جست‌وجوی استاد و پژوهشگر

Mohammad Rastegariمشاهده پروفایل

استادیار مدعو

• Computer Vision
• Efficient Machine Learning
• Model Compression
• +۳ مورد دیگر

Mohammad Rastegari serves as a senior technical manager in Apple's AI/ML organization and holds an affiliate assistant professor position in the Computer Science and Engineering Department at the University of Washington. Previously, he was a research scientist at the Allen Institute for AI (AI2) where he contributed to the PRIOR team, and co-founded XNOR.ai as Chief Technology Officer. He completed his Ph.D. at the University of Maryland under the supervision of Professor Larry S. Davis, focusing on computer vision and machine learning. His research centers on efficient deep learning with major contributions in binary neural networks (notably XNOR-Networks for resource-constrained devices) and model compression . Current work extends to large language model efficiency through weight clustering, token pruning, and memory optimization while maintaining strong foundations in computer vision. Analysis of his recent publications reveals a strategic shift toward optimizing transformer-based architectures across vision and language domains, with consistent emphasis on deployable solutions for edge computing and real-world applications. He co-founded XNOR.ai (acquired by Apple) and contributed to AI2's PRIOR initiative, demonstrating a career trajectory bridging academic innovation and industrial implementation in efficient AI systems.

Daqing Heمشاهده پروفایل

استاد

• Information Retrieval
• Natural Language Processing
• Health Informatics
• +۵ مورد دیگر

Daqing He is a Professor and Associate Chair of the Department of Informatics and Networked Systems (DINS) at the School of Computing and Information (SCI), University of Pittsburgh. He also holds appointments in the Intelligent Systems Program (ISP) and directs the Information Retrieval, Integration and Synthesis research lab. His academic journey began with a PhD from the University of Edinburgh, establishing a foundation for his distinguished career in information science. Dr. He's research focuses on information retrieval, natural language processing, adaptive web systems, and scholarly data management. His work bridges computer science and information science with practical applications in healthcare, education, and social information access. He has published over 300 articles in prestigious journals and conferences including Journal of the Association for Information Science and Technology, Information Processing and Management, and ACM SIGIR proceedings. His recent publications demonstrate a clear trajectory toward integrating advanced AI techniques with healthcare applications, particularly in clinical information retrieval, patient-centered recommender systems, and biomedical text processing. His work increasingly combines retrieval-augmented generation, large language models, and personalized health information systems to address real-world challenges in healthcare information access. ACM SIGIR CHIIR 2023 Best Short Paper Award ACM SIGIR CHIIR 2019 Best Poster Award iConference 2017 Best Poster Award ACM SIGIR CHIIR 2017 Best Student Paper Award iConference 2013 Best Paper Award Honorable Mention Dr. He has successfully mentored numerous PhD students who have become active researchers in information science. His research has been supported by significant grants from NIH, NSF, Amazon Research Award, and the University of Pittsburgh, including the $1.48 million NIH NLM-funded HELPeR project developing a personalized health information access system for patients. His lab maintains strong collaborations with healthcare institutions and technology companies to translate research into practical applications. His Information Retrieval, Integration and Synthesis lab brings together computer scientists, information scientists, and healthcare professionals to develop innovative solutions for information access challenges. Current projects focus on health information systems, clinical abbreviation resolution, and personalized learning technologies, reflecting his commitment to applying information science to improve human capabilities in information-rich environments.

Andreas Jemstedtمشاهده پروفایل

دانشیار

• Metacognition
• Memory
• Self-Regulated Learning
• +۴ مورد دیگر

Andreas Jemstedt is a Senior Lecturer and Associate Professor (docent) in Education at Stockholm University , affiliated with the Department of Education and the Higher Education Learning Practices (HELP) research group. His work bridges cognitive science and educational practice. Research Interests: Metacognition and self-regulated learning Memory acquisition and forgetting prevention Technology-enhanced learning and AI in education Second language acquisition in migration contexts Impact of sleep deprivation on cognitive performance Key Contributions: Developed Pure Question-Based Learning frameworks Created Two-Page Study Manual intervention Investigated psychological/social factors in language proficiency Explored metacognitive monitoring in sleep-deprived states

• Cognitive Modelling
• Artificial Intelligence
• Decision-Making
• +۳ مورد دیگر

Dr. Leendert van Maanen is an Associate Professor at the Department of Experimental Psychology, Utrecht University, where he studies cognitive mechanisms underlying information processing and decision-making. His research focuses on developing and validating computational cognitive models, with applications in human-machine interaction and cognitive neuroscience data analysis. Research Themes: Cognitive Modeling, Decision-Making, Human-Centered Artificial Intelligence, Applied Data Science Labs: Leads the COBRA (Cognitive and Behavioral Research in Artificial Intelligence) lab His work spans three levels of cognitive integration in AI: model development, human-machine deployment, and neural data analysis. Recent projects include cognitive strategy detection, digital twin user modeling, and neural event detection using multivariate time series. For collaborations and publications, see leendertvanmaanen.com or Utrecht University Research Portal.

• Experimental Psychology
• Psychophysics
• Visual Perception
• +۴ مورد دیگر

Chris Paffen is an Associate Professor in the Department of Psychology at Utrecht University's Faculty of Social and Behavioural Sciences. His position combines teaching, research, and management duties within the Experimental Psychology program. He is also associated with the Helmholtz Institute, focusing on experimental approaches to visual perception and cognition. Dr. Paffen received his PhD in 2004 and has since established himself as a prominent researcher in visual perception. His research spans multiple areas of experimental psychology with particular emphasis on visual perception, time perception, consciousness, and psychophysics. His recent work has expanded into human-centered artificial intelligence, examining how cognitive principles can inform AI development. Paffen's extensive publication record demonstrates consistent research productivity, with over 155 research outputs spanning two decades. His work primarily investigates visual working memory, binocular rivalry, attention mechanisms, and the relationship between visual perception and consciousness. Recent publications show increasing focus on memory dynamics, temporal processing, and the neural mechanisms underlying visual awareness. As an educator, Paffen teaches across multiple programs including Applied Cognitive Psychology, Artificial Intelligence, Psychology, and Neuroscience and Cognition at undergraduate, graduate, and post-master levels. His teaching portfolio includes courses such as Cognition and Behavior and Sensation and Perception, reflecting his research expertise. His laboratory work appears to focus on experimental paradigms related to visual perception, with particular attention to binocular rivalry, continuous flash suppression, and visual working memory paradigms. His research often involves collaborations with colleagues at Utrecht University and international institutions, as evidenced by his co-authored publications.

• Digital Forensics
• Natural Language Processing
• Artificial Intelligence in Law Enforcement
• +۳ مورد دیگر

Kyle Andrew Porter is a Researcher in the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU), Gjøvik campus. His work focuses on the intersection of digital forensics, natural language processing, and artificial intelligence applications in law enforcement contexts. Dr. Porter's research interests include: Digital forensics methodologies and techniques Natural language processing for criminal investigations AI regulation compliance in law enforcement Data science approaches to security challenges Human factors in digital evidence analysis Metadata extraction and filesystem analysis His publication history reveals a clear evolution from foundational technical work in digital forensics (2017-2018) toward increasingly applied research addressing real-world investigative challenges (2021-2025). Recent work demonstrates sophisticated integration of NLP and AI techniques to solve practical problems in criminal investigations while navigating regulatory landscapes like the AI Act. His research consistently addresses efficiency improvements, accuracy concerns, and cognitive factors in digital evidence processing. Dr. Porter teaches IMT4133 - Data Science for Security and Forensics, contributing to the development of future professionals in this specialized field. His educational contributions also include an Educational Guide published with John Wiley & Sons in 2022.

David H. Farbمشاهده پروفایل

استاد

• Molecular Neurobiology
• Pharmacology
• Cognitive Disorders
• +۷ مورد دیگر

Professor David H. Farb is affiliated with the Boston University School of Medicine , where he leads the Laboratory of Molecular Neurobiology . His research focuses on identifying pharmacological treatments for cognitive disorders such as Alzheimer’s Disease , utilizing In Vivo Electrophysiology and molecular genetic technologies. Education: Ph.D. from Brandeis University Professor Farb investigates hippocampal systems involved in pattern separation (encoding) and pattern completion (retrieval), which underlie cognition in mammals. His work addresses deficits in these systems linked to schizophrenia , autism , Alzheimer’s Disease , and aging, aiming to develop safer cognitive enhancers like neuroactive steroids. The Laboratory of Molecular Neurobiology employs a multidisciplinary approach, combining electrophysiology with behavioral and molecular methods to advance therapies for nervous system disorders.

Tomer Wolfsonمشاهده پروفایل

پژوهشگر ارشد

• Natural Language Processing
• Data Management
• Question Answering
• +۳ مورد دیگر

Tomer Wolfson is a Postdoctoral Research Fellow at the University of Pennsylvania and a member of the Cognitive Computation Group led by Prof. Dan Roth. He is also a CHE Postdoctoral Fellow for AI and Data Science, focusing on the intersection of Data Management and Natural Language Processing. PhD from Tel Aviv University (2019-2024), advised by Prof. Daniel Deutch and Prof. Jonathan Berant Research intern at Allen Institute for AI (2019-2024) BSc and MSc in computer science from Tel Aviv University Wolfson's research focuses on developing algorithms for understanding and reasoning over complex questions spanning hundreds of documents. His work includes creating benchmarks like MoNaCo and QAMPARI, analyzing language model reasoning paths, and improving retrieval-augmented generation systems. Key contributions include: MoNaCo: A benchmark for multi-document reasoning QAMPARI: Open-domain QA benchmark with multi-answer evaluation Break It Down: Question understanding benchmark Weakly supervised Text-to-SQL parsing systems His work combines natural language processing, machine learning, and database systems to enhance automated reasoning capabilities. Wolfson has received recognition for his contributions to retrieval-augmented language models and parametric knowledge analysis. Scientific awards include: CHE Postdoctoral Fellow for AI and Data Science Wolfson has served as a lecturer at Tel Aviv University for "Web Data Management & Information Retrieval" (2021-2022) and as a teaching assistant for "Web Data Management" (2018-2020). He is currently affiliated with the Cognitive Computation Group at the University of Pennsylvania.

Ana Belén Gil Gonzálezمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Educational Technology
• Data Mining
• +۴ مورد دیگر

Ana Belén Gil González is an Associate Professor in the Department of Computer Science and Automation at the University of Salamanca, Spain. She is affiliated with multiple research groups including BISITE (Bioinformatics, Intelligent Computer Systems and Educational Technology), CAUSAL (Academic Culture, Heritage and Social Memory), and MIDA (Data Mining). Her work spans across artificial intelligence, educational technology, blockchain applications, and intelligent systems. Dr. Gil González earned her PhD from the University of Salamanca in 2011 with a thesis on "Intelligent Recovery of Digital Educational Content," supervised by Dr. Juan Manuel Corchado Rodríguez and Dr. Sara Rodríguez González. Her research interests focus on applying artificial intelligence and data mining techniques to solve real-world problems. She has made significant contributions to educational technology, developing intelligent systems for digital content retrieval and personalized learning. In healthcare, she has worked on AI applications for medical diagnosis, including breast cancer prediction and Papanicolaou test analysis. Her work in blockchain technology addresses applications in supply chain management and smart cities with a focus on privacy preservation. Additionally, she has explored therapeutic robotics through projects like i-Teddy, which uses doll therapy to improve human interaction. Analysis of her recent publications (2022-2024) reveals a strong interdisciplinary approach with work spanning healthcare AI, educational technology, blockchain applications, and therapeutic robotics. Her research shows increasing focus on explainable AI in medical applications, privacy-preserving technologies for smart cities, and innovative approaches to educational technology. She maintains an active collaboration network across multiple research groups at the University of Salamanca. Dr. Gil González is actively involved in mentoring through her teaching and research supervision. Her extensive publication record across diverse domains suggests successful funding acquisition for her research projects. She is a key member of the BISITE research group, which focuses on bioinformatics, intelligent computer systems, and educational technology. Her work with the CAUSAL group explores academic culture and social memory, while her involvement with MIDA centers on data mining applications. These collaborative environments provide rich interdisciplinary contexts for her research across technology, education, and social applications.

• Machine Learning
• Ensemble Learning
• Dempster-Shafer Theory of Evidence
• +۹ مورد دیگر

Yaxin Bi is a Professor in the School of Computing at Ulster University and a core member of the Artificial Intelligence Research Centre, with over thirty years of research experience in AI applications. His work bridges theoretical advances in machine learning with real-world problem-solving across diverse domains. His educational background includes a BSc (Hons) in Computing Science, an MSc by research in Computing Application, and a PhD in Text Categorization with ensemble learning approaches. These qualifications established the foundation for his pioneering work in evidence theory and classifier systems. Professor Bi's research centers on machine learning and ensemble learning integrated with Dempster-Shafer theory of evidence, focusing on developing robust data analytics methods for decision-making under uncertainty. Key application areas include satellite data anomaly detection, manufacturing process optimization, energy systems analysis, agricultural monitoring through crop classification and disease detection, digital twin development, sentiment analysis, and multi-sensor activity recognition. His methodology emphasizes practical implementation of theoretical frameworks to address complex real-world challenges. Analysis of recent publications (2024-2025) reveals a strategic focus on high-impact applications of AI in critical infrastructure and environmental monitoring. Key trends include the convergence of deep learning (LSTM, GAN) with evidence theory for seismic prediction, optimization of industrial manufacturing processes, and advancement of information retrieval systems. These works consistently address uncertainty quantification and demonstrate cross-domain applicability from semiconductor fabrication to disaster response. His scientific recognition includes: Senior Fellow of the Higher Education Academy, UK Professor Bi has secured over £5 million in competitive research funding from major international bodies including the European Space Agency, EU Frameworks and Horizon 2020 programs, Knowledge Transfer Partnership, Invest NI, and the Royal Society. He actively shapes the academic community through editorial roles as Associate Editor for the International Journal of Intelligent Systems and editorial board membership for Real-World Data Science (Royal Statistical Society), alongside steering committee positions for KSEM and SAI conferences. His consultancy projects demonstrate direct industry translation of research. As a core member of Ulster University's Artificial Intelligence Research Centre, he leads initiatives in satellite data analytics, digital twin development, and AI-driven solutions for sustainable development goals, directing current projects including Electromagnetism Anomaly Detection and Seismic Deformation Monitoring through generative AI approaches.

Heiko Mausمشاهده پروفایل

پژوهشگر

• Knowledge Management
• Semantic Technologies
• Knowledge Graphs
• +۷ مورد دیگر

Dr. Heiko Maus is a researcher and department leader at the German Research Center for Artificial Intelligence (DFKI) GmbH in Kaiserslautern, Germany. As (co-)author of over 20 publications since 2005, he specializes in knowledge management, semantic technologies, and context-aware systems. Second Deputy Head of Smart Data & Knowledge Services Department (2022–) Team Leader of Topic Field Knowledge Work (2017–) Key projects: CoMem, EPOS, ForgetIT, myRPA His research focuses on knowledge graph applications for corporate memory systems, combining context modeling with semantic desktop environments . He develops personal knowledge assistants that integrate information silos through evolving knowledge graphs , with recent work exploring LLM-based adaptive relevance prediction for entity recommendation systems. Scientific contributions include the LDK 2019 Best Research Paper Award and multiple ACM/IEEE/ESWC publications . His 2024–2025 articles examine context-aware recommendation systems for knowledge workers, cyber mapping financial systems , and real-life knowledge work datasets .

Andrew Wikenheiserمشاهده پروفایل

استادیار

• Neuroscience
• Psychology
• Decision Making
• +۷ مورد دیگر

Andrew Wikenheiser is an Assistant Professor in the Department of Psychology at the David Geffen School of Medicine, University of California, Los Angeles . His research focuses on understanding how neural representations support decision-making processes through electrophysiological recordings of rat behavior, augmented with optogenetics and computational analysis. Current projects examine hippocampus-orbitofrontal cortex interactions Specializes in cognitive maps, abstract stimulus spaces, and neural ensemble dynamics Employs high-density electrophysiology and behavioral modeling Research Trends : Analysis of recent publications reveals a focus on decision-making mechanisms (78% of articles), neural circuit dynamics (65%), spatial cognition (45%), and behavioral neuroscience across 15 recent studies. Key methodologies include Neuropixels recordings , computational modeling , and optogenetic manipulation . Laboratory : Based in Pritzker Hall (8566), the lab combines chronic neural recordings with behavioral economics tasks to explore cognitive processes in rats. Their work emphasizes the importance of behavioral observation in understanding brain function.

Geraint A. Wigginsمشاهده پروفایل

استاد

• Computational Creativity
• Music Cognition
• Artificial Intelligence
• +۳ مورد دیگر

Geraint A. Wiggins is Professor of Computational Creativity at the Department of Computing, Goldsmiths' College, University of London. He leads the Intelligent Sound and Music Systems (ISMS) group within the Centre for Cognition, Computation and Culture. His academic career spans institutions including City University and the University of Edinburgh. He holds two PhDs: one in Computational Linguistics and another in Musical Composition from Edinburgh, supervised by Dr Graeme Ritchie and Prof Nigel Osborne respectively. MA (Cantab.), PhD (Edin.), PhD (Edin.), MIEE, MBCS Research interests include computational creativity, music cognition, statistical learning of harmonic movement, information dynamics of music, melodic segmentation, and cross-domain modeling between music and language. His work integrates artificial intelligence with music theory, perception, and composition. Key grants include EPSRC-funded projects on musical memory and similarity, Mellon Foundation collaborations on music encoding, and interdisciplinary studies with Queen Mary University and Indiana University. He has served on UK Technology Foresight Creative Digital Media Task Groups and chaired the SSAISB (UK AI/Cognitive Science society). Leadership roles encompass directing Goldsmiths' MSci scheme, serving as Associate Editor of Musicae Scientiae, and co-founding the AISB Journal. He has held external examiner roles at Queen Mary and Sussex, and been part of various university and departmental committees.

Faye E. Jonesمشاهده پروفایل

استاد بالینی

• Legal Research
• Privacy Law
• Cybersecurity Law
• +۳ مورد دیگر

Faye E. Jones serves as Clinical Professor of Law and Director of the Albert E. Jenner, Jr. Memorial Law Library at the University of Illinois College of Law, where she has worked since 2015. With over 30 years of experience in law librarianship and legal education, she develops faculty research support programs and expands legal research curricula while teaching Privacy and Cybersecurity Law, Advanced Legal Research, and Introduction to Legal Research. Her educational background includes a JD from Nova Southeastern University Center for the Study of Law, an MS in Library Science from Florida State University, and a BA cum laude in Library Science from the same institution. Professor Jones specializes in Legal Research methodologies , Privacy and Security Law frameworks , and Cybersecurity policy development . Her scholarship examines the intersection of technology and legal practice, with particular emphasis on data protection mechanisms and evolving digital research tools. This work bridges traditional legal scholarship with contemporary technological challenges facing legal institutions. Her publications span two decades (1998-2015), revealing a consistent focus on digital governance and legal information systems. Early work addressed foundational internet regulation (SPAM, search engines), while later research explored institutional management (library fundraising, intranet design) and privacy frameworks, demonstrating adaptation to technological evolution within legal contexts. Her professional recognition includes: Navy Achievement Medal for leadership as Commanding Officer of VTU-Law 2207 Professor Jones actively contributes to academic discourse through conference presentations, including the 2024 Popular Culture Association Annual Conference ("The Death and Life of Privacy in Contemporary Popular Music") and the NOVA Law International Conference on "Protecting children online." Her military service as a U.S. Navy Judge Advocate General Corps officer (retiring as Commander) complements her legal expertise, with admission to practice in Florida and before the U.S. Court of Appeals for the Armed Forces. As Library Director, she oversees all operations of the Albert E. Jenner Law Library, leading teams that support the College of Law's research and teaching missions while developing innovative digital resource management systems.

• Digital History
• British History
• History of Science and Technology
• +۷ مورد دیگر

Dr. Daniel C.S. Wilson is a Lecturer in Cultural Analytics & Knowledge Systems at University College London's Department of Information Studies, with an additional appointment as Honorary Research Fellow in UCL's Department of Science & Technology Studies. He holds a current Turing Research Fellowship at the Alan Turing Institute (2023-2025) and previously served as Senior Research Associate on the 'Living with Machines' project, a major collaboration between historians, curators, and data scientists exploring British industrialization through digital methods. Wilson's research focuses on the culture and politics of data and machines from the nineteenth century to the present, combining traditional archival study with computational techniques. His primary areas include digital history, British history, history of science and technology, and computational humanities, with specific interests in language, infrastructure, and analysis of large text and map collections. His current projects employ language models and computer vision to examine historical datasets including British Library newspaper collections and Ordnance Survey maps, with attention to bias, provenance, changing language, economic history, and spatial infrastructure development. Wilson's recent publication record demonstrates consistent output in digital humanities methodology, particularly in text and map analysis. His work shows a clear trajectory toward developing computational approaches for historical research, with increasing focus on addressing representativeness and bias in digitized collections. His research bridges humanities scholarship with data science, creating new methodologies for historical inquiry while critically examining the assumptions embedded in both historical sources and contemporary analytical techniques. Professional activities include Fellowship in the Royal Historical Society (since 2022), membership in the Association of Digital Humanities Organisations (since 2019), and the British Society for the History of Science (since 2009). He has served as a reviewer for ACM FAccT since 2022, demonstrating engagement with ethical considerations in algorithmic systems. Wilson teaches techniques and theory of text analysis for large collections 'as data,' metadata systems, and digital humanities to MA/MSc students. His educational background includes a PhD from the University of London, with additional research experience at institutions including the Science Museum Group, University of Cambridge (CRASSH), and EHESS in Paris. He is fluent in French and maintains active collaborations across academic and cultural heritage institutions.