جست‌وجوی استاد و پژوهشگر

Ian Brittonمشاهده پروفایل

مدرس

• Performance Analysis
• Sports Coaching
• Elite Sport Development
• +۲ مورد دیگر

Dr Ian Britton serves as Lecturer in Sports Performance Analysis at Manchester Metropolitan University's Department of Sport and Exercise Sciences since January 2024, providing performance analysis support to elite sports teams and individuals while teaching across academic levels. His educational background features a PhD from Manchester Metropolitan University investigating video-based feedback in elite sport through ethnographic methodology, with ongoing publication of related research findings. Research focuses on integrating Performance Analysis and Sports Coaching to optimize athlete and team development, emphasizing practical applications of analytical insights in high-performance environments through interdisciplinary approaches. He actively supervises undergraduate and postgraduate projects examining bowling/batting analysis, set-piece impacts, technical match demands, and scoring patterns across team and individual sports. Based at the Institute of Sport (room 1.02), Dr Britton contributes to MMU's high-performance sport ecosystem through teaching, supervision, and applied research translation.

Xia Cuiمشاهده پروفایل

مدرس ارشد

• Natural Language Processing
• Speech Processing
• Multi-modal Learning
• +۴ مورد دیگر

Xia Cui is a Senior Lecturer in Software Engineering at the Manchester Metropolitan University, affiliated with the Manchester Metropolitan Joint Institute and the Department of Computing and Mathematics. She leads research in the Human-Centred Computing Lab and Natural Language Processing (NLP) Lab. Her expertise spans Natural Language Processing, Speech Processing, Multi-modal Learning, and Machine Learning Applications. Xia holds a PhD (Computer Science, University of Liverpool 2020), MSc (Web Science and Big Data Analytics, UCL 2015), and BSc (Internet Computing, University of Liverpool 2013). Notable projects include a KTP collaboration with University of Manchester and VoiceIQ Ltd to develop AI systems for detecting speaker vulnerability indicators. She also worked with BBC R&D on speech/music discrimination tools during her MSc. Her research trends focus on advancing NLP techniques for mental health detection, multimodal data fusion, and domain adaptation challenges. Recent work includes developing annotator ranking models for mixed-annotator datasets and multimodal approaches for vulnerability identification. She actively participates in conferences like Society of Psychotherapy Research and Cyberpsychology Annual Conference. Xia advises prospective students on MSc/PhD projects requiring Python and ML/NLP proficiency. She is a member of interdisciplinary labs exploring human-computer interaction and AI ethics.

Abhijit Mahalanobisمشاهده پروفایل

دانشیار

• Computational Sensing and Imaging
• Video/Image Processing
• Automatic Target Recognition (ATR)
• +۳ مورد دیگر

Dr. Abhijit Mahalanobis is an Associate Professor at the University of Central Florida (UCF), affiliated with the Center for Research in Computer Vision (CRCV). Previously, he served as a Senior Fellow at Lockheed Martin and held academic positions at the University of Arizona and the University of Maryland. He earned his B.S. from UC Santa Barbara (1984) and M.S./Ph.D. from Carnegie Mellon University (1985/1987). His research focuses on computational sensing, imaging systems, and automatic target recognition (ATR). Notable contributions include work on correlation filters, compressive sensing, and 3D imaging. He has published over 170 papers, holds four patents, and co-authored a book on pattern recognition. Dr. Mahalanobis has received prestigious awards such as the IEEE Fellow (2015), OSA Fellow (2004), and SPIE Fellow (1997). He has been honored with the Lockheed Martin NOVA Award (2005), Scientist of the Year (2006), and Innovator of the Year (1999). He has served on editorial boards for journals like Applied Optics and Pattern Recognition , and chairs for OSA and SPIE conferences. His research spans topics including infrared target detection, compressive sensing architectures, and deep learning for target recognition. The CRCV lab, under his leadership, develops advanced algorithms for defense and surveillance applications.

• Computer Vision
• Medical Image Analysis
• Machine Learning
• +۲ مورد دیگر

Anna Fabijańska is a Professor at the Institute of Applied Computer Science within the Faculty of Electrical, Electronic, Computer and Control Engineering at Lodz University of Technology, Poland. Her research focuses on computer vision, medical image analysis, and machine learning applications in biomedical engineering and environmental science. Her primary research interests include developing advanced algorithms for image segmentation, 3D reconstruction, and neural network applications in medical diagnostics (especially ophthalmology and pulmonology) and forestry research. She specializes in techniques like graph convolutional networks, hyperspectral imaging, and generative adversarial networks for data augmentation. Through analysis of her recent publications, Fabijańska demonstrates consistent focus on improving computational efficiency and accuracy in image-based diagnostics and monitoring systems. Her work spans industrial tomography, corneal endothelium analysis, timber identification, and glacial sediment studies. Her laboratory develops practical computer vision pipelines for healthcare and environmental applications, demonstrating interdisciplinary collaboration between computer science, medicine, and ecology.

• Machine Learning
• Data Science
• Network Science
• +۳ مورد دیگر

Mehmet Aktas is an Associate Professor in the School of Data Science and Analytics at Kennesaw State University, part of the College of Computing and Software Engineering. His research focuses on machine learning and data science, particularly applying topological data analysis (TDA) to network science challenges such as influence maximization and network matching in social, biological, and business networks. He holds a Ph.D. in Mathematics from Florida State University and an M.S. in Mathematics from Bilkent University. His educational background includes studies under Eriko Hironaka (Florida State) and Alex Degtyarev (Bilkent). Research interests emphasize TDA tools for addressing network problems, with applications across domains. Key work includes exploring deception effects in social networks, hypergraph classification, and graph compression techniques. His GitHub repositories (e.g., hypergraphPH, deception) reflect active engagement in algorithm development for network analysis. Publications span topics like influence maximization dynamics, bot detection via persistent homology, and topological approaches to graph classification. Despite no listed awards, his work demonstrates significant contributions to TDA applications in network science. Advising/grants details are not provided, but his research portfolio indicates active involvement in computational and topological method development. Research foci include sheaf Laplacian methods, diffusion processes on graphs, and higher-order interaction analysis in complex systems. He collaborates across disciplines, blending mathematical rigor with data-driven approaches to solve real-world network challenges.

Mohammad Dadashzadehمشاهده پروفایل

استاد

• Management Information Systems
• Database Management
• IT Strategy
• +۵ مورد دیگر

Mohammad Dadashzadeh is a Professor of Management Information Systems at Oakland University’s School of Business Administration, within the Decision and Information Sciences Department. He has held roles including Director of Applied Technology in Business (ATiB) from 2003 to 2009. His education includes a B.Sc. in Electrical Engineering (MIT), M.Sc. in Computer Science (MIT), MBA, and Ph.D. in Computer & Information Science (University of Massachusetts). Previously, he served at Wichita State University as W. Frank Barton Endowed Chair in MIS, and the University of Detroit. Research Interests : Enterprise Data Integration, MIS Audit, Strategic IT Planning, Database Management, IT Governance, and Data-Driven Decision Making. His work often focuses on practical case studies to bridge theory and application in business systems. Awards & Recognition : Recipient of multiple teaching awards, including the Board of Trustees’ Excellence in Teaching (2000), Co-op Education Adviser of the Year (2003), and MIT Teaching Excellence (1977). Recognized for editorial contributions to journals like Journal of Database Management and leadership roles in academic and professional organizations. Publications & Contributions : Authored 4 books and over 50 articles, with recent works addressing online education challenges, database design, and IT strategy. His 2023 article explores data-driven decision-making in ecotourism, while earlier works covered cybersecurity, lean manufacturing, and green IT. He has also contributed to standards like the Iranian Standard Code for Information Interchange (ISCII). Professional Engagement : Served on editorial boards, conference committees, and advisory panels globally. Co-founder of Sinasoft and active in academic consulting, having trained over 6,000 professionals worldwide through seminars.

Dengsheng Zhangمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Big Data
• Image Classification
• +۵ مورد دیگر

Dr. Dengsheng Zhang is a Senior Lecturer at Federation University Australia (formerly Monash University) and a Guest Professor at Xi'an University of Posts & Telecommunications, China. He holds a PhD in Computing (2002) and a GCHE (2006). His expertise spans over 25 years in artificial intelligence, big data, image processing, and music classification. He has published over 100 refereed papers (10,000+ citations) and authored the award-winning book Fundamentals of Image Data Mining (Springer, 2019). His research focuses on image classification, object detection, deep learning, and feature extraction. Dr. Zhang leads the Computational Science and Mathematics Group, contributing to projects like SIRBOT (Semantic Image Retrieval) and VFR (Visiting Friends and Relatives Travel Research). He serves as an Associate Editor for World Scientific's Transactions on Signal Processing. His teaching spans calculus, web design, computer networks, and project management. He has supervised 8 completed PhD students and actively engages in grants, including two ARC Discovery projects. His lab explores innovations in image retrieval, music classification, and machine learning applications.

• Nephrology
• Kidney Transplantation
• Transplant Medicine
• +۳ مورد دیگر

Wai Lim is a Clinical Professor at the UWA Medical School (Internal Medicine) within The University of Western Australia. His primary affiliation includes the Perth campus at 35 Stirling Highway. He specializes in nephrology and transplantation medicine, contributing to UN Sustainable Development Goals through research impacting health equity and non-communicable diseases. Research interests focus on kidney transplantation outcomes, immunological challenges in transplants, vascular health in transplant recipients, and clinical practices in dialysis and organ preservation. His work spans HLA mismatch analysis, cancer screening protocols, and novel approaches to organ perfusion techniques. Key grants include leadership in the AN-INSPIRE study exploring alloantibody risks in heart disease among transplant recipients, and the Best-Parent study assessing immunological profiles for optimal kidney donors. Active projects also involve polygenic risk scores for graft loss prediction. Collaborations span international teams in Australia, Europe, and Asia, addressing global transplant medicine challenges. His research has been featured in high-impact journals like Transplant International and Clinical Kidney Journal.

Luca Cosmoمشاهده پروفایل

دانشیار

• Shape Analysis
• Geometric Deep Learning
• Spectral Geometry
• +۳ مورد دیگر

Luca Cosmo is an Associate Professor at Ca’ Foscari University of Venice, specializing in Shape Analysis, Geometric Deep Learning, and Spectral Geometry. His research focuses on 3D shape analysis, spectral methods, and generative models for computer vision tasks. He has contributed to projects like the Average Mixing Kernel Signature (AMKS) and isospectralization techniques, advancing robust shape representation and correspondence. Previously, he held postdoctoral positions at the Geometric Deep Learning group under Prof. Michael Bronstein and collaborated with the spin-off DigitalViews Srl on industrial projects for quality control. He actively serves on program committees for top conferences (CVPR, ICCV, ECCV) and journals (IJCV, PR). His work bridges spectral geometry with machine learning, addressing challenges in partial shape matching, adversarial attacks on deformable shapes, and latent space preservation in generative models. Key trends in his publications include advancements in 3D shape generation (e.g., UniMoGen, 3D-WAG), spectral methods for subgraph learning, and quantum-inspired approaches to shape analysis. He has organized tutorials on shape analysis and contributed benchmark datasets like SHREC’16/17. His research emphasizes practical applications in computer graphics, robotics, and industrial automation. Luca’s advising and grants involve collaborations with institutions like Oxford University (Antonio Norelli) and industry partners. His lab, GLADIA, focuses on geometric deep learning and generative models. Current work includes universal motion generation, high-fidelity 3D reconstruction, and scalable generative models for large datasets.

• Autonomous Vehicles
• Computer Vision
• Intelligent Transportation Systems
• +۵ مورد دیگر

Jose Maria Armingol Moreno is a Full Professor at the University Carlos III of Madrid, affiliated with the Department of Systems Engineering and Automation. He serves as Director of the Master's Degree in Internet of Things: Applied Technologies and leads the Intelligent Systems Laboratory research group within the Duque de Santomauro Institute of Motor Vehicle Safety. His expertise spans autonomous systems, computer vision, robotics, and intelligent transportation infrastructure. Key research interests include vehicle safety, sensor fusion, and deep learning applications in traffic monitoring and autonomous navigation. He has authored over 100 peer-reviewed articles, with recent work focusing on 3D vehicle detection, UAV battery systems, and intelligent infrastructure for smart cities. Dr. Armingol has also led numerous projects on autonomous vehicle development, cooperative driving systems, and traffic safety technologies. His contributions include patented innovations in vehicle inspection and collision avoidance systems.

Chris Childمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Game Development
• Reinforcement Learning
• +۳ مورد دیگر

Dr. Chris Child is a Senior Lecturer in the Department of Computer Science at City, University of London, and currently serves as the Associate Dean for Employability and Engagement since 2020. He leads initiatives such as virtual internships, client-based projects, and the Last Mile training program in collaboration with Accenture. As a researcher, he focuses on AI-driven game agents, employing techniques like reinforcement learning and approximate dynamic programming. Additionally, he founded Childish Things Ltd., a computer game company, and pioneered City’s first Data Science apprenticeship program. His academic journey includes a PhD in Approximate Dynamic Programming from City, University of London (2011), alongside leadership in game development and industry-academia partnerships. His educational background encompasses a BSc in Computer Science and Software Engineering (University of Birmingham, 1993), MSc in Cognitive Science (University of Birmingham, 1994), and a PGDip in Academic Practice (City, University of London, 2004). Professional memberships include the British Computing Society and IEEE. Research interests center on intelligent agents for games, robotics, and healthcare applications. Notable projects include the Cricket Captain game series, virtual laparoscopic training tools, and emotion-based reward systems for AI agents. His work bridges academic research with industry, emphasizing practical applications of AI in gaming and education. Advising and grants are not explicitly detailed, but his role in apprenticeship development highlights his commitment to student employability. His company Childish Things Ltd. exemplifies full-stack game development expertise, from design to marketing. Future research aims to integrate agent research into commercial games and improve software engineering practices in gaming.

Adam Kortylewskiمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Robust AI
• 3D Understanding
• +۳ مورد دیگر

Dr. Adam Kortylewski leads an Emmy Noether research group at the University of Freiburg's Department of Computer Science. His work focuses on developing robust computer vision systems that reliably understand images under challenging conditions including occlusion, novel viewpoints, and adverse weather. The research combines deep learning with computer graphics to create 3D-aware neural network architectures. Research interests include: Robustness to out-of-distribution scenarios 3D object understanding from 2D images Neural analysis-by-synthesis approaches Occlusion handling in visual recognition Compositional network architectures Adversarial robustness Recent publications demonstrate advancements in self-supervised 3D learning, semantic correspondence, and neural rendering. Articles show consistent focus on improving model robustness through 3D-aware architectures and compositionality. Dr. Kortylewski received prestigious Emmy Noether funding from the German Research Foundation (DFG) to support his work on reliable computer vision. His research has applications in autonomous vehicles, industrial robotics, and safety-critical vision systems.

Robert D. Timmermanمشاهده پروفایل

استاد

• Stereotactic Body Radiation Therapy
• Radiation Oncology
• Cancer Immunotherapy
• +۴ مورد دیگر

Robert D. Timmerman, M.D., serves as Professor and Chair of Radiation Oncology and Professor of Neurosurgery at the University of Texas Southwestern Medical Center, holding the Effie Marie Cain Distinguished Chair in Cancer Therapy Research. His clinical focus centers on treating adult and pediatric brain tumors using advanced stereotactic techniques. Dr. Timmerman's research portfolio demonstrates deep expertise in stereotactic body radiation therapy (SBRT/SABR), particularly through leadership in the PULSAR (Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy) initiative. His work bridges radiation physics, immunotherapy, and AI-driven adaptive systems, with recent publications emphasizing biology-guided radiotherapy, multi-omics outcome prediction, and radio-immunotherapy synergies. Current investigations explore FLASH radiotherapy, PET-guided treatments, and real-time adaptive workflows using novel platforms like the RefleXion X1 system. His publication trends reveal three dominant themes: (1) Technical innovation in adaptive radiotherapy (35% of recent work), focusing on CBCT-guided and PET-guided systems; (2) Radio-immunotherapy combinations (30%), particularly mathematical modeling of synergistic effects; (3) Clinical implementation of hypofractionated regimens (25%) across multiple cancer types. Notable projects include the VALOR phase 3 trial comparing lung cancer surgery to stereotactic radiotherapy and NRG Oncology/RTOG 0631 for spinal metastases. Fellow of the American Society for Radiation Oncology (FASTRO) Fellow of the American College of Radiology (FACR) As national principal investigator for RTOG trials and leader of the Veterans Affairs Cooperative Studies Program Study 2005 (VALOR), Dr. Timmerman directs multiple multi-institutional grants evaluating stereotactic radiotherapy for early-stage lung cancer. His team collaborates extensively with physics and AI researchers to develop real-time adaptive systems, including the SCINTIX biology-guided platform. Current efforts focus on integrating circulating tumor DNA analysis with radiation response and optimizing risk-benefit ratios through Calculated Useful Trauma (CUT) frameworks. Dr. Timmerman co-leads the PULSAR research consortium developing personalized ultra-hypofractionated regimens, with active projects spanning prostate, renal cell carcinoma, and brain metastases treatments. The team maintains close partnerships with medical physics and bioengineering departments to implement novel imaging and delivery systems.

Renske Maria van Damمشاهده پروفایل

مدرس ارشد

• Architecture
• Spatial Practices
• Cognitive Sciences
• +۶ مورد دیگر

Renske Maria van Dam is a senior lecturer and researcher in architecture at ArtEZ University of the Arts, and founder of the atelier Spacious. She holds a PhD from KU Leuven (2021) for research on 間 [Ma], the Japanese concept of in-between space, and degrees from TU Delft (BSc 2010, MSc 2013). Her work integrates architecture with cognitive sciences, contemplative practices, and performative arts to explore radical transformations through spatial design. Research focuses on bodily action, slow architecture, feminist spatial practices, and post-Western methodologies. Professional engagements include studios like Herman Hertzberger (NL) and Atelier Li Xiaodong (CN), as well as residencies at Senselab (Australia/Canada) and Reversible Destiny Foundation (Japan/USA). She currently teaches at TU Delft (2021–present) and previously at the University of the Arts in The Hague (2013–2023). Grants/Awards: FWO Flanders Research Fellowships (2018, 2019), The Hague Makers Grant (2022, 2024) Recognition: Featured in Venice Biennale 2014 as a promising Dutch architect Recent projects include Matching & Meshing (2022) and Hugging Architecture (2023), emphasizing participatory and sensorial spatial experiences. Her pedagogical approach blends Asian/Western practices, intuitive bodywork, and interdisciplinary collaboration.

Erwin Folmerمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Linked Data
• +۳ مورد دیگر

Erwin Folmer holds the Applied Data Science & AI professorship at HAN University of Applied Sciences. He is an experienced researcher and consultant specializing in the intersection of business and ICT. His current roles include leading the Data Science Team at the Dutch Land Registry (part-time), serving as a Steering Committee Member of Platform Linked Data Netherlands, and participating in academic advisory boards. Folmer obtained his PhD from the University of Twente in 2012 with a dissertation on semantic standards quality. His research focuses on data interoperability, linked open data, knowledge graphs, and AI applications in real-world contexts. Notable projects include developing the Kadaster Knowledge Graph and advancing FAIR metadata schemas for geospatial data. Folmer also contributes to national initiatives like the National Study Choice Center and European collaborations through platforms like EuroSDR. Education: PhD (2012) in Information Systems from University of Twente; prior work at TNO as senior scientist and lecturer roles at University of Twente. Professional Activities: Member of Permanent Advisory Board for Semantics Conference Contributor to Platform Linked Data Netherlands experimental environments (Utrecht Big Data Value Center) Development of tools like GeoDataWizard for spatial data creation Data Science Group Leadership: Oversees interdisciplinary research integrating data science with AI, emphasizing accessibility and practical applications across government, business, and academia.