جست‌وجوی استاد و پژوهشگر

Emmanuel Stamatakisمشاهده پروفایل

پژوهشگر ارشد

• Neuroscience
• Neuroimaging
• Cognitive Neuroscience
• +۵ مورد دیگر

Dr. Emmanuel Stamatakis is a Senior Research Associate at the University of Cambridge, affiliated with the School of Clinical Medicine and the Division of Anaesthesia. He maintains research ties with the Centre for Speech, Language, and the Brain (CSLB) while focusing on neuroimaging, consciousness studies, and traumatic brain injury (TBI) mechanisms. Institution: University of Cambridge Primary Affiliation: School of Clinical Medicine, Division of Anaesthesia Research Affiliation: Centre for Speech, Language, and the Brain (CSLB) Academic Rank: Research Fellow His research spans neuroscience, neuroimaging, and anaesthesia, with particular emphasis on: Consciousness dynamics under pharmacological and pathological conditions Functional and structural brain connectivity in TBI and delirium Neural mechanisms of anaesthetic agents Evolutionary and comparative neuroscience of brain states Long-term outcomes after neurological trauma Neurotechnology applications in clinical settings Recent publications reveal trends in: Quantifying consciousness through functional gradients and harmonic decomposition Linking anaesthesia-induced neural changes to clinical outcomes Exploring genetic and inflammatory factors in TBI recovery Multi-modal imaging approaches to neurological disorders Translational studies bridging animal models and human conditions Methodological advancements in brain network analysis

• Urban Sustainability
• Urban and Regional Planning
• Transportation and Freight Services
• +۴ مورد دیگر

Dr. Ahmadreza Faghih Imani is a Teaching Fellow in Urban Sustainability at the Centre for Environmental Policy, part of the Faculty of Natural Sciences at Imperial College London. He also serves as co-convenor of the Urban Sustainable Environments (USE) option for the MSc in Environmental Technology. Previously, he was a Research Associate at the Centre for Transport Studies (Imperial College London) and held an NSERC Postdoctoral Fellowship at the University of Toronto. He earned his PhD from McGill University in 2017, recognized with the Benjamin H. Stevens Graduate Fellowship. His research focuses on urban sustainability, exploring how built environments, transport infrastructure, and advanced modeling techniques can enhance environmental and social sustainability. Key interests include transportation planning, energy consumption modeling, and policy analysis with over 30 publications in top-tier journals. He has contributed to industry/government projects (9 technical reports), volunteered with TRB's Bicycle Transportation Committee, and reviewed for academic journals. His work bridges disciplines such as urban planning, civil engineering, and applied mathematics. Recent efforts emphasize health integration into mobility policies, smart energy systems, and innovative data collection methods (e.g., smartphone apps). He collaborates widely with over 10 faculty members and 20 researchers globally. Notable achievements include modeling air pollution exposure from transit policies and analyzing cycling infrastructure impacts in cities like Toronto and Montreal. His research addresses urgent urban challenges through interdisciplinary frameworks and policy-relevant insights.

Sharib Aliمشاهده پروفایل

استادیار

• Biomedical Image Analysis
• Computer Vision
• Machine Learning
• +۱۲ مورد دیگر

Dr. Sharib Ali is a Lecturer (Assistant Professor) in the School of Computer Science at the University of Leeds, Faculty of Engineering and Physical Sciences. He is affiliated with the Leeds Cancer Research Centre and actively contributes to research in biomedical image analysis and computer vision. His work bridges cutting-edge AI with clinical applications, particularly in endoscopy and surgical technologies. PhD in Medical Image Analysis, University of Lorraine, France MSc in Computer Vision (by research), University of Burgundy, France Dr. Ali's research focuses on biomedical image analysis , computer vision , and machine learning , with applications in early cancer detection , computational endoscopy , and 3D reconstruction . He develops robust algorithms for segmentation, registration, depth estimation, and mosaicking, using both classical mathematical models and deep learning. His work emphasizes translational research and generalisability in real-world clinical settings. The recent publications highlight a strong trend in generalisability assessment , multi-modal data fusion , and AI benchmarking in endoscopy. His work spans from foundational algorithm development to clinical deployment, including federated learning , mixed reality in surgery , and multi-centre datasets , addressing key challenges like bias, data imbalance, and privacy. Dr. Ali has co-supervised multiple DPhil/PhD students and currently supervises several PhD candidates at the University of Leeds, University of Oxford, and Tec de Monterrey. He is actively involved in securing research funding and leading projects such as Leveraging multi-modality data for targeted biopsy and Federated learning in healthcare . He is a founding member of NAAMII, Nepal, where he volunteers to train students from LMICs. He also organizes international research initiatives including the EndoCV and P2ILF challenges at MICCAI, and serves on program committees and as a reviewer for journals like Nature Communications and Medical Image Analysis . His research is conducted within interdisciplinary teams, collaborating with clinicians from Oxford NHS University Hospitals, neuroscientists at Forschungszentrum Jülich, and engineers across Europe. He leads the development of open tools and datasets to advance the field of endoscopic computer vision.

• Intelligent Transportation Systems
• Connected and Autonomous Vehicles
• Vehicular Networks
• +۶ مورد دیگر

Daxin Tian is a prominent professor at Beihang University's School of Transportation Science and Engineering, specializing in intelligent transportation systems and vehicular networks. With over 170 publications spanning from 2006 to 2025, his research has significantly contributed to the advancement of connected and autonomous vehicle technologies. His work appears consistently in top-tier IEEE journals including IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Vehicles, and IEEE Internet of Things Journal, establishing him as a leading authority in the field. Professor Tian's research interests encompass several critical areas in modern transportation technology: Connected and Autonomous Vehicle Systems Vehicular Networking and Communication Protocols Vehicle Platooning and Cooperative Driving Algorithms Edge Computing Applications for Transportation Computer Vision for Autonomous Driving Perception Traffic Flow Optimization and Prediction Models Resource Allocation in Vehicular Networks His recent publications demonstrate an increasing sophistication in addressing complex multi-vehicle scenarios while maintaining practical considerations like communication reliability, energy efficiency, and safety constraints. The research trajectory shows a clear evolution from foundational networking and control problems toward more integrated AI-driven solutions that combine computer vision, natural language processing, and advanced control theory for next-generation transportation systems. Professor Tian maintains extensive international collaborations, particularly with researchers at Canadian institutions including Victor C. M. Leung's group, while leading a substantial research team at Beihang University. His work frequently bridges theoretical advances with practical transportation challenges, resulting in numerous high-impact publications that address real-world implementation barriers in intelligent transportation systems.

Maria Elisa Finaمشاهده پروفایل

پژوهشگر

• Multimodal Analysis
• Translation Studies
• Cultural Heritage Communication
• +۵ مورد دیگر

Maria Elisa Fina is a Researcher (RtdB) at Ca' Foscari University of Venice, affiliated with the Department of Comparative Linguistic and Cultural Studies and the Interdepartmental School of Economics, Languages and Entrepreneurship for International Exchanges. Her work focuses on multimodal analysis of cultural heritage communication, translation studies, and audiovisual translation. She teaches courses in translation technologies, post-editing, and audiovisual translation at both undergraduate and master's levels. Education: Bachelor's in Literary and Technical-Scientific Translation (University of Salento, 2011) PhD in Linguistic, Historical-Literary, and Intercultural Studies (Ca' Foscari University, 2016) Research Interests: Multimodal analysis of audio guides, intersemiotic translation, museum communication for children, digital storytelling, and accessibility in cultural mediation. She has contributed to projects like DIETALY (PRIN 2020) and iNEST Tourism, exploring translation in cultural heritage contexts. Notable Achievements: Winner of the AIA/Carocci PhD Prize 2017 for best PhD thesis in Anglistica Published Investigating Effective Audio Guiding (Carocci, 2018) Recipient of the 2021 Ca' Foscari Excellence Award Teaching: Courses include Translation and Post-Editing Technologies , Audiovisual Translation and Audiodescription , and Translation Technologies: Tools and Methods . She emphasizes practical skills in translation technology and digital tools. Lab/Team Affiliations: Collaborates with research groups on accessibility in cultural heritage, digital humanities, and language mediation. Active in editorial boards of Trans-Kata and Le Simplegadi .

Donald E. Brownمشاهده پروفایل

استاد

• Data Fusion
• Knowledge Discovery
• Predictive Modeling
• +۵ مورد دیگر

Donald E. Brown is the W.S. Calcott Professor in the Systems and Information Engineering Department at the University of Virginia, serving as Founding Director of the Data Science Institute and Co-Director of the Translational Health Institute of Virginia. He holds a B.S. from the United States Military Academy (1973), M.S. and M.E. from UC Berkeley (1979), and a Ph.D. from the University of Michigan (1985). His research focuses on data fusion, knowledge discovery, and predictive modeling with applications in healthcare, security, and safety. Dr. Brown leads over 90 federal/state/private research projects, publishes extensively (120+ papers, 2 books), and is a Fellow of the IEEE. He has received prestigious awards including the Norbert Wiener Award and IEEE Millennium Medal. His work bridges academia and industry through Commonwealth Computer Research, Inc., providing data analysis services. He advises on national committees including the National Research Council and the NRC Committee on Transportation Security. His teaching excellence was recognized by students three times as 'best undergraduate teacher' (2001–2003). Research Interests: Data Fusion, Knowledge Discovery, Simulation Optimization, Machine Learning, Predictive Analytics Publications: Focus on healthcare analytics (e.g., Long COVID, tuberculosis, histopathology), AI-driven medical imaging (capsule endoscopy, eosinophil segmentation), and cybersecurity applications. Awards: IEEE Joseph Wohl Career Achievement Award (2017), Governor's Technology Award (1999), Norbert Wiener Award (2002). Grants/Projects: Over 90 funded projects on data science, healthcare tech, and security systems. He leads interdisciplinary initiatives like the iTHRIV Commons for health data sharing and develops AI tools for medical diagnostics at UVA. Current work includes AI in cardiovascular disease prediction, perioperative data digitization for LMICs, and real-time anomaly detection in healthcare systems.

Tillman Weydeمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Signal Processing
• Data Analysis
• +۵ مورد دیگر

Dr. Tillman Weyde is a Reader in the Department of Computer Science at City, University of London , where he has been employed since 2021. He leads the Machine Intelligence and Media Informatics Research Group and is a member of the Machine Learning Group . Prior to this, he served as Senior Lecturer (2009–2021) and Lecturer (2005–2009) at City, and worked as a researcher at the University of Osnabrück (2001–2005), coordinating the MUSITECH project. His academic background includes PhD in Music Technology (2002), MSc in Computer Science (1999), and MSc in Mathematics, Music, Philosophy & Pedagogy (1994), all from the University of Osnabrück. Research Focus: Machine learning and signal processing methods for data analysis with applications in finance, audio, NLP, music, health, security, and education. His recent work emphasizes inductive biases in neural networks for rule-learning, extrapolation, generalization, and interpretability. Grants & Projects: Principal Investigator for the AHRC-funded Digital Music Lab (2012–2017) and Integrated Audio-Symbolic Model of Music Similarity (2017–present). Co-investigator in Innovate UK and EPSRC projects on safer gambling ( Advancing Consumer Protection , 2015–2018) and Raven (2012–2021). Collaborations: Affiliated with the Institute of Cognitive Science (Osnabrück), Intelligent Systems Research Laboratory (Reading), and the MPEG Ad-Hoc Group on Symbolic Music Representation. Awards: Co-author of the 2000 Comenius Medal-winning educational software Computer Courses in Music Ear Training and co-editor of the Osnabrück Series on Music and Computation . Publications: Over 150 peer-reviewed works including conference papers, journal articles, and book chapters, focusing on interdisciplinary applications of machine learning in music, health, and finance. Students: Supervised 13 PhD students across topics like grammar bias in neural networks, emotion recognition from audio, extrapolation behavior in neural networks, relation-based patterns, legal text parsing, and more.

Ashutosh Bhaveمشاهده پروفایل

استادیار

• Quantitative Marketing
• Econometrics
• Machine Learning Applications in Marketing
• +۴ مورد دیگر

Ashutosh Bhave is an Assistant Professor in the Department of Marketing at the University of Arkansas Walton College of Business . His research focuses on quantitative marketing, causal inference in policy analysis, and machine learning applications to consumer behavior. Education : PhD in Quantitative Marketing (UT Dallas), MS in Information Technology and Management (UT Dallas), BE in Information Technology (Pune University) Industry Experience : Worked at Digital Room, Inc. and Cognizant Technology Solutions Teaching : Undergraduate Marketing Research, Graduate Marketing Analytics His research combines econometric modeling with machine learning to analyze quasi-experimental datasets. Key areas include: Free shipping policy impacts in online retailing Social media brand conflicts and offline sales effects Substance use policy interactions (marijuana legalization vs cigarette sales) Psychedelics decriminalization effects Course modality impacts in business education Public policy evaluation through consumer data Publications demonstrate methodological expertise in: Quasi-experimental design Time-series analysis Causal inference techniques Machine learning integration Data-driven policy evaluation Consumer behavior modeling

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Prof. Fabio Galasso is a Full Professor in the Department of Computer Science at Sapienza University of Rome, where he heads the Perception and Intelligence Lab (PINLab). His research focuses on fundamental innovation in computer vision and machine learning, with particular emphasis on distributed intelligent systems, perception frameworks, and general intelligence within sustainable and interpretable AI contexts. His research interests span multiple domains of computer vision including video segmentation , motion forecasting , distributed intelligent systems , and shape reconstruction . Galasso's work emphasizes sustainable AI approaches that operate within constrained computational resources while maintaining interpretability and verifiability. His research bridges theoretical foundations with practical applications across retail, smart cities, and industrial settings. His recent publications demonstrate a clear progression toward increasingly complex human motion understanding and forecasting, with a strong emphasis on real-world applications. The research trajectory shows movement from foundational video segmentation techniques toward sophisticated motion prediction systems and anomaly detection frameworks that integrate multiple modalities. Key themes include temporal consistency, computational efficiency, and practical deployability in resource-constrained environments. His scientific achievements have been recognized with prestigious awards: 2019 IoT/WT Innovation World Cup 2019 Digital Champions Award 2018 Deutscher Digital Award Galasso has coordinated significant research initiatives including a Marie Sklodowska-Curie Actions project (Horizon 2020) and served as Principal Co-Investigator in multiple German-funded projects from the Ministry of Education and Ministry of Economics. His industry experience includes founding and directing OSRAM's Computer Vision Department in Munich, where he led R&D efforts connecting AI research with smart lighting applications, resulting in successful innovation transfers like the award-winning VISN product. He leads the Perception and Intelligence Lab (PINLab) at Sapienza University of Rome, fostering research that spans fundamental computer vision techniques to practical implementations in retail, smart cities, and industrial applications. The lab maintains strong connections with both academic institutions (including previous collaborations with University of Cambridge and Max Planck Institute) and industry partners.

Christian Büselمشاهده پروفایل

پژوهشگر

• Visual Attention
• Visual Search
• Top-Down and Bottom-Up Processing
• +۶ مورد دیگر

Dr. Christian Büsel is a researcher at the Department of General Psychology I, Institute of Psychology, University of Innsbruck . His work focuses on visual attention mechanisms, including top-down and bottom-up processing, and their relationships with working memory. Email: Christian.Buesel@uibk.ac.at Research Interests: Visual attention, visual search, and the interplay between working memory and intentional visual attention. He employs behavioral experiments, meta-analyses, and mobile eye tracking to study suppression of irrelevant stimuli and contingent capture effects. Methodological Contributions: A key developer of the open-source Experimenters' Tools project, which provides resources for psychological research, including visual angle calculations, color conversions, power analyses, and diffusion modeling. His textbook on empirical-experimental methods is available via Springer. Articles Trends: Recent publications emphasize attentional control, feature-based suppression, and cross-modal cognitive processes (e.g., music reading and auditory discrimination). Technical contributions highlight mobile experimentation frameworks and OpenSesame-based methodologies.

Kathleen Curranمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Prof. Kathleen Curran is a Professor at University College Dublin (UCD) and director of the UCD machine learning in medical imaging and diagnostics innovative research lab ( https://www.ucd-ml-mi.com/ ). She serves as an Affiliated Principal Investigator in the Centre for Biomedical Engineering, an INSIGHT funded investigator, and a funded investigator in the Science Foundation Ireland centre for research training in machine learning (ML-Labs). Her research integrates artificial intelligence, computer vision, and clinical medicine to develop interpretable AI solutions for medical diagnostics. Key focus areas include fetal ultrasound imaging, cardiac MRI reconstruction, neuroimaging for Alzheimer's disease and multiple sclerosis, and biomarker discovery for conditions like lymphangioleiomyomatosis and placenta accreta spectrum. She pioneers techniques in diffusion models, explainable AI, and multi-modal learning to address challenges in low-data medical scenarios. Analysis of her recent publications reveals dominant trends in applying generative models for medical data augmentation, developing uncertainty-aware diagnostic systems, and creating interpretable clinical AI tools. Her work consistently targets high-impact clinical applications including fetal development monitoring, cardiovascular disease management, and neurological disorder detection, with strong emphasis on real-world clinical implementation. Scientific recognition includes: 2019 InterTrade Ireland FUSION Project Exemplar Award (with Axial Medical Printing Ltd.) Three Enterprise Ireland Commercialisation Fund awards as Principal Investigator Horizon Europe consortium funding for SMASH-HCM project (Stratification, Management, and Guidance of Hypertrophic Cardiomyopathy Patients using Hybrid Digital Twin Solutions) Prof. Curran leads significant research funding initiatives including Horizon Europe and multiple Enterprise Ireland awards. Her group actively collaborates with industry partners like Axial Medical Printing Ltd. and participates in national research centers such as INSIGHT and ML-Labs, driving translational AI research from bench to bedside. The UCD machine learning in medical imaging and diagnostics lab ( https://www.ucd-ml-mi.com/ ) serves as her primary research hub, fostering interdisciplinary collaborations between computer scientists, clinicians, and biomedical engineers to advance clinical AI solutions.

Sotiria Fotopoulouمشاهده پروفایل

مدرس

• Astrophysics
• Active Galactic Nuclei
• Galaxy Clusters
• +۴ مورد دیگر

Sotiria Fotopoulou is a Lecturer in the School of Physics at the University of Bristol. Her research focuses on astrophysics, particularly in active galactic nuclei (AGN), quasars, galaxy clusters, and cosmic web structure. She contributes to the Euclid mission's scientific objectives through studies of photometric redshifts, galaxy evolution, and environmental effects. Current Project: Principal Investigator for 'Co-evolution or co-existence? Growth of supermassive black holes in next generation surveys' (2023-2024) Research Themes: Quasar census, slitless infrared spectroscopy for high-redshift objects, cosmic web reconstruction, intracluster light analysis, and gravitational lensing detection. Her work integrates Euclid data with machine learning techniques for galaxy morphology and photometric studies. Award: Recipient of Undergraduate Bursary Grant (2020)

Eugene Yangمشاهده پروفایل

پژوهشگر

• Information Retrieval
• Cross-Language Information Retrieval
• Multilingual Information Retrieval
• +۴ مورد دیگر

Eugene Yang is a Research Scientist at the Human Language Technology Center of Excellence (HLTCOE) at Johns Hopkins University, where he focuses on cross language and multilingual information retrieval, multilingual multimodal report generation, and retrieval-augmented generation systems. He received his Ph.D. in Computer Science from Georgetown University in 2021 under the supervision of Ophir Frieder, David D. Lewis, and Jeremy Fineman. His research spans multiple domains within information retrieval, with particular emphasis on high recall retrieval systems, technology-assisted review frameworks, and multilingual processing. He is the developer of TARexp, an open-source Python framework for Technology-Assisted Review experiments, which demonstrates his commitment to creating practical tools for the research community. Yang's publication record shows a clear trend toward increasingly sophisticated multimodal and multilingual retrieval systems, with his recent work focusing on retrieval-augmented generation evaluation, cross-language model distillation, and modular fusion approaches for complex information needs. His research bridges theoretical advances with practical applications in legal technology, healthcare informatics, and multilingual information access. As an active contributor to the information retrieval community, Yang has presented at numerous conferences including SIGIR, ECIR, and TREC, and has collaborated extensively with researchers across institutions. His work demonstrates a strong commitment to reproducibility and practical evaluation methodologies in information retrieval research.

Ladan Shamsمشاهده پروفایل

استاد

• Multisensory Perception
• Bayesian Causal Inference
• Computational Neuroscience
• +۷ مورد دیگر

Ladan Shams is a Professor in the Department of Psychology , Department of BioEngineering , and Neuroscience Program at the University of California, Los Angeles (UCLA), where she directs the Multisensory Perception Laboratory . Her research explores how the brain integrates sensory information into coherent perceptions, combining behavioral studies, mathematical modeling, and computational approaches. Academic Affiliation: Department of Psychology, BioEngineering, and Neuroscience Program at UCLA Lab: Multisensory Perception Laboratory Research Focus: Dr. Shams investigates multisensory integration , Bayesian causal inference , perceptual learning , memory , and neural mechanisms of sensory processing. Recent work examines how crossmodal interactions influence memory, body perception, and aesthetic evaluation. Publications: Her 15 most recent articles (2022–2025) address topics like Bayesian causal inference , BCI tools , sensory precision , and perceptual learning , reflecting her computational and experimental approach to multisensory research. Scientific Recognition: Dr. Shams received the UCLA Undergraduate Research Faculty Mentorship Award (2021) and serves as Associate Editor for Psychonomic Bulletin & Review , Multisensory Research , and Frontiers in Integrative Neuroscience . Students: Mentored graduate students like Charlotte Kelly (NDSEG Fellow), Haocheng (BCI workshop organizer), and Sashel Haygood (NSF GRFP Honorable Mention) Funding: Supported by NSF, NIH, and DARPA grants Media: Featured in Science Friday , NPR , and ESPN for the Jersey Study

I.V. Ramakrishnanمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Logic
• Machine Learning/Computational Logic Combination
• +۲ مورد دیگر

I.V. Ramakrishnan is a Professor in the Department of Computer Science at Stony Brook University. His research spans Artificial Intelligence, Computational Logic, Machine Learning, Information Retrieval, and Computer Accessibility. Ph.D. in Computer Science, University of Texas at Austin (1983) His work focuses on advancing AI and machine learning to solve accessibility challenges for visually impaired users, healthcare informatics, and robotic manipulation. Key contributions include leveraging large language models for multimodal text correction, developing gesture recognition systems for blind users, and applying reinforcement learning to medical data analysis. Recent publications highlight the integration of LLMs in accessibility tools, AI-driven healthcare solutions (e.g., mortality risk prediction, physician attribution), and robotics innovations (e.g., manipulation planning, vertical farming automation). Faculty Service Award (2014) He teaches courses CSE 352 (Artificial Intelligence) and CSE 537 (AI). His research bridges theoretical and applied domains, emphasizing inclusive technology and clinical decision support systems.