جست‌وجوی استاد و پژوهشگر

• Cardiovascular Imaging
• Translational Research
• Molecular Imaging
• +۹ مورد دیگر

Dr. Albert J. Sinusas is a Professor of Medicine (Cardiology) , Radiology & Biomedical Imaging , and Biomedical Engineering at Yale University . He serves as Director of the Yale Translational Research Imaging Center (Y-TRIC) and Advanced Cardiovascular Imaging at Yale New Haven Hospital. Education: BS from Rensselaer Polytechnic Institute (1979), MD from University of Vermont (1983), Internal Medicine training at University of Oklahoma (1986), Cardiology/Nuclear Cardiology at University of Virginia (1989) Dr. Sinusas specializes in non-invasive cardiovascular imaging with expertise in PET/CT, SPECT/CT, echocardiography, and MR imaging . His research focuses on molecular imaging of myocardial injury , angiogenesis , post-infarction remodeling , and deep learning applications in cardiac diagnostics. He has pioneered multimodality imaging approaches for cardiovascular pathophysiology assessment. Recent publications highlight his work in AI-driven cardiac imaging , novel PET tracers , and medical robotics . His team's 15 most recent articles (2024-2025) span topics from ARDS diagnostics to cardiovascular risk stratification using CT and PET technologies. Scientific Awards: SNMMI Hermann Blumgart Award (2008) Best Doctor in America (2001-2002, 2005-2015) M.A. Privatim from Yale (2006) Robert Wilkinson Lectureship (2014) Interurban Clinical Club membership (2017) As Principal Investigator on multiple NIH grants, Dr. Sinusas directs the NHLBI-funded T32 training program in multimodality cardiovascular imaging. His lab (Y-TRIC) houses state-of-the-art imaging resources including hybrid SPECT/CT , microCT , and 3D ultrasound systems for translational research from animal models to clinical applications.

Thomas Tie Luoمشاهده پروفایل

دانشیار

• Trustworthy Artificial Intelligence
• Explainable AI
• Adversarial and Robust Machine Learning
• +۶ مورد دیگر

Thomas Tie Luo is a tenured Associate Professor in the Department of Electrical and Computer Engineering and holds a courtesy joint appointment in the Department of Computer Science at the University of Kentucky, affiliated with the Stanley and Karen Pigman College of Engineering. He previously served as Associate Professor at Missouri University of Science and Technology and earned his PhD in Electrical and Computer Engineering from the National University of Singapore (ranked #8 globally by QS). His research focuses on Trustworthy Artificial Intelligence with applications in medicine, healthcare, and IoT, emphasizing Explainable AI (XAI) , Robust Machine Learning , and Privacy-Preserving Federated Learning . Education: PhD, Electrical and Computer Engineering, National University of Singapore (2009) His recent work explores Time Series Anomaly Detection , Secure Federated Learning for LEO Satellite Networks , and Medical Imaging Analysis through advanced deep learning architectures and adversarial attack mitigation. His research has been recognized with Best Paper Awards at ECAI'25, PAKDD'24, and PerCom'24, as well as a Best Student Paper Award at AAIM'18. Dr. Luo actively contributes to academic service as a Senior Member of IEEE, serving on editorial boards for journals like IEEE Transactions on Services Computing and Elsevier Ad Hoc Networks . He has advised PhD students in Computer Science, Electrical Engineering, and Computer Engineering, with graduates placed at institutions such as Washington State University and ByteDance.

Simone Paolo Ponzettoمشاهده پروفایل

استادیار

• Knowledge acquisition
• Natural Language Processing
• Semantic Web technologies
• +۵ مورد دیگر

Simone Paolo Ponzetto is an Assistant Professor (Juniorprofessor) at the University of Mannheim since 2013, affiliated with the Research Group Data and Web Science. His research focuses on Semantic Web technologies, Natural Language Processing (NLP), and knowledge acquisition, particularly leveraging collaboratively built resources like Wikipedia. Prior to Mannheim, he held postdoctoral roles at Sapienza University of Rome and research positions at the University of Heidelberg and Stuttgart. His work includes pioneering projects like BabelNet, a multilingual semantic network. Ponzetto earned his PhD in Computational Linguistics from the University of Stuttgart, with interdisciplinary contributions to coreference resolution, semantic relatedness, and ontology learning. Research Interests: Unsupervised/weakly-supervised knowledge extraction Multilingual ontology learning and semantic networks Lexical semantics (word sense disambiguation, semantic similarity) Discourse semantics (coreference resolution, coherence modeling) Professional Contributions: Guest editor for a Artificial Intelligence Journal special issue on AI and Wikipedia Area chair for EMNLP-CoNLL 2012 and EACL 2014 Program committee member for ACL, AAAI, and other top conferences Lab/Team: Active in the Research Group Data and Web Science at Mannheim, advancing AI and NLP applications in collaborative knowledge systems.

• AI Agents
• Multi-Agent AI
• Artificial Intelligence
• +۱۴ مورد دیگر

Saptarashmi Bandyopadhyay is a Tenure-Track Assistant Professor of Computer Science at the City College of New York and the Graduate Center at the City University of New York (CUNY). Her research focuses on Artificial Intelligence Agents and Autonomous Decision Making, with special emphasis on Multi-Agent Reinforcement Learning, Multi-Agent Imitation Learning, and related paradigms. She has established significant collaborations with leading institutions including Google DeepMind, Carnegie Mellon University, Oxford University, and MIT. Dr. Bandyopadhyay received her PhD from the University of Maryland, College Park, where she was advised by Professor John Dickerson and Professor Tom Goldstein. Prior to that, she graduated from Penn State in 2020 with a thesis on Multimodal Computer Vision in Medical Domain advised by Prof. William Evan Higgins. Her research expertise spans multiple domains of AI including Multi-Agent Systems, Reinforcement Learning, Imitation Learning, and Multimodal Perception. She specializes in developing AI agents for applications in climate conservation, economic systems, and AI safety. Her work integrates techniques from computer vision, natural language processing, and robotics to create more robust and explainable AI systems that can operate effectively in complex, real-world scenarios. Current work includes improving explainable AI, developing Multimodal LLM/VLM/Robotic Agents, and creating libraries to speed up Multi-Agent evolutionary training with JAXMARL. Analysis of Dr. Bandyopadhyay's publication record reveals a clear progression from foundational work in medical imaging and natural language processing toward increasingly sophisticated multi-agent AI systems. Her recent publications demonstrate a strong focus on Multi-Agent Reinforcement Learning frameworks like JAXMARL, with applications spanning from supply chain orchestration to climate conservation. The interdisciplinary nature of her work is evident in publications spanning computer vision, NLP, and multi-agent systems conferences including AAAI, NeurIPS, AAMAS, EMNLP, and ACL. DoGood Fellow (2022) UMD Dean's Summer Fellow (2021) Dr. Bandyopadhyay has been actively involved in securing research funding from major agencies including NSF, NIH, DoD, and ARL. She served as the lead PhD student RA in a DoD project for Multi-Agent Explainable AI to improve AI trustworthiness. Her service to the academic community includes membership on program committees for major conferences including IJCAI 2024, KDD 2024, ACL 2024, and AAMAS 2023-2024. She has also created the AI Agents Seminar Series at UMD in 2022 with over 1,000 participants from six continents. Currently, Dr. Bandyopadhyay leads research on improving explainable AI, developing Multimodal LLM/VLM/Robotic Agents, and creating libraries to speed up Multi-Agent evolutionary training with JAXMARL. Her lab collaborates prominently with researchers from Google DeepMind, Carnegie Mellon University, Oxford University, University of Sheffield, Waymo, Meta AI, and MIT, with special focus on Dr. Jakob Foerster's and Dr. Robert Loftin's groups.

Yali Jiaمشاهده پروفایل

استاد

• Optical Coherence Tomography
• OCT angiography
• Retinal imaging
• +۱ مورد دیگر

Yali Jia, PhD is Professor of Ophthalmology and Biomedical Engineering and Jennie P. Weeks Professor of Ophthalmology at Oregon Health & Science University (OHSU). She serves as associate director of the Center for Ophthalmic Optics & Lasers and co-founded the International Ocular Circulation Society. Recognized as a world-leading expert in advanced ophthalmic imaging, Dr. Jia pioneered functional optical coherence tomography (OCT) and OCT angiography (OCTA) technologies with over 180 peer-reviewed publications, 13,000+ citations (h-index=48), and six co-edited books. Her research centers on Optical Coherence Tomography, OCT angiography, retinal imaging, and artificial intelligence applications for ophthalmic diagnostics. Dr. Jia's revolutionary SSADA technique enabled clinical deployment of OCTA across 1,000+ international centers. Current work integrates AI with multimodal imaging for automated pathology detection, widefield pediatric applications, and quantitative perfusion analysis. Recent breakthroughs include OCT oximetry (PNAS, 2020) and deep learning models for diabetic retinopathy classification (PRER, 2021). Analysis of her 15 most recent publications (2023-2025) reveals dominant trends in AI-enhanced OCT/OCTA systems, with 80% incorporating machine learning for vascular segmentation and pathology detection. Research spans pediatric applications (Retinopathy of Prematurity), neuro-ophthalmology (MS/glaucoma differentiation), and widefield imaging, demonstrating consistent focus on clinical translation of imaging biomarkers. Dr. Jia's scientific honors include: Special Scholar Award from Research to Prevent Blindness Carl Camras Translation Research Award from ARVO Fellow of the American Institute for Medical and Biological Engineering Senior Member of the National Academy of Inventors As principal investigator on federal, foundation, and industry-sponsored grants, she has secured funding for clinical translation of OCTA with over 10 licensed patents. Her NIH-funded projects focus on AI-driven diagnostic systems and handheld pediatric OCT devices, emphasizing real-world clinical implementation. Leading the imaging research arm of OHSU's Center for Ophthalmic Optics & Lasers, Dr. Jia directs a multidisciplinary team developing next-generation OCT systems. Her lab specializes in projection-resolved angiography, oximetry, and multimodal platforms for neuronal-vascular imaging, with active collaborations across neurology, pediatrics, and biomedical engineering departments.

Gerhard Weberمشاهده پروفایل

استاد

• Accessible Computing
• Assistive Technologies
• Human-Computer Interaction
• +۴ مورد دیگر

Prof. Gerhard Weber holds the Chair in Human-Computer Interaction at Technische Universität Dresden, Germany. Previously, he served as Chair for Human-Centered Interfaces at Christian-Albrechts-Universität zu Kiel (2000–2007) and Professor for Operating Systems and Graphical User Interfaces at Harz University of Applied Sciences (1996–2000). His research focuses on accessible computing, assistive technologies, haptics, and multimodal interaction. Key projects include development of tactile charts (SVGPlott), robotic guidance systems (HapticRein), and indoor navigation solutions for visually impaired users. Current work explores voice interfaces for social robots, autism-inclusive technologies, and accessibility maturity models for higher education institutions. Over 70 publications span conferences like CHI, IEEE, and ACM, emphasizing practical applications in assistive tech. Education & Professional Journey: 2007–Present: Chair in Human-Computer Interaction, TU Dresden 2000–2007: Chair for Human-Centered Interfaces, Kiel University 1996–2000: Professor of Operating Systems and GUIs, Harz University Research Interests: Prof. Weber's work bridges theory and practice in accessibility, emphasizing tactile interfaces, inclusive design, and assistive robotics. Recent projects include: Mosaik : Enabling blind users to create and share graphics via audio-tactile tools Cloud4All : Personalized web accessibility solutions Range-IT : Real-time object detection for navigation aids Advising & Grants: Managed €3.2M in EU and national grants (2011–2020) Supervised 12+ graduate projects on assistive tech Labs & Teams: Leads TU Dresden's Human-Computer Interaction Lab, collaborating with industry partners like Siemens and rehabilitation centers to deploy assistive systems in real-world settings.

Tuukka Ruotsaloمشاهده پروفایل

دانشیار

• Brain-Computer Interfaces
• Human-Information Interaction
• Recommender Systems
• +۴ مورد دیگر

Tuukka Ruotsalo serves as Associate Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. His research bridges human cognition with computational systems through brain-computer interfaces and physiological computing. As Academy Research Fellow at University of Helsinki (2019-2024), he maintained dual institutional affiliations while leading cutting-edge work in neuro-linguistic modeling and affective relevance. His research focuses on brain-computer interfaces for information retrieval , where he pioneers methods to decode cognitive states from neural signals to improve search systems. Key areas include affective relevance modeling that integrates emotional states into search algorithms, and neuro-linguistic reconstruction that translates brain activity into language. His work on fairness-relevance tradeoffs in recommender systems established Pareto frontier evaluation frameworks now widely adopted in ethical AI research. Recent publications demonstrate how physiological signals like EEG and galvanic skin response can create more adaptive human-information interaction systems. Ruotsalo's scientific recognition includes the prestigious Academy Research Fellow position. His publications in IEEE Transactions on Human-Machine Systems , Journal of the Association for Information Science and Technology , and Communications Biology reveal growing interdisciplinary impact. His advising spans cognitive neuroscience and machine learning students, with notable collaborations across the SCIENCE AI Centre. Current projects include the TreeSense initiative for remote sensing of global tree resources and development of quantum-inspired neural architectures. His lab leverages the department's powerful compute cluster for large-scale physiological data analysis.

Fu Yunمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۱ مورد دیگر

Yun Fu is a Distinguished Professor at Northeastern University, affiliated with the College of Engineering and Khoury College of Computer Science. He holds tenure in Electrical and Computer Engineering (ECE). His roles include Professor, Senior Vice President at Shiseido Americas, founder of Giaran (acquired by Shiseido), and co-founder of TVision Insights. He earned his Ph.D. from the University of Illinois at Urbana-Champaign. His research focuses on artificial intelligence, computer vision, machine learning, and data mining. Key achievements include over 500 publications, 50+ patents, and prestigious awards like IEEE Fellow, OSA Fellow, and AAIA Fellow. He leads the SMILE Lab, exploring AI applications in vision, robotics, and healthcare. Notable entrepreneurship includes AI-driven ventures in cosmetics and media analytics. Research interests emphasize AI-driven solutions for computer vision challenges, including anomaly detection, trajectory prediction, and multimodal learning. His work bridges academia and industry, with impactful contributions to both fields.

Nikil Duttمشاهده پروفایل

استاد

• Embedded Systems
• Electronic Design Automation
• Computer Architecture
• +۶ مورد دیگر

Nikil Dutt is a Chancellor’s Professor at the University of California, Irvine (UCI), with academic appointments in Computer Science, Electrical Engineering and Computer Science (EECS), and Cognitive Sciences. He is affiliated with UCI's Center for Embedded Computer Systems (CECS), Center for Cognitive Neuroscience and Engineering (CENCE), Calit2, CPCC, and LUCI. His research focuses on embedded systems, electronic design automation, computer architecture, and healthcare IoT. Dutt has authored/co-authored seven books and holds IEEE Fellow and ACM Distinguished Scientist titles. Education: B.E. (Mechanical Engineering) from Birla Institute of Technology and Science (Pilani, India), 1980; M.S. (Computer Science) from Pennsylvania State University, 1983; Ph.D. (Computer Science) from University of Illinois at Urbana-Champaign, 1989. Research Interests: Embedded systems, brain-inspired architectures, neuromorphic computing, and healthcare IoT. Current projects include the Information Processing Factory (IPF) for autonomous systems and CareDex for disaster resilience in aging communities. Awards: Multiple Best Paper Awards, NSF grants, and fellowships. Serves as editor for ACM TECS, IEEE TVLSI, and former Editor-in-Chief of ACM TODAES. Active in academic service, including ESWEEK Steering Committee roles. Grants: NSF IPF, UNITE, CareDex, and industry partnerships (e.g., Facebook). Research addresses energy-efficient data centers, autonomous driving systems, and wearable health technologies. Labs/Teams: Leads the Dutt Research Group (DRG), focusing on self-aware systems, edge computing, and neuromorphic architectures.

• AI for Sound
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Mark Plumbley is a Professor of Signal Processing at the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering at the University of Surrey. He holds an EPSRC Fellowship in 'AI for Sound' and has led major research initiatives, including the DCASE challenges. His work focuses on AI-driven analysis of acoustic scenes and events, with contributions to machine learning, audio source separation, and sparse representations. Previously, he was Director of the Centre for Digital Music at Queen Mary University of London and Head of the School of Computer Science at Surrey. Education: PhD in Neural Networks (1991). Academic roles include Professorships at King’s College London (1991–2002) and Queen Mary University of London (2002–2014). Research spans audio event detection, sound scene classification, and generative AI for audio synthesis. He leads projects like the EPSRC-funded 'Making Sense of Sounds' and 'Musical Audio Repurposing using Source Separation', and co-edited the Springer book on Computational Analysis of Sound Scenes and Events. Research Interests: AI for Sound: Machine learning applied to real-world audio analysis. Acoustic Scene and Event Recognition: Developing models for sound classification and localization. Generative Audio Models: Text-to-audio systems and diffusion models for sound synthesis. Healthcare Applications: Audio-based diagnostics and bioacoustic signal processing. Grants and Awards: EPSRC Fellowships, EU-funded networks (SpaRTaN, MacSeNet), and Fellowships from IET and IEEE. Notable awards include the IEEE Young Author Best Paper Award (co-authored with students) and leadership in the DCASE community. Labs and Collaborations: CVSSP at Surrey, collaborations with BBC R&D, and interdisciplinary projects on urban soundscapes and noise pollution (UK Acoustics Network Plus).

Diego Klabjanمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Diego Klabjan is a Professor at Northwestern University within the Department of Industrial Engineering and Management Sciences. He serves as the Founding Director of the Master of Science in Machine Learning and Data Science Program and Director of the Center for Deep Learning. Ph.D. in Algorithms, Combinatorics, and Optimization from Georgia Institute of Technology (1999) B.S. in Applied Mathematics from University of Ljubljana (1994) His research focuses on machine learning, deep learning, and analytics with applications in finance, transportation, sports, and bioinformatics. Key contributions include federated learning algorithms, reinforcement learning for cryptocurrency trading, and neural network applications in impact mechanics. Recent publications highlight advancements in blockchain-based federated learning, second-order policy gradient convergence, and ensemble deep reinforcement learning. His work bridges theoretical foundations with industrial applications across diverse sectors. Preseren’s Award for the Best Undergraduate Thesis (1994) Transportation Science Section Dissertation Prize (2000) Intel's Outstanding Researcher Award (2019) Jack Meredith Best Paper Honorable Mention (2022) Klabjan has advised notable students including Luis Guimarães (2015 APDIO/IO Award winner) and Young Woong Park (2015 INFORMS Computing Society Best Student Paper recipient). His collaborations span Fortune 500 companies and startups in analytics-driven domains.

Manoj Karkeeمشاهده پروفایل

استاد

• Agricultural Automation
• Machine Vision
• AI in Agriculture
• +۳ مورد دیگر

Dr. Manoj Karkee is the Norman R. and Sharon R. Scott Professor of Agriculture and Life Sciences at Cornell University's Department of Biological and Environmental Engineering. He leads the #AgRobotics Lab, focusing on AI, machine vision, and robotics for smart farming. Previously, he directed the Washington State University Center for Precision and Automated Agricultural Systems. His expertise spans sensing systems, autonomous robotics, and agricultural automation. He holds a PhD in Agricultural Engineering and Human Computer Interaction from Iowa State University. Education: PhD (2009, Iowa State), MSc (2005, Asian Institute of Technology), B.Sc. (2002, Tribhuvan University). Research interests include precision agriculture, robotic harvesting, and AI-driven crop management. He has published widely in journals like Computers and Electronics in Agriculture and serves as Editor-in-Chief of that journal. Awards include the 2020 Rainbird Engineering Concept Award and recognition as a 2019 AI Pioneer. Lab work focuses on multi-purpose orchard robots, soft robotics for fruit harvesting, and AI-enhanced crop monitoring. Collaborations span global institutions and industry partners to develop scalable farming solutions. His research aims to enhance resource efficiency, crop yields, and sustainability in agriculture.

Engin Erzinمشاهده پروفایل

استاد

• Artificial Intelligence
• Multimedia Systems
• Computer Vision
• +۶ مورد دیگر

Engin Erzin is a Professor at Koç University's College of Engineering, leading the KUIS AI Lab and Multimedia, Vision and Graphics Lab . His research focuses on AI-driven human-centric systems, affective computing, and multimodal interaction analysis. He has contributed extensively to robotics, speech processing, and human-robot interaction through over 70 peer-reviewed publications since 2008. Research interests include: Affective computing and emotion recognition from speech/gestures Human-robot interaction and socially engaging agents Speech-driven animation and gesture synthesis Multimodal data fusion for interaction analysis Deep learning applications in robotics and biomedical engineering Recent work emphasizes: Developing adaptive pHRI controllers for manufacturing tasks Creating engagement measurement frameworks for human-machine interfaces Advancing Turkish speech recognition through self-supervised learning Designing multimodal databases for interaction studies Labs: KUIS AI Lab : Focuses on AI applications in robotics and human-computer interaction Multimedia Lab : Specializes in vision, graphics, and audiovisual analysis

• Computer Vision
• Extended Reality (XR)
• Artificial Intelligence
• +۷ مورد دیگر

Prof. Helmut Grabner is a Professor at the Zurich University of Applied Sciences (ZHAW), leading the Visual Intelligence and Applications Group and the Entrepreneurship initiatives within the School of Engineering. His work bridges computer science, medical technology, and visual communication, with a focus on Extended Reality (XR), surgical training simulations, and AI-driven decision making. Education: PhD in Computer Science (Graz University of Technology, 2008), Master's in Computer Science (2008), and a Certificate of Advanced Studies in Higher Education (ZHAW, 2021). Prior to academia, he held roles including CTO at Logitech and co-founder of upicto, applying computer vision in industry and startups. Research spans augmented reality medical training tools, NMR spectrum analysis via deep learning, and understanding visual engagement in advertising. Awards include the prestigious Koenderink Prize (2018) for contributions to computer vision. Projects include Immersive Education frameworks, bias-mitigation in venture capital algorithms, and surgical proficiency measurement systems. Teaching includes courses on Visual Computing, Machine Learning, and Deep Learning. His work integrates academic research with practical applications in healthcare, education, and entrepreneurship.

Slim Essidمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۷ مورد دیگر

Slim Essid is a Full Professor at Télécom Paris, leading the Audio Data Analysis and Signal Processing (ADASP) group. He holds a Doctorat (Ph.D.) and Habilitation from Université Pierre et Marie Curie (UPMC). With 15+ years of research experience, he has advised 15 PhD graduates and currently co-advises 10 others. His work focuses on machine learning, signal processing, and multimodal systems, publishing over 150 peer-reviewed papers. He serves as a reviewer for top journals/conferences (e.g., IEEE Transactions) and research funding agencies. Education: State Engineering Degree, École Nationale d’Ingénieurs de Tunis (2001) M.Sc. (D.E.A.) in Digital Communication Systems, École Nationale Supérieure des Télécommunications, Paris (2002) Ph.D., Université Pierre et Marie Curie (2005) Habilitation (HDR), UPMC (2015) Research Interests: Multimodal learning, self-supervised representations, audio-visual segmentation, music structure analysis, domain generalization, and speech enhancement. Recent publications highlight innovations like TACO (training-free sound-prompted segmentation) and CLOUDS (domain-generalized semantic segmentation framework using foundation models). His work bridges audio processing with vision and language models, emphasizing unsupervised/zero-shot approaches. Key achievements include state-of-the-art methods in sound event detection, speaker diarization, and music segmentation. He collaborates with 14 post-docs and leads projects funded by French/EU agencies.