - Multimodal ML research repository He oversees the Işık University Multimedia Lab , which integrates medical image computing with animation production, pushing boundaries in both scientific and artistic domains.

Debayan Banerjeeمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Explainability
• Machine Learning
• +۵ مورد دیگر

Dr. Debayan Banerjee is a Researcher at the Institute for Business Information Systems (IIS) and part of the Professorship for Business Informatics, especially Artificial Intelligence and Explainability at Leuphana University Lüneburg. His work bridges academic research with practical applications in knowledge management, network science, and AI systems. Institute: Institute for Business Information Systems (IIS) Professorship: Business Informatics, Artificial Intelligence and Explainability Location: Universitätsallee 1, C4.308b, Lüneburg (21335) Email: debayan.banerjee@leuphana.de Research interests focus on knowledge graph integration, hybrid intelligence systems, and explainable AI. His projects include USIN5G, ARDIAS, and INSTANT, emphasizing human-AI collaboration and scholarly data accessibility. Recent publications highlight SPARQL translation automation, hybrid question answering frameworks, and environmental impact analysis of language models. Collaborative work spans DBpedia-Wikidata interoperability, DBLP knowledge graph applications, and graph embeddings for QA systems. Education and advising : Mentored students include Mathias Gross, Fatemeh Ghoochani, and Soham Majumder, focusing on final theses related to AI-driven data extraction and knowledge graph development.

Nima Akbarzadehمشاهده پروفایل

پژوهشگر ارشد

• Game theory
• Optimal order
• Mathematical modeling
• +۳ مورد دیگر

Nima Akbarzadeh is a Research Fellow at HEC Montréal since May 2023, co-supervised by Erick Delage and Yossiri Adulyasak. He previously completed his Doctorate (2017–2022) in Electrical and Computer Engineering at McGill University under Aditya Mahajan's supervision. Current affiliation: HEC Montréal (Postdoctoral Research Fellow) PhD: McGill University (Electrical and Computer Engineering) Research focus areas: Game theory, Mathematical modeling, and Optimization without derivatives Nima's work bridges algorithm development with risk-aware reinforcement learning (RL), particularly in restless multi-arm bandits and weakly coupled Markov decision processes (MDPs). His recent publications at AISTATS 2025 highlight advancements in sequential decision-making frameworks and fair resource allocation strategies. Scientific awards include: 2024 GERAD Postdoctoral Fellowship 2020–2022 FRQNT Doctoral Scholarship 2017–2019 McGill Engineering Doctoral Award He actively participates in academic workshops and seminars, including IVADO's Risk-Aware/Safe RL Workshop (2023) and McGill's Informal Systems Theory Seminar (2021).

• Computer Science
• Knowledge Graph
• Representation Learning
• +۷ مورد دیگر

Huijuan Xu is an Assistant Professor in the Department of Computer Science and Engineering. Her research spans artificial intelligence, computer vision, and knowledge representation, with a focus on temporal modeling, semantic reasoning, and multimodal learning. She has contributed to advancements in virtual reality streaming, knowledge graph completion, and weakly-supervised video analysis. Research output: 32 publications (2015-2025), including 15 peer-reviewed articles and conference contributions Core research areas: Representation Learning (100% match), Knowledge Graph (100% match), Temporal Action Detection (86% match), and Motion Feature Learning (73% match) Her recent work explores: 2025 : Bandwidth-optimized VR streaming for edge devices 2024 : Neural concept reasoning for image retrieval and avatar generation from sparse data 2023 : Zero-shot scene graph generation and bias mitigation in visual QA