جست‌وجوی استاد و پژوهشگر

• Neuroscience
• Machine Learning
• Neuroimaging
• +۴ مورد دیگر

Dustin Scheinost is an Associate Professor at Yale School of Medicine, affiliated with the Department of Radiology & Biomedical Imaging, Yale Child Study Center, Department of Statistics, and Yale Biomedical Imaging Institute. His research focuses on connectomics , machine learning , and neuroinformatics through the Multi-modal Imaging, Neuroinformatics, & Data Science (MINDS) Lab. Radiology & Biomedical Imaging (Primary) Child Study Center (Secondary) Statistics (Secondary) Wu Tsai Institute Yale Stress Center Research Interests include developing novel statistical and machine learning methods for functional connectivity in big neuroscience data, leading the BioImage Suite Web (BISWeb) platform, and advancing early life neuroimaging through the Fetal, Infant, Toddler Neuroimaging Group (FIT’NG). His work is supported by grants from NIMH, NIAA, NIDA, and NHLBI. Selected Scientific Contributions span functional connectivity in laterality preferences, anti-racist AI governance in psychiatry, self-citation trends in neuroscience, and predictive modeling of mood disorders. He collaborates extensively with Todd Constable and others on multimodal neuroimaging studies.

Oliver Nachtweyمشاهده پروفایل

استاد

• Social Modernization
• Individualization
• Digital Transformation
• +۵ مورد دیگر

Prof. Dr. Oliver Nachtwey is a Professor at the Department of Social Sciences , University of Basel, specializing in processes of social modernization, individualization, and the transformation of digital capitalism. His research spans political sociology, social conflict, authoritarianism, and the societal impacts of digitalization. University: University of Basel Department: Department of Social Sciences Academic Rank: Professor Nachtwey’s work focuses on the social effects of digitalization , including labor alienation, platform economies, and the political sociology of Corona protests . He explores how libertarian authoritarianism and regressive modernity shape contemporary social crises. His recent publications analyze digital labor alienation , gift exchange on platforms , and the spirit of digital capitalism . Articles emphasize keywords like algorithmic labor, surveillance capitalism, and techno-religion. Scientific Awards: Shortlisted for Leipzig Book Fair Prize (non-fiction) for Gekränkte Freiheit Recipient of multiple academic awards for Die Abstiegsgesellschaft Oliver Nachtwey’s research has been featured in The New York Times , The Guardian , and El Pais , with collaborations at institutions like the Hamburg Institute for Social Research and Institute for Social Research Frankfurt.

Ibrahim Khalilمشاهده پروفایل

استاد

• Security
• Privacy
• Federated Learning
• +۴ مورد دیگر

Professor Ibrahim Khalil is a faculty member in the School of Computing Technologies at RMIT University, Melbourne, Australia. He holds a PhD in Computer Science from the University of Bern (2003) and has extensive industry experience in Silicon Valley focusing on secure network protocols. His research spans Security, Privacy, Federated Learning, Blockchain, Quantum Computing, and Distributed Systems. He leads high-impact projects funded by ARC grants (DP250100582, DP220100215, etc.) and international initiatives like the EU’s SELFY project. His work addresses challenges in secure AI data analytics, privacy-preserving systems, and critical infrastructure protection. Khalil supervises PhD/Masters students on topics ranging from federated learning security to quantum-enhanced machine learning. Education: PhD in Computer Science (University of Bern, 2003); prior roles at EPFL, Osaka University, and industry tech hubs. Research Interests: Privacy-Preserving Technologies Blockchain Applications in Healthcare and Supply Chains Quantum Computing for Machine Learning Secure Edge Computing and Federated Learning IoT Security and Critical Infrastructure Protection Grants & Collaborations: Over 10 major grants since 2017, including ARC Discovery/Linkage Projects and international partnerships (QNRF, EU). Notable projects include Privacy-Aware Digital Twins for Critical Infrastructure and Federated Learning frameworks for GenAI models. Advising & Labs: Active supervisor of 25+ research projects since 2013, focusing on anomaly detection, secure data analytics, and blockchain-based systems. Collaborates with industry partners on defense and healthcare tech.

Virginia de Saمشاهده پروفایل

استاد

• Machine Learning
• Neuroscience
• Brain-Computer Interfaces
• +۵ مورد دیگر

Virginia de Sa is a Professor in the Department of Cognitive Science at the University of California, San Diego. Her research integrates computational modeling, psychophysics, and machine learning to investigate visual and multi-sensory perception, with a focus on understanding how humans learn and perceive through neural mechanisms. Her work emphasizes the synergy between human learning and machine learning, applying insights from both fields to advance understanding of perception. Notable projects include developing brain-computer interface (BCI) systems and analyzing biases in facial expression recognition algorithms. She leads the de Sa Lab, which explores the neural basis of learning through interdisciplinary methods, including EEG analysis and biologically inspired algorithms. Key research directions include improving BCI usability through adaptive spatial filtering, investigating pain assessment via facial and electrophysiological data fusion, and enhancing AI fairness in facial expression analysis. Dr. de Sa has contributed to grants such as the NSF-funded CHS project to enhance BCI reliability and collaborates on initiatives like AI-READI to improve healthcare data practices. Her lab’s BCI division focuses on interpreting EEG data for assistive technologies, while her work on divisive normalization bridges biological insights with artificial neural network design. Ongoing efforts explore zero-shot learning and the generalization of neural models to unseen tasks. Dr. de Sa’s interdisciplinary approach spans neuroscience, computer science, and engineering, with a commitment to advancing both theoretical understanding and practical applications in human-computer interaction.

Pierre Marionمشاهده پروفایل

پژوهشگر

• Deep Learning Theory
• Optimization of Neural Networks
• Generative Models
• +۲ مورد دیگر

Pierre Marion is a Researcher at INRIA Paris, working within the Sierra research team since September 2025. His work focuses on the theoretical foundations of deep learning and he is beginning to explore applications of AI in mathematics. Marion has established collaborations across multiple institutions including EPFL, Sorbonne Université, and Google DeepMind. His educational background includes: Engineering degree from École polytechnique (2015-2018) with specialization in Applied Mathematics Master's degree from Sorbonne Université (2019-2020) PhD from Sorbonne Université (2020-2023) under the supervision of Gérard Biau and Jean-Philippe Vert Postdoctoral research at EPFL (2024) supervised by Lénaïc Chizat Marion's research interests primarily focus on the theory of deep learning, where he investigates the optimization and statistical properties of various neural network architectures. His work spans from shallow networks to complex generative models, with a particular emphasis on understanding the mathematical foundations that govern deep learning performance. Recently, he has begun exploring applications of AI in mathematical research, aiming to bridge the gap between theoretical machine learning and mathematical discovery. His research often combines rigorous theoretical analysis with practical implications for training deep neural networks. Analysis of Marion's recent publications reveals several key trends in his research. He has made significant contributions to understanding the role of large learning rates in optimization dynamics, demonstrating how they can accelerate convergence in logistic regression and prevent memorization in score-based generative models. His work on attention mechanisms has provided theoretical guarantees for their effectiveness in specific tasks like single-location regression and clustering. Additionally, Marion has extensively studied the connections between residual networks and neural ordinary differential equations , establishing generalization bounds and exploring scaling properties in the large-depth regime. His earlier work included contributions to natural language processing and quasi-Monte Carlo methods, reflecting a broad mathematical foundation that informs his current deep learning research. Marion has received several notable scientific awards: Runner-up PhD Award of AFIA (French Association for Artificial Intelligence) in 2024 Google PhD Fellowship in 2022 Ecole polytechnique Grand Prize of Research Internships in 2018 As an advisor, Marion currently supervises PhD student Yu-Han Wu (since 2024), with whom he has co-authored multiple publications on large learning rates and denoising score matching. Previously, he co-supervised several Master's students including Seorim Park, Yerkin Yesbay, and Nathan Doumèche. Marion has been actively involved in the machine learning community through conference organization (NeurIPS@Paris meetups), session chairing (ICSDS 2022), and extensive reviewing activities. He has served as a reviewer for top journals including JASA and Annals of Statistics, and conferences including NeurIPS and ICLR, where he was recognized as a top reviewer at NeurIPS 2023. Marion is a member of the Sierra research team at INRIA Paris, which focuses on machine learning theory and applications. He has also collaborated with researchers at CREST (Center for Research in Economics and Statistics), as evidenced by his participation in seminars organized by Anna Korba and Karim Lounici. His work often bridges theoretical computer science, statistics, and applied mathematics, reflecting the interdisciplinary nature of modern machine learning research.

Victor R. Leeمشاهده پروفایل

دانشیار

• Data Science Education
• STEM Education
• K-12 Education
• +۴ مورد دیگر

Victor R. Lee serves as an Associate Professor at Stanford University's Graduate School of Education, with his office located at CERAS Building (520 Galvez Mall, Suite 531) in Stanford, California. He is actively affiliated with the Center for Studies in Education and Technology (CSET), where he conducts interdisciplinary research at the intersection of technology and learning. Dr. Lee holds a Ph.D. in Learning Sciences from Northwestern University and earned dual Bachelor's degrees in Cognitive Science and Mathematics from the University of California, San Diego. His academic trajectory bridges technical disciplines with educational research, establishing a foundation for his work in data-intensive learning environments. His research program centers on two interconnected domains: data literacy development in K-12 contexts and STEM education innovation across diverse learning spaces. He investigates how individuals make meaning from data during inquiry-based learning, with particular emphasis on self-collected student data and the epistemological challenges of data sense-making. Concurrently, his STEM education work spans traditional classrooms, makerspaces, computer labs, and school libraries, examining engaged learning practices and conceptual change in mathematics and science. Current projects focus on identifying the specialized knowledge teachers require to effectively scaffold student interactions with complex real-world datasets. Recent publications (2023-2024) reveal a strategic pivot toward artificial intelligence education, examining both teacher preparation and student understanding of AI systems. His work demonstrates consistent methodological rigor through design-based research, classroom implementations, and analysis of student reasoning patterns, particularly regarding how learners conceptualize algorithmic processes in platforms like YouTube. As a core faculty member within CSET, Dr. Lee collaborates with multidisciplinary teams to develop and evaluate educational interventions that bridge theoretical learning sciences with practical classroom applications, with recent emphasis on AI literacy tools and data-enabled pedagogical approaches.

Cheung Ngai-Manمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Augmented Intelligence
• Data Science
• +۴ مورد دیگر

Cheung Ngai-Man is an Associate Professor and Associate Head of Pillar (Education) at Singapore University of Technology and Design (SUTD), part of the Information Systems Technology and Design (ISTD) pillar. He holds a Ph.D. in Electrical Engineering from the University of Southern California (2008) and has held research positions at Stanford University, Texas Instruments, IBM, and others. His research focuses on image and signal processing, computer vision, machine learning, and artificial intelligence. Education: Ph.D., Electrical Engineering, University of Southern California (2008); Postdoctoral research at Stanford University (2009–2011). Research Interests: Develops algorithms for multimedia data processing, explores interdisciplinary applications of signal processing and AI, and addresses challenges in computer vision and generative models. Recent work includes fairness in generative models, few-shot image generation, and adversarial robustness. Publications: Over 100+ peer-reviewed papers in top venues (CVPR, NeurIPS, IEEE TIP, TPAMI) focusing on computer vision, generative models, and AI security. Notable 2023 work includes studies on label-only model inversion attacks and fairness metrics in generative systems. Awards: Best Paper Finalist (CVPR 2019), SAIL Award Finalist (WAIC 2019), Outstanding Associate Editor (IEEE T-MM), Croucher Foundation Fellowship. Students: Supervised postdocs (Hossein Nejati, Fang Lu), research assistants (Mohammad Rostami), and visiting students (Ma Rui). Labs/Teams: Leads research groups in AI, computer vision, and multimedia systems at SUTD. Has spun off AI initiatives for wound care and contributed to Singapore’s National AI Strategy.

Harrison Smithمشاهده پروفایل

مدرس

• Political Economy of Data Analytics
• Consumer Surveillance
• Smart Cities
• +۶ مورد دیگر

Dr Harrison Smith is a Lecturer in Digital Media & Society at the University of Sheffield's School of Sociological Studies, Politics and International Relations. He holds a PhD from the University of Toronto’s Faculty of Information, alongside MA and BA degrees in Sociology from Queen’s University, Canada. His research focuses on the political economy of data analytics, particularly in smart cities and consumer surveillance contexts. Education: PhD in Information Studies, University of Toronto (Canada) MA in Sociology, Queen’s University (Canada) BA (Hons) in Sociology, Queen’s University (Canada) Smith’s research examines how data infrastructures shape socio-economic inequality through processes like surveillance, classification, and market segmentation. Key areas include location-based marketing, 5G data infrastructures, and industry consolidation in data analytics. His work critiques how digital technologies reconfigure urban spaces and labor markets, particularly in 'smart city' contexts. Recent publications explore the metaverse's industrial implications, blockchain’s role in dispute resolution, and surveillance practices at music festivals. His teaching includes leading the module SCS2016: The Sociology of the Media. Smith’s expertise bridges media theory, urban informatics, and critical data studies, emphasizing ethical and political dimensions of digital technologies in everyday life.

• Internet Governance
• Intellectual Property Law
• Website Accessibility
• +۷ مورد دیگر

Professor Catherine Easton serves as Professor in Information Technology and Intellectual Property Law at Lancaster University's School of Law, with additional affiliations at Security Lancaster and the Centre for Law and Society. Her work bridges legal scholarship with practical technology implementation, focusing on digital inclusion and governance frameworks. Her research centers on internet governance, domain name regulation, intellectual property law, and accessibility for disabled users. She examines how legal frameworks interact with human-computer interaction systems, particularly in crisis response scenarios and educational technology. Her scholarship consistently addresses the tension between regulatory compliance and genuine digital inclusion, with special attention to the UN Convention on the Rights of Persons with Disabilities. Her recent publications reveal growing emphasis on autonomous systems regulation, particularly regarding disability access in driverless vehicles, and ethical frameworks for cloud-based disaster response. The scholarly trajectory shows evolution from foundational website accessibility analysis toward complex systems governance in emerging technologies. Higher Education Academy International Scholarship recipient MMU Promising Researcher Fellow (2011) Co-chair of UN Internet Governance Forum's Internet Rights and Principles Dynamic Coalition Treasurer of British and Irish Law, Education and Technology Association Guest editor for Web Journal of Current Legal Issues Disability Special Edition Professor Easton actively develops legal education technologies, having created interactive teaching resources for major textbooks and pioneered clicker technology applications in law classrooms. She leads initiatives like the National Law Student Forum and has presented extensively on MOOCs and legal pedagogy. Her Security Lancaster affiliation connects her work to broader research on information transparency and crisis response ethics, where she examines big data's implications for inclusion and human rights.

• Human-Computer Interaction
• Digital Health
• Mixed Reality
• +۴ مورد دیگر

Ryan Kelly is an Associate Professor in the School of Computing Technologies at RMIT University, specializing in Human-Computer Interaction (HCI), digital health, and mixed reality. His research focuses on technology-enabled social connection and wellbeing, including designing communication tools for close relationships, digital health initiatives like the Music-Attuned Care via eHealth (MATCH) project, and VR applications for mental health. He collaborates with industry partners such as Google, Netflix, and the Worldwide Fund for Nature, and has developed technologies for older adults, including simplified video calling systems for aged care during the pandemic. His work has been featured globally in media, including Al-Jazeera's documentary All Hail the Algorithm . He actively contributes to the HCI community as a reviewer and committee member for conferences like ACM CHI and CSCW, holding roles such as Editor for CSCW 2025 and Awards Co-Chair for CSCW 2023. Ryan supervises PhD candidates exploring human-centered AI for social connection and online safety for older adults. His research aligns with UN Sustainable Development Goals 3 (Good Health), 9 (Industry Innovation), and 11 (Sustainable Cities). Key projects include the VR mindfulness application used by over 200,000 Australians and the Painpad device for pain logging in UK hospitals. He advocates for participatory design frameworks, emphasizing ethical AI and technology’s role in fostering meaningful human experiences.

• Artificial Intelligence
• Legal Technology
• Dispute Resolution
• +۳ مورد دیگر

Professor John Zeleznikow is an Honorary Associate at La Trobe University's Law School, with a distinguished career spanning 49 years across multiple institutions including the University of Edinburgh and Victoria Business School. His research focuses on AI applications in legal decision-making, dispute resolution, and autonomous vehicle technology. He has secured over $8M in research grants and supervised 20 PhD graduates. Key projects include the Split-Up system (used in high-profile divorce cases like Prince Charles and Lady Di) and Family-Winner software, which won an ABC TV innovation award. His work bridges law, technology, and ethics, with publications in leading journals like the Harvard Negotiation Law Review and Artificial Intelligence and Law . Recent research explores AI-driven online dispute resolution (ODR), autonomous vehicle regulation, and the ethical implications of technology in legal systems. He advocates for transparent, user-centric legal tech solutions to enhance access to justice and improve decision-making processes.

James S. Duncanمشاهده پروفایل

استاد

• Biomedical Image Processing
• Quantitative Image Analysis
• Cardiac Function Analysis
• +۴ مورد دیگر

James S. Duncan is the Ebenezer K. Hunt Professor of Biomedical Engineering at Yale University, with additional appointments in Electrical & Computer Engineering and Radiology & Biomedical Imaging. His research focuses on biomedical image processing, quantitative image analysis using geometrical models, and applications in cardiac function and neuro-structure analysis. He has pioneered image-guided interventions and developed computational frameworks for medical imaging challenges. He holds a Ph.D. from the University of Southern California. His work integrates AI, deep learning, and statistical decision-making to advance medical imaging technologies. Notable contributions include advancements in 3D image segmentation, deformable models, and MRI-based tumor response assessment. Dr. Duncan has received prestigious awards, including IEEE Fellow (2001) and induction into the American Institute for Medical and Biological Engineering (2000). His recent research spans AI-driven hemodynamics modeling, trustworthy healthcare AI guidelines, and molecular MRI innovations in immunotherapy monitoring. He collaborates across disciplines to address challenges in cardiovascular, neuroimaging, and oncological applications.

Nicholas Vincentمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Responsible Artificial Intelligence
• Human-Centric Machine Learning
• +۳ مورد دیگر

Nicholas Vincent is an Assistant Professor in the School of Computing Science at Simon Fraser University. His research focuses on Human-Computer Interaction (HCI), Responsible Artificial Intelligence, and Human-Centric Machine Learning. He holds a Ph.D. in Technology and Social Behavior from Northwestern University (2022) and a B.Sc. in Electrical Engineering from UCLA (2016). Education: Ph.D., Northwestern University (2022); B.Sc., UCLA (2016) Affiliations: Simon Fraser University School of Computing Science His work examines the societal implications of AI, including data labor rights, algorithmic governance, and ethical design. Notable contributions include frameworks for measuring attentional agency on digital platforms and studies on collective action in tech contexts. He received the SIGCHI Outstanding Dissertation Award in 2024. Recent publications explore topics like AI ethics, governance of generative models, and Wikipedia's role in content ecosystems. He actively participates in policy discussions through media engagements and conference talks, advocating for public-oriented AI development.

• Medical Image Reconstruction
• Computed Tomography
• Positron Emission Tomography
• +۶ مورد دیگر

Professor Jerome Liang is a distinguished faculty member at Stony Brook University's Renaissance School of Medicine, holding professorships in Radiology, Biomedical Engineering, Electrical and Computer Engineering, and Computer Science. He serves as Co-Director of Radiology Research and has established himself as a leading expert in medical imaging reconstruction techniques. Dr. Liang's educational background includes a Ph.D. in Physics from City University of New York, postdoctoral training at Duke University, and fellowship at Albert Einstein College of Medicine. His undergraduate degree in Modern Physics was obtained from Lanzhou University in China. His primary research interests focus on advanced medical imaging techniques, particularly low-dose computed tomography image reconstruction, quantitative SPECT reconstruction, high-resolution PET imaging, tissue segmentation from multi-spectral images, computer-aided diagnosis systems, and virtual colonoscopy development. His work bridges engineering principles with clinical applications to improve diagnostic imaging capabilities while reducing radiation exposure. Analysis of his recent publications reveals a strong focus on machine learning applications in medical imaging, particularly in polyp classification, dual-energy CT spectral analysis, and virtual endoscopy. His research consistently aims to enhance diagnostic accuracy while optimizing radiation dose and improving visualization techniques for various medical conditions. 1981 China-US Physics Examination and Application Program (CUSPEA) Winner (Top 25 among 250,000 candidates) 1990 NIH First Investigator Award 1996 American Heart Association Established Investigator Award 1996 Radiological Society of North America Certificate of Merit Award 2002 SUNY Chancellor's Entrepreneur Award 2007 IEEE Society Fellow 2011-2013 SBU, BNL and CSHL Certificates of Excellence in Research and Invention 2013 Stony Brook School of Medicine Award for Excellence in Translational Research Dr. Liang has secured significant research funding including NIH/NCI R01 grants for "Advanced Virtual Colonoscopy for Early Cancer Screening" and "Radiogenomics of Colorectal Polyps." He currently leads active protocols including IRB 93995-MODCR005 focused on integrating virtual and optical colonoscopies with pathological analysis. His laboratory (IRIS - Imaging Research and Informatics) continues to advance medical imaging technology while mentoring the next generation of researchers in this critical field.

• Strategic Human Resource Management
• Employment Relations
• HR Digitization (AI Impact)
• +۳ مورد دیگر

Associate Professor Sunghoon Kim holds a position at the University of Sydney Business School's Discipline of Work and Organisational Studies. He previously taught at UNSW Business School, Cornell University, and Peking University. His PhD is from Cornell University, with MBA and BBA degrees from Seoul National University. His research focuses on strategic HRM, employment relations, and the impact of technological changes like AI on workplaces. He has edited major works including the Routledge Handbook of Human Resource Management in Asia and contributes to the APRU project on 21st-century work transformations. Kim serves as Associate Editor for journals like Human Resource Management and Asia Pacific Journal of Human Resources . His recent work addresses algorithmic management, employee well-being in AI contexts, and cross-border labor dynamics. He has advised students researching migrant worker choices and voice processes in organizations. Media engagements include commentary on workplace perks and work-life balance in South Korea. Teaching responsibilities include courses on performance management, strategic HRM, and international HR practices. His research highlights the intersection of technology, culture, and organizational sustainability.