جست‌وجوی استاد و پژوهشگر

• Data Science
• Speech Processing
• Natural Language Processing
• +۳ مورد دیگر

Dr. Yunting Yin is an Assistant Professor of Computer Science at Earlham College, located in Richmond, Indiana. She holds a Ph.D. from Stony Brook University (2024) and a B.S. from Pace University (2019). Her research focuses on speech processing, natural language processing, and machine learning, with applications in aging analysis, facial expression recognition, and large language model forecasting. She actively collaborates with students on innovative projects and contributes to interdisciplinary fields such as computational linguistics and data science. Education: Ph.D., Stony Brook University, 2024 B.S., Pace University, 2019 Research Interests: Speech Processing : Analyzing vocal attributes to estimate aging and mortality risk in veterans. Natural Language Processing : Exploring alignment between computational language models and classical dictionaries. Machine Learning : Developing forecasting systems using large language models (LLMs) for real-world event prediction. Publications reflect a blend of interdisciplinary work, spanning computational linguistics, multimedia analysis, and biochemistry. Recent trends emphasize aging studies and LLM applications. Her 2024 dissertation underscores the intersection of speech and language in gerontology. Professional memberships include IEEE and IEEE WIE. She advises students on research projects and fosters collaborative environments for academic growth. Labs/Teams: Her research projects engage students in cutting-edge areas like facial expression bias analysis, LLM-driven forecasting, and audio-linguistic studies of aging.

Stephanie Sicklerمشاهده پروفایل

دانشیار

• Design Education
• Material Studies
• Student Success
• +۲ مورد دیگر

Stephanie Sickler is an Associate Professor and Director of Undergraduate Programs in Florida State University's Department of Interior Architecture & Design. She holds an MFA in Interior Design and serves on the Interior Design Educators Council (IDEC) Board of Directors. Her research focuses on student success, material competencies, and participatory learning in design education. Her scholarly work explores innovative teaching methods including service-learning abroad, community engagement frameworks, and material-focused pedagogies. Awards include multiple IDEC Presidential Awards and presentation honors for research on remote work impacts and emotional intelligence in admissions. Current projects examine sensory translation of textiles and equity in material consumption. She authored Soft Goods Guidebook for Interiors (Bloomsbury, 2019) and is developing Material Box , a hands-on teaching resource.

• Music Cognition
• Auditory Perception
• Medical Alarm Design
• +۴ مورد دیگر

Dr. Michael Schutz is a Professor of Music Cognition/Percussion at McMaster University's School of the Arts, serving as University Scholar and founding director of the MAPLE Lab. His research bridges music performance/perception, focusing on auditory perception, medical alarm design, and cross-modal sensory integration. Supported by grants from CFI, NSERC, SSHRC, NIH, and others, his work appears in journals like Psychology of Music and British Journal of Anaesthesia . He actively contributes to the Percussive Arts Society and Society for Music Perception and Cognition, co-authoring influential textbooks and leading national percussion networks. Recent studies explore alarm redesign using musical principles and historic shifts in emotional expression across musical eras. His research has been featured on CBC's Quirks & Quarks , On The Nature of Things , and in textbooks including Cognitive Psychology and The Psychology of Music in Multimedia . He directs the McMaster Institute for Music and the Mind, emphasizing interdisciplinary connections between music, mind, and technology. Grants: CFI, NSERC, SSHRC, NIH, Early Researcher Award (Ontario) Key Roles: MAPLE Lab Director, SMPC Board Member (2014–2017) Public Engagement: Over 60 media features, 30+ invited performances, and collaborative research with Vanderbilt University Teaching includes courses on music cognition, percussion ensembles, and laboratory methods. His work has influenced anti-racist practices in music cognition research and ergonomic design of medical interfaces.

• Computer Vision
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Nathanael L. Baisa is a Lecturer in Artificial Intelligence at De Montfort University (DMU), part of the Computing, Engineering and Media faculty within the School of Computer Science and Informatics. His academic career includes roles as a senior research associate at Lancaster University, researcher at AnyVision, and research fellow at the University of Lincoln, focusing on computer vision and machine learning projects funded by ERC, EPSRC, and Innovate UK. Education: PhD in Electrical Engineering (Computer Vision/ML - Heriot-Watt University, 2018) MSc in Computer Vision and Robotics (Erasmus Mundus program, 2013) Research focuses on computer vision applications including object detection, scene understanding, autonomous systems, and biometrics. He leads work on deep learning for visual tracking, hand-based person identification, and robotic perception. He teaches courses like Introduction to Computer Vision and Deep Learning frameworks. His recent publications emphasize vision-language models, multi-object tracking algorithms, and geoscience machine learning applications. He collaborates with the Institute of Artificial Intelligence (IAI) and contributes to interdisciplinary projects in robotics and energy exploration.

Alaa Ahmedمشاهده پروفایل

استاد

• Neuroeconomics
• Motor Control
• Decision-Making
• +۳ مورد دیگر

Alaa Ahmed is a Professor in the Department of Integrative Physiology at the University of Colorado Boulder. She holds a PhD in Biomedical Engineering from the University of Michigan (2005) and completed postdoctoral training at the University of Michigan Medical School (NIH fellowship) and the University of Cambridge (Whitaker International Fellow). Her research explores the neuroeconomic principles underlying sensorimotor decision-making, integrating neuroscience, economics, and engineering. Notable awards include the NSF CAREER Award and DARPA Young Faculty Award. Her work investigates how the brain optimizes movement costs, effort, and rewards, with implications for aging, neurological disorders, and motor adaptation. Education: PhD, Biomedical Engineering, University of Michigan (2005) Postdoctoral Fellowships: NIH, University of Michigan Medical School; Whitaker International Fellow, University of Cambridge Research Focus: Neuroeconomics of movement control, sensorimotor decision-making, metabolic cost optimization, and aging-related motor adaptations. Dr. Ahmed’s studies highlight how economic utility principles govern movement vigor and learning. Her lab uses interdisciplinary methods to quantify subjective costs (e.g., metabolic, temporal) and their impact on motor behavior. Media features include Forbes, Wired, and PBS. She explores translational applications for rehabilitation and human-machine interfaces.

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۴ مورد دیگر

Rizwan Qureshi is an active researcher and academic specializing in artificial intelligence, machine learning, and their applications in medical imaging and bioinformatics. With a robust publication record spanning from 2017 to 2025, he has established himself as a significant contributor to the fields of computer vision and biomedical AI. His research interests focus on Artificial Intelligence , Machine Learning , Medical Imaging , Computer Vision , and Biomedical Engineering . Qureshi's work demonstrates particular expertise in object detection systems (especially YOLO variants), medical image segmentation, vision-language models, and applications of AI to healthcare problems including lung cancer research and diabetic retinopathy detection. Analysis of his recent publications (2023-2025) reveals a strong trend toward medical applications of AI, with approximately 60% of his work focusing on healthcare-related problems. His research shows increasing emphasis on model robustness, explainability, and handling distribution shifts in real-world applications. The publications span top venues including IEEE Access, IEEE Transactions on Medical Imaging, CVPR, and BIBM. Qureshi maintains extensive collaborations with researchers across multiple institutions, with frequent co-authorship with Hong Yan, Tanvir Alam, Jia Wu, and Sheheryar Khan. His work demonstrates both technical depth in machine learning methodologies and practical application to significant healthcare challenges. While specific details about his academic advising are not evident from the publication record alone, his numerous publications with multiple co-authors suggest active participation in research teams and likely supervision of graduate students. His work shows consistent funding support through publication in reputable journals and conferences.

Jacqueline Leybaertمشاهده پروفایل

استاد

• Multimodal language perception
• Deafness
• Hearing disabilities
• +۶ مورد دیگر

Jacqueline Leybaert is a Professor at the Université libre de Bruxelles (ULB) in the Faculty of Psychological and Educational Sciences. She is a leading researcher in the LCLD (Language, Cognition, Learning, and Development) laboratory, focusing on multimodal language perception, particularly in the context of deafness and hearing disabilities. Her work bridges psychology, linguistics, and audiology with significant implications for deaf education and communication strategies. Her research interests encompass multimodal language perception, audiovisual speech integration, deafness, hearing disabilities, orthographic learning, and numerical cognition. Dr. Leybaert has made substantial contributions to understanding how Cued Speech—a system that uses handshapes near the mouth to disambiguate speech sounds—can enhance language acquisition and speech perception for deaf and hard-of-hearing individuals. Her work explores the cognitive and perceptual mechanisms underlying language development in deaf children, particularly those using cochlear implants or Cued Speech. Analyzing her recent publications reveals a consistent focus on how multimodal information (auditory, visual, and manual cues) integrates in speech perception, especially in challenging listening conditions. Her research demonstrates how Cued Speech can improve speech recognition in noise, enhance orthographic learning, and support language development in deaf children. The interdisciplinary nature of her work connects cognitive psychology, linguistics, audiology, and educational practices for deaf individuals. Dr. Leybaert has supervised doctoral students, including Sabatier, E., whose dissertation focused on orthographic representations in deaf children. Her collaborative work extends to researchers such as Cécile Colin, Caron Jirschik, and Vilain, indicating strong research networks in deaf education and speech science. Though specific grant information isn't detailed in the provided text, her extensive publication record suggests significant research funding and project leadership. Her laboratory work centers on the LCLD lab at ULB, which appears to focus on language development, cognitive processes, and learning mechanisms, particularly in special populations including deaf and hard-of-hearing individuals. The lab's research employs various methodologies including behavioral experiments, eye-tracking, and potentially neuroimaging techniques to investigate language processing and development.

Tobias Langlotzمشاهده پروفایل

استاد

• Visual Computing
• Augmented Reality
• Virtual Reality
• +۲ مورد دیگر

Tobias Langlotz is a Professor at the Department of Computer Science, Aarhus University. His primary research focuses on Visual Computing, Augmented Reality, Virtual Reality, Human-Computer Interaction, and Augmented Human applications. He explores advanced vision augmentations and wearable technologies to enhance human perception and interaction with digital environments. His recent work emphasizes understanding visual discomfort in augmented reality systems, developing novel display technologies for mixed reality, and refining presence measurement methodologies in virtual environments. Key contributions include studies on presence questionnaires, neural rendering techniques, and hybrid layered displays. No scientific awards are explicitly listed in the provided materials. His research publications primarily address technical challenges in AR/VR systems and human-centric design principles. No advising or grant information is available in the current text. His work contributes to both academic and applied aspects of augmented and virtual reality technologies.

Els Lefeverمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۵ مورد دیگر

Els Lefever is an Associate Professor at Ghent University, where she works with the LT3 (Language and Translation Technology) research team. Her position focuses on computational linguistics and natural language processing research, with strong ties to both theoretical and applied aspects of language technology. Dr. Lefever earned her PhD in Computer Science from Ghent University in 2012 with her dissertation titled "ParaSense: Parallel Corpora for Word Sense Disambiguation." Her academic journey began as a computational linguist at the R&D department of Lernout & Hauspie Speech Products before transitioning to academia. Els Lefever's research spans multiple areas within computational linguistics with particular expertise in multilingual natural language processing. Her work focuses on computational semantics, cross-lingual word sense disambiguation, and multilingual terminology extraction. Recent research directions include automatic detection of irony in online text, argumentation mining in social media, sentiment analysis of financial news, language modeling for low-resourced languages, and computational approaches to Byzantine Greek epigrams. Her research demonstrates a consistent pattern of bridging theoretical computational linguistics with practical applications across diverse language domains and historical periods. Professor Lefever actively supervises PhD research on several cutting-edge topics including terminology extraction from comparable corpora, event extraction and sentiment mining of financial news, language modeling for low-resourced languages, argumentation mining in social media, and the automatic detection of links between Byzantine Greek epigrams. Her supervision portfolio demonstrates her commitment to advancing multiple frontiers of computational linguistics simultaneously. As an educator, Professor Lefever teaches courses in Terminology and Translation Technology, Language Technology, Localisation, Digital Text Analysis, and Digital Humanities. Her teaching reflects her research interests, providing students with both theoretical foundations and practical skills in language technology applications. The LT3 research group, where Professor Lefever is a key member, maintains strong connections with both academic and industry partners. The group has participated in numerous international conferences and shared tasks including SemEval competitions across multiple years, demonstrating consistent contributions to benchmark datasets and evaluation methodologies in natural language processing.

Stephen Mitroffمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Visual Cognition
• Attention
• +۵ مورد دیگر

Stephen Mitroff is a Professor of Cognitive Neuroscience at George Washington University’s Department of Psychological and Brain Sciences. He leads the Visual Cognition Lab, focusing on mechanisms of visual memory, perception, and attention. Prior to GW, he held faculty positions at Duke University (2005–2015) after completing postdoctoral research at Yale and a Ph.D. in Cognitive Psychology from Harvard University. His research emphasizes individual differences in visual search performance, leveraging big data from smartphone apps and professional populations like radiologists and TSA officers. Education: Ph.D. in Cognitive Psychology (Harvard, 2002), B.A. in Cognitive Science (UC Berkeley). Research explores how individual traits, training, and environmental factors influence visual cognition. Recent work includes pandemic-related mental health impacts, error dynamics in decision-making, and optimizing visual search for real-world applications. The lab employs innovative methodologies such as consistency-based training protocols and drift-diffusion modeling to dissect cognitive processes. Advising and grants: While specific grant details are not provided, his work has been supported by industry-academic partnerships, particularly in aviation security and medical diagnostics. The Visual Cognition Lab collaborates with professional groups to translate research into practical tools like sports vision training and baggage screening assessments. Labs/Teams: Principal Investigator of the GW Visual Cognition Lab, which integrates computational modeling with behavioral experiments to address applied cognitive science challenges.

Shahin Shahrampourمشاهده پروفایل

استادیار

• Machine Learning
• Optimization and Control
• Distributed Learning
• +۵ مورد دیگر

Shahin Shahrampour is an Assistant Professor in the Department of Mechanical & Industrial Engineering at Northeastern University, with a courtesy appointment in Electrical & Computer Engineering. He holds a Ph.D. in Electrical & Systems Engineering from the University of Pennsylvania, an M.A. in Statistics from The Wharton School, and a B.S. in Electrical Engineering from Sharif University of Technology. Prior to Northeastern, he was at Texas A&M University and held a postdoctoral fellowship at Harvard University. Research Interests: Machine learning, optimization and control, distributed and sequential learning, with a focus on computationally efficient methods for data analytics. Specific areas include manifold optimization, online and reinforcement learning, non-convex optimization, and statistical signal processing. His work bridges theory and applications in networked systems and dynamic environments. Awards & Grants: Received a $500,000 NSF grant (2023) for distributed optimization in non-convex environments. Best Paper Award at IEEE ICASSP 2022 for contributions to signal processing. TEES Engineering Genesis Award (2020) for multidisciplinary research at Texas A&M. Principal Investigator on NSF-funded projects in collaborative online optimization and decentralized learning. Labs & Teams: Leads research in machine learning and control systems at Northeastern University, focusing on interdisciplinary challenges in distributed optimization and real-time learning. His lab emphasizes theoretical foundations and practical implementations in networked and dynamic systems.

Yun Fuمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +۲ مورد دیگر

Yun Fu is a tenured Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a joint appointment in the Khoury College of Computer Science. He has established himself as a leading researcher in Artificial Intelligence, with over 500 publications in top-tier venues including IEEE/ACM transactions and major AI conferences. His work spans both theoretical foundations and practical applications, with significant impact in computer vision and machine learning. Professor Fu earned his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. His academic career progressed from Assistant Professor at SUNY Buffalo to his current position as tenured Professor at Northeastern University, where he has held appointments since 2012. His educational background includes a Beckman Graduate Fellowship at UIUC (2007-2008). His research focuses on advancing Artificial Intelligence with particular emphasis on Computer Vision, Pattern Recognition, and Machine Learning. His seminal work includes the "Residual Dense Network for Image Super-Resolution" presented at CVPR 2018, which was ranked among the Top 10 Most Influential CVPR papers. His research interests span image processing, anomaly detection, multimodal learning, and trajectory prediction, with applications ranging from healthcare to consumer technology. Analysis of his recent publications reveals a strong trend toward developing efficient and robust AI systems that bridge computer vision with language understanding. His work increasingly focuses on multimodal learning, trajectory prediction for multi-agent systems, anomaly detection in complex environments, and model validation techniques for black-box systems, while maintaining practical applications in real-world scenarios. Professor Fu's extensive recognition includes: Fellow of IEEE (2018), OSA (2019), SPIE (2018), IAPR (2016), AAIA (2021), and AAAI (2025) Member of Academia Europaea (2022) and European Academy of Sciences and Arts (2023) Fellow of National Academy of Inventors (2023) Multiple Young Investigator Awards from NAE, ONR, ARO, IEEE, ACM, and INNS 12 Best Paper Awards from major conferences Industrial Research Awards from Google, Amazon, Samsung, JPMorgan, and others Professor Fu has successfully mentored numerous Ph.D. students who now hold prominent positions in academia and industry at institutions including Amazon, Microsoft, Meta, Adobe, and major universities. His entrepreneurial ventures include founding Giaran (acquired by Shiseido in 2017) and co-founding TVision Insights, demonstrating his commitment to translating research into real-world impact. He has secured significant research funding from both government agencies and industry partners. As the PI and Founding Director of the SmiLe Lab at Northeastern University, Professor Fu leads a dynamic research group focused on advancing the state-of-the-art in AI and Computer Vision. The lab fosters interdisciplinary collaboration across computer science, electrical engineering, and applied mathematics, with ongoing projects in efficient deep learning, multimodal understanding, and practical AI applications.

Naeha Sharifمشاهده پروفایل

مدرس

• Computer Vision
• Natural Language Processing
• Medical Image Analysis
• +۵ مورد دیگر

Dr. Naeha Sharif is a Lecturer in Computer Science & Software Engineering at The University of Western Australia (UWA), specializing in Computer Vision (CV) and Natural Language Processing (NLP). She holds a PhD from UWA (2021) focused on AI-driven image captioning and received the prestigious ACS 1962 Medal for outstanding IT/CS research. Her work bridges AI with healthcare, including automated medical assessment via spine scans (DXA) for abdominal aortic calcification analysis and AI-driven medical report generation for fundus imaging. Education: PhD in Computer Science (UWA 2021), Master of Engineering in Biomedical Engineering (Kyung Hee University, 2013). Previous roles include Postdoctoral Fellow at Edith Cowan University (2021–2022). Research Interests: Vision-language models, medical image analysis, captioning metrics, multimodal learning, and AI applications in healthcare. Active in UN SDGs related to health and innovation. Teaching: Unit Coordinator for Computer Graphics & Animation (CITS3003, 2022–2023) Computational Thinking with Python (CITS1401, 2023) Grants: Lead investigator on the 'Generative AI for Screening of Depressive Disorders' project (2024). Awards: Recognized for student learning contributions (2025), best paper awards (2024/2020), and academic excellence honors (2021/2020). Labs/Teams: Member of UWA Natural & Technical Language Processing Group and IEEE Signal Processing Society.

• Reinforcement Learning
• Robotics
• Deep Learning
• +۳ مورد دیگر

Glen Berseth is an Associate Professor in the Department of Computer Science and Operations Research at the University of Montreal and a Senior Academic Fellow at Mila – Quebec Institute for Artificial Intelligence. He is also a Canada CIFAR Chair in AI and Co-Director of the Montreal Robotics and Integrative AI Laboratory (REAL). His work focuses on reinforcement learning, robotics, and deep learning applied to autonomous systems. He holds a postdoctoral background from Berkeley Artificial Intelligence Research (BAIR), working under Sergey Levine. His research emphasizes real-world applications, including human-robot collaboration, continual learning, and multi-agent systems. He teaches courses on robot learning at the University of Montreal and Mila, covering cutting-edge techniques for general-purpose robots. Key research interests include reinforcement learning for robotics, adaptive interfaces, and sim-to-real transfer. His recent work addresses challenges in autonomous learning systems, such as robust locomotion control and efficient exploration strategies. Notable awards include the Canada CIFAR AI Chair. He has supervised numerous students, including PhD candidates Ozgur Aslan and Siddarth Venkatraman, and Master’s students like Roger Creus-Castanyer and Léa Demeule, focusing on topics like reinforcement learning and robotic control. Berseth leads research projects funded by organizations like the CRSNG, FCI, and MITACS, addressing topics such as modular lifelong learning and generalization in robotics. His lab, REAL, explores embodied AI and robotics integration.

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Hady W. LAUW is an Associate Professor in the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU), serving as Director of the BSc (Computer Science) Programme and a Lee Kong Chian Fellow. He holds a PhD from Nanyang Technological University (2008). His research focuses on artificial intelligence, data science, machine learning, and recommender systems, with notable contributions to multimodal recommendation frameworks like Cornac and collaborative filtering techniques. He teaches advanced courses including IS712 Machine Learning for postgraduate students and CS608 Recommender Systems for MITB programme participants. His work emphasizes practical applications, such as designing explainable recommendation systems and integrating A/B testing into frameworks. Key research interests include web mining, preference learning, representation learning, and decision support systems. He has advised multiple students on topics like neural networks, collaborative filtering, and comparative analysis of reviews. Scientific achievements include the Lee Kong Chian Fellowship and over 100 publications in top venues like ACM WWW and IEEE TKDE. He actively contributes to conferences as a PC member and chairs events like PAKDD. His Cornac framework supports reproducible research in multimodal recommendations. He leads the Preferred.AI research group, fostering undergraduate and postgraduate research in computing. His work bridges theoretical advancements with industry applications, particularly in data-driven decision making and automated evaluation metrics.