جست‌وجوی استاد و پژوهشگر

• Biomedical engineering
• Medical physics
• Diffuse optical tomography
• +۹ مورد دیگر

Adam Gibson is Professor of Medical Physics and Heritage Science at University College London (UCL), holding a full-time position in the Department of Medical Physics & Biomedical Engineering. He has been with UCL since 1999, progressing through various academic ranks from Research Associate to his current professorship. Gibson maintains a dual focus in his academic career, bridging medical physics and heritage science disciplines. His educational background includes a PhD from the University of London (1999), an MSc from the University of Exeter (1996), and a BSc (Hons) from Cardiff University (1994). He is also recognized as a Senior Fellow of the Higher Education Academy. Gibson's research spans two primary domains. In medical physics, he specializes in diffuse optical tomography for functional brain imaging of adults and children, having pioneered the first 3D electrical impedance images of adult brain function and the first 3D optical tomography images of the neonatal head. His heritage science work focuses on developing applications of heritage imaging, particularly multispectral, hyperspectral, and x-ray fluorescence imaging for cultural artifacts and documents. His research has attracted significant funding, with over £3.4 million as Principal Investigator and £6.5 million as Co-Investigator across numerous grants. His recent publications reveal an interdisciplinary research trajectory spanning medical physics applications, heritage conservation techniques, forensic science, and paleontology. The articles demonstrate consistent innovation in imaging technologies applied across diverse scientific domains, with particular emphasis on non-destructive analysis methods for both medical diagnostics and cultural heritage preservation. EPSRC Challenging Engineering Award EPSRC Advanced Research Fellowship Gibson has held significant leadership roles including Research Director in UCL Medical Physics & Biomedical Engineering for 8 years, guiding the department through REF2014 (where they achieved the UK's highest ranking in biomedical engineering) and REF2021. He has secured substantial grant funding and led the UK's contribution to IPERION HS, a major EU research infrastructure project in heritage science. As an educator, he created new BEng/MEng Biomedical Engineering degrees, serves as Faculty Graduate Tutor for UCL Faculty of Engineering Sciences, and has led innovative modules on Heritage Imaging and Physics of the Human Body. Gibson leads research groups in both medical physics and heritage science, with particular focus on developing imaging technologies applicable across both domains. His heritage science group specializes in non-destructive analysis of cultural artifacts, while his medical physics research continues to advance brain imaging techniques for clinical applications.

• Biomedical Imaging
• Radiology
• Molecular Imaging
• +۵ مورد دیگر

Feng Li serves as an Associate Research Scientist in the Department of Radiology & Biomedical Imaging at Yale School of Medicine. His work bridges the gap between advanced materials science and biomedical imaging, with a particular focus on PET imaging and molecular structure analysis. He maintains affiliation with the PET Core facility at Yale, indicating his specialized expertise in positron emission tomography techniques. His research interests span Biomedical Imaging, Radiology, Molecular Imaging, Polymer Science, and Nanotechnology. Dr. Li's work demonstrates exceptional interdisciplinary integration, applying polymer chemistry principles to develop novel imaging agents and drug delivery systems. His publications reveal expertise in cyclic polymers, nanoparticle interactions, and biosensor development, with applications ranging from cancer diagnostics to neurological imaging. Analysis of his recent publications (2024-2025) shows a strong trend toward interdisciplinary research combining materials science with medical applications. His work frequently involves molecular design of polymers for specific biomedical functions, development of novel imaging techniques, and creation of biosensors for disease detection. The publications demonstrate methodological diversity, incorporating computational modeling, chemical synthesis, and clinical validation approaches. Conjugated Polymer Nanoparticles research exploring polymer topology effects Unique Complex Structure studies of cyclic poly(ethylene glycol) on gold nanoparticles Molecular Design work on oligosaccharide-block-oligodimethylsiloxane systems Supramolecular Biosensor development for disease-associated metabolite quantification Advanced imaging techniques for cancer and neurological applications While specific grant information isn't detailed in the provided text, Dr. Li's extensive publication record across high-impact journals suggests successful funding for his research programs. His collaborative work across chemistry, radiology, and biomedical engineering departments indicates leadership in interdisciplinary research initiatives. Dr. Li's laboratory likely focuses on developing novel imaging agents and materials for medical applications, with emphasis on molecular-level design and characterization.

Stina Kleinمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction
• Human-Robot Interaction
• Cobot Error Management in Industry
• +۳ مورد دیگر

Stina Klein is a Scientific Associate at the Chair of Human-Centered Artificial Intelligence within the Institute of Computer Science at the University of Augsburg 's Faculty of Applied Computer Science . Her research focuses center on Human-Computer Interaction , Human-Robot Interaction , and Cobot Error Management in Industry , with applications in social robotics, affective computing, and multimodal systems. Research Interests Human-Computer Interaction Human-Robot Interaction Cobot Error Management in Industry Affective Computing Sign Language Recognition Media Memorability Teaching Lecture: Human-Computer Interaction (Summer 2025) Lecture + Exercise: Human-Robot Interaction (Summer 2025) Research Projects Open Topics for Theses & Projects FORSocialRobots KodiLL

Mark Cravenمشاهده پروفایل

استاد

• Machine Learning
• Bioinformatics
• Computational Biology
• +۵ مورد دیگر

Mark Craven is a Professor at the University of Wisconsin-Madison, holding dual appointments in the Department of Biostatistics and Medical Informatics and the Department of Computer Sciences. He leads the NIH-funded Computation and Informatics in Biology and Medicine (CIBM) Training Program and is affiliated with the Carbone Cancer Center and the Center for Genomic Science Innovation. His research focuses on machine learning applications in biological networks, clinical data analysis, and biomedical text mining. Craven’s work emphasizes interpretable machine learning, active learning, and weak supervision techniques. Key projects include modeling host-virus interactions, gene regulatory networks, and clinical risk prediction from electronic health records. He has contributed to tools like the EDGE platform for toxicogenomic analysis and developed methods for extracting insights from biomedical literature. Publications highlight advancements in viral replication networks, temporal data modeling, and multi-omics profiling. His team’s work spans bioinformatics, computational biology, and healthcare informatics, with applications in asthma, surgical outcomes, and genetic risk assessment. Craven teaches machine learning courses and mentors a vibrant research group, fostering interdisciplinary collaborations across computer science and life sciences. Affiliations include the NIH’s Center for Predictive Computational Phenotyping and the Center for Genomic Science Innovation. His research integrates computational methods with biological and clinical data to address complex problems in health and disease.

• Causal Machine Learning
• Healthcare Informatics
• Medical Data Analysis
• +۳ مورد دیگر

Dr. Vinod Kumar Chauhan is a Research Fellow at the Institute of Biomedical Engineering, University of Oxford. His work focuses on causal machine learning and healthcare applications, including individualized treatment effects estimation and addressing sample selection bias in medical data. Previously, he held a postdoctoral position at the University of Cambridge and earned his PhD from Panjab University, Chandigarh, India. Education: BSc, MCA, PhD (Panjab University, India). Research interests include causal inference, healthcare informatics, and graph neural networks for EHR analysis. Recent projects involve postoperative atrial fibrillation prediction and optimizing treatment effects for composite outcomes. Key Awards: 2021 Institute for Manufacturing Postdoctoral Award (Cambridge), Travel Grant for Asian Conference on Machine Learning 2017. Selected Talks: Keynote on HyperNetworks (2023 Panjab University), invited talks on treatment effects estimation (IISER Pune, 2023). Labs/Teams: Computational Health Informatics group under Prof. David Clifton. Active in departmental EDI committee roles.

Giuseppe Careniniمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• Human-Computer Interaction
• +۴ مورد دیگر

Giuseppe Carenini is a Professor in the Department of Computer Science at the University of British Columbia (UBC), holding the role of Director of the UBC Master of Data Science program. He is affiliated with the Faculty of Science and leads multiple research groups including the UBC NLP Group, CANARY, IUI Group, and CAIDA. His research focuses on intersections between Artificial Intelligence, Computational Linguistics, and Human-Computer Interaction, with notable contributions to discourse analysis, summarization, and interactive visualization. He has been recognized with awards such as the SIGDIAL Best Paper Award and ACM TiiS Best Paper Award. His work spans academic leadership, course instruction (e.g., CPSC courses on AI, NLP), and collaborations with industry and sustainability initiatives (e.g., IRES, Data Science Institute). His projects often integrate multimodal data analysis, decision support systems, and adaptive interfaces. Research Interests: - Artificial Intelligence: User Modeling, Preference Elicitation, Decision Theory. - Computational Linguistics: Discourse Analysis, Summarization, Argumentation. - Human-Computer Interaction: Intelligent Interfaces, Information Visualization. Publications highlight advancements in neural discourse parsing, topic segmentation, and LLM applications in healthcare and operations research. His work bridges theoretical contributions with practical tools like CONVisIT and T³-Vis for data exploration and model interpretation.

• Digital Forensics
• Cyber Security
• Machine Learning
• +۳ مورد دیگر

Arun Kumar is a Lecturer in Digital Forensics and Cyber Security at the University of South Wales, affiliated with the Faculty of Computing, Engineering and Science. His research focuses on interdisciplinary areas such as cyber security frameworks, machine learning applications in healthcare and finance, nanotechnology-based drug delivery systems, and smart city infrastructure protection. His work spans technical innovations like Android-based emergency response systems, intrusion detection in sensor networks, and low-bitwidth neural networks for edge computing. He also explores practical implementations such as solar-powered vehicle control systems and hydrogel synthesis for biomedical applications. Key themes in his publications include predictive analytics for financial markets, ethical hacking practices, and securing industrial cyber-physical systems. His research integrates computational methods with domain-specific challenges in healthcare, materials science, and energy systems.

• Machine Learning
• Intelligent Data Analysis
• Biomedical Signal Processing
• +۷ مورد دیگر

Jose Rafael Magdalena Benedicto is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia, Spain. He is a core member of the Intelligent Data Analysis Laboratory (IDAL), where he leads research in machine learning, biomedical signal processing, and data science applications in health and industry. Doctorate: Universitat de València (2000) His research interests span machine learning , intelligent data analysis , biomedical engineering , neurocognitive disorders in metabolic and psychiatric diseases , algebraic geometry , and quantum machine learning . His work bridges theoretical advances in symbolic computation and applied AI with real-world problems in medicine and industrial systems. Recent publications show a strong trend in applying machine learning to anomaly detection in enterprise systems, asymptotic analysis of algebraic curves and surfaces, and identifying inflammatory and metabolic biomarkers for cognitive impairment in diabetes and psychiatric disorders. He also explores multimodal video analysis for public safety and quantum reinforcement learning. While no scientific awards are listed in the provided text, his extensive publication record and leadership in research projects indicate significant scholarly contributions. He has supervised doctoral students, including Dr. Juan Francisco Guerrero Martínez and Dr. Javier Calpe Maravilla. His research is supported by ongoing projects in intelligent data analysis, though specific grants are not detailed. He contributes to educational innovation, having developed tools like BiomedChallenge to enhance data science learning. He is actively involved in the Intelligent Data Analysis Laboratory (IDAL) , which focuses on developing and applying advanced data analysis techniques to real-life applications in biomedicine, education, and industry.

Peter de B. Harringtonمشاهده پروفایل

استاد

• Chemometrics
• Analytical Chemistry
• Ion Mobility Spectrometry
• +۱۲ مورد دیگر

Peter de B. Harrington is a Professor in the Department of Chemistry and Biochemistry at Ohio University's College of Arts and Sciences. His research focuses on the development of intelligent chemical instruments through the integration of chemometric methods with ion mobility spectrometry (IMS) and mass spectrometry (MS). He is based in Chemistry 305 on the Athens Campus and is an active researcher with a strong publication record. Research Interests: His work aims to make analytical instruments more user-intelligent by enabling real-time data processing and interpretation. Key areas include chemometrics, forensic chemistry (e.g., drug and explosive detection), bacterial identification via MALDI-MS, food authenticity (e.g., flaxseed oil, cranberries), and metabolomic profiling of cannabis and hemp. He develops algorithms for signal enhancement, classification, and multivariate analysis to extract meaningful information from complex datasets. Recent Research Trends: His recent publications (2018–2020) show a consistent focus on applying machine learning (e.g., Restricted Boltzmann Machines), preprocessing techniques (e.g., non-integer root transforms), and multivariate classifiers to spectroscopic and spectrometric data. The research spans diverse applications—from food safety to pharmaceutical development—unified by a strong foundation in chemometrics and data science. Scientific Awards: No specific awards listed in the provided text. Advising and Grants: While specific students and grants are not named, Dr. Harrington has mentored numerous researchers, as evidenced by his frequent inclusion as corresponding author on collaborative publications. His work likely involves extramural funding given the technical nature of the instrumentation and interdisciplinary collaborations. He has contributed to method development in botanical drug regulation and sustainable energy systems. Labs and Teams: Dr. Harrington leads a research group focused on chemometrics and intelligent instrumentation. His team collaborates across disciplines, including food science, electrochemistry, and forensic analysis, leveraging advanced analytical platforms like MS, IMS, and UV/IR spectroscopy.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۵ مورد دیگر

Dr. Sriparna Saha is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Patna, India. She holds a Ph.D. from the Indian Statistical Institute Kolkata and has held leadership roles including Head of Department (2021-2023) and Associate Dean for Research and Development (2019-2021). Her research focuses on AI, machine learning, natural language processing, bioinformatics, and multiobjective optimization. She has authored over 400 publications with an h-index of 38 and received awards such as the NASI Young Scientist Platinum Jubilee Award and Google India Women in Engineering Award. Her work spans multimodal systems, medical image analysis, and computational social systems. Education: M.Tech (2005) and Ph.D. (2011) in Computer Science from Indian Statistical Institute Kolkata. Research Interests: Multimodal information processing, NLP, machine learning, bioinformatics, and optimization techniques. Awards: Includes Lt. Rashi Roy Memorial Gold Medal, BIRD Award, and multiple fellowships (Humboldt, CNRS, etc.). Administrative Roles: IEEE Student Branch Councilor, Senate Member, and Visvesvaraya Nodal Officer at IIT Patna. Her recent work includes advancements in multimodal recommendation systems, breast cancer prognosis models, and computational social systems for crisis management. She has also contributed to hate speech detection in multilingual contexts and medical imaging diagnostics.

Bernard Favratمشاهده پروفایل

دانشیار

• Medical Expertise
• Traffic Medicine
• Internal Medicine
• +۹ مورد دیگر

Bernard Favrat is an Associate Professor of Medical Expertise at the University of Lausanne (UNIL), Faculty of Biology and Medicine. He serves as an assistant physician at the Romand University Center for Legal Medicine (CURML) and the University Medical Outpatient Clinic (PMU), with a dual focus on traffic medicine and internal medicine research. His clinical and academic work integrates forensic, clinical, and epidemiological perspectives. Bachelor of Medicine, University of Lausanne (1986) Doctor of Medicine, University of Lausanne (1996) Master of Teaching and Research (MER1), University of Lausanne Training in Clinical Epidemiology, Yale University (1995–1997) Bernard Favrat's research spans two major domains: clinical expertise in traffic medicine and clinical research in internal medicine. His work in traffic medicine focuses on substance use (alcohol, medication, illicit drugs) and driving ability, including studies on cannabinoids and recidivism prevention through educational interventions. He is developing neuropsychological and neuroergonomic tools to assess driver fitness, particularly in seniors and vulnerable populations. In internal medicine, he leads randomized controlled trials on iron therapy for non-anemic fatigue and international projects on vitamin deficiencies, including meta-analyses of individual patient data. His work often involves collaboration with the blood transfusion service and the University Institute of Family Medicine. His publications since 2010 reflect a strong focus on forensic medicine, internal medicine, and public health. Key themes include impaired driving, substance abuse, iron and vitamin deficiencies, and primary care diagnostics. His research combines clinical trials, systematic reviews, and epidemiological studies, published in high-impact journals such as BMJ, PLoS One, BMC Medicine, and Forensic Science International. Bernard Favrat has received no explicitly mentioned scientific awards in the text, but his sustained publication record and leadership in national expert committees indicate professional recognition. He is actively involved in teaching pre- and postgraduate students in general medicine, traffic medicine, and medical expertise. He contributes to the federal medical examination and delivers postgraduate lectures on driving fitness. He serves on several national assessment committees, including the Fachausschuss Strassenverkehr and the Collège romand des spécialistes en aptitude à conduire, and acts as a co-expert for specialist certification in traffic medicine. Since 2012, he has been involved in developing training programs for medical expertise in social insurance within Swiss Insurance Medicine. Bernard Favrat collaborates with multiple institutions, including the CHUV, Geneva University Hospitals (HUG), Transfusion interrégionale CRS, and the University Institute of Family Medicine. He leads or participates in interdisciplinary research teams focused on traffic medicine, neuropsychological assessment, and clinical nutrition. His work on a neuropsychological test for vulnerable drivers involves simulation and cognitive assessment tools.

• Veterinary Medicine
• Dairy Cow Reproduction
• Udder Health
• +۷ مورد دیگر

Olav Reksen is a Professor at the Norwegian School of Veterinary Medicine, Norwegian University of Life Sciences (NMBU), where he specializes in large animal reproduction and obstetrics. He is affiliated with the Section for Outpatient Clinic and Herd Medicine and has been a key figure in veterinary research, particularly in dairy cow health and reproduction. He served as head of the Department of Production Animal Medicine during two periods: 2010–2014 and 2018–2021. His research focuses on dairy cow reproduction, udder health, mastitis, somatic cell count dynamics, and metabolic disorders . He has extensively studied reproductive performance in Norwegian and Tanzanian cattle systems, incorporating advanced techniques such as Fourier Transform Infrared (FTIR) spectroscopy for milk analysis and mathematical modeling of infection transmission. His work bridges clinical practice, epidemiology, and data-driven diagnostics in veterinary science. His recent publications (2020–2024) show a strong trend toward integrating sensor data, spectroscopic methods, and modeling to improve herd health monitoring. Themes include automatic milking systems, subclinical ketosis detection, pathogen-specific milking patterns, and transmission dynamics of intramammary infections. These works reflect a shift toward precision livestock farming and real-time health assessment using non-invasive technologies. Although no scientific awards are mentioned in the provided text, his long publication record in high-impact journals such as Journal of Dairy Science , Preventive Veterinary Medicine , and Theriogenology underscores his academic influence. He has supervised or collaborated with numerous researchers, especially on projects involving reproductive management, calf health, and dairy herd monitoring in both developed and low-resource settings. Olav Reksen has been actively involved in grant-funded research, particularly on reproductive performance, antibiotic resistance, and herd health in Norwegian Red Cattle, as well as smallholder dairy systems in Tanzania and Malawi. His leadership roles and sustained research output indicate ongoing involvement in both national and international veterinary research initiatives. He is associated with clinical and research teams focused on herd medicine and outpatient services, contributing to both academic training and practical veterinary care. His work often involves interdisciplinary collaboration with statisticians, microbiologists, and agricultural scientists to address complex animal health challenges.

Javed Mostafaمشاهده پروفایل

استاد

• Multimedia Information Retrieval
• Personalization and User Modeling
• Cyberinfrastructure for Research and Learning
• +۷ مورد دیگر

Javed Mostafa is a Professor and Dean of the Faculty of Information at the University of Toronto since September 2023. He previously served as Professor and founding Director of UNC's Carolina Health Informatics Program (2011-2023), Deputy Director for Education & Training at UNC's Biomedical Informatics unit (2010-2023), and as Victor H. Yngve Endowed Professor and Associate Dean at Indiana University (2000s). Education : PhD in Information Science (1994) from The University of Texas at Austin, MA from The Ohio State University, BSc from Northwestern Oklahoma State University Companies : Co-founder of KeonaHealth (2010-present) and Cymantix (2018-present) His research focuses on multimedia information retrieval , personalization/user modeling , and cyberinfrastructure for research . Key contributions include: Development of neurophysiologically-informed information retrieval systems Creation of the Health Data Exchange platform for expert identification Advancements in SNOMED CT-based clinical cohort identification Editorial leadership as Editor-in-Chief of the Journal of the Association for Information Science and Technology Establishment of the Laboratory of Applied Informatics Research (LAIR) at U of T Mostafa's scientific awards include: Victor H. Yngve Endowed Professorship McColl Term Professorship His work spans interdisciplinary collaborations with grants like the Sponsored Research Agreement (2025-2026) for the Society 2025 project. He has authored over 105 peer-reviewed publications and contributed to policy development for health data systems in India, Malawi, and Bangladesh.

• Artificial Intelligence in Healthcare
• Physiological Signal Analysis
• Foundation Models
• +۴ مورد دیگر

Rita Alexandre Pinto Ribeiro is a Research Fellow at GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, within the School of Engineering at the Polytechnic Institute of Porto (ISEP). She is currently pursuing her Ph.D. in Artificial Intelligence and Intelligent Systems Engineering at the same institution, focusing on non-invasive health monitoring using wearable devices and physiological signals. Her educational background includes: Ph.D. in Artificial Intelligence and Intelligent Systems Engineering (in progress, 2024-2028), Polytechnic Institute of Porto, School of Engineering. Master's in Biomedical Engineering (2021-2023), Polytechnic Institute of Porto, School of Engineering, with a thesis on "Automatic anonymization of clinical text". Bachelor's in Medical Imaging and Radiotherapy (2016-2020), Polytechnic Institute of Porto, School of Health. Ribeiro's research centers on artificial intelligence in healthcare, particularly the analysis of physiological signals (like PPG) from wearables for health monitoring. She employs explainable and robust machine learning methods to develop clinical decision support systems. Her work spans foundation models, signal processing, and the application of AI to improve patient outcomes in areas such as blood pressure prediction, sleep analysis, and medication adherence. Her recent publications reflect a strong focus on wearable-enabled remote health monitoring, explainable AI in biosignal analysis, and computer vision for healthcare. These works collectively advance the integration of AI into clinical practice, emphasizing reliability and interpretability. As a Research Fellow, she contributes to international projects including RM4HEALTH (Remote Monitoring in Health and Sports) and REMO (Remote patient-targeted health monitoring to reduce clinical workload), funded by COMPETE2030-FEDER. These projects involve collaboration with academic and industrial partners to develop and evaluate machine learning models for health applications. Ribeiro is an active member of the GECAD research group, which fosters interdisciplinary work in intelligent engineering and computing for healthcare innovation. She also participates in international events such as the DeepLearn 2025 summer school and the EAIA summer school to stay at the forefront of AI advancements.

Marek Reiمشاهده پروفایل

دانشیار

• Machine Learning
• Natural Language Processing
• planning and reasoning with LLMs models
• +۴ مورد دیگر

Marek Rei is an Associate Professor of Machine Learning at Imperial College London and a Visiting Researcher at the University of Cambridge . He serves as an AI Advisor for Locai Labs and Esgrid Technologies , while providing consultancy through Perception Labs . PhD in Minimally supervised dependency-based methods for natural language processing (University of Cambridge, 2013) MPhil in Computer Speech, Text and Internet Technology (University of Cambridge Computer Lab, 2008-2009) Bachelor's in Creating a Model for Audiovisual Speech in Estonian (Tallinn University of Technology) His research spans Machine Learning and Natural Language Processing , focusing on planning and reasoning with LLMs , model explainability , medical data modeling , information extraction , and grammatical error correction . Recent publications highlight trends in LLM robustness , membership inference , medical NLP , and corporate sustainability analytics . Scientific accolades include the Best Paper Award at the IEEE Conference on Secure and Trustworthy Machine Learning (SaTML 2025) . Collaborative work with institutions like Microsoft (via SwiftKey acquisition) and contributions to neural sequence labeling frameworks demonstrate his technical and industry impact.