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نمایش ۷۲۱-۷۳۵ از ۱۶٬۱۷۳ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
دانشیار
Dr. Reuben Binns is an Associate Professor of Human Centred Computing at the University of Oxford , where he investigates intersections between computer science, law, and philosophy. His research focuses on data protection , machine learning ethics , and regulation of technology .
استادیار
Dae-Jin Lee is an Assistant Professor at IE University’s School of Science and Technology, specializing in statistical modeling and data science. Previously, he served as a Research Line Leader at the Basque Centre for Applied Mathematics (BCAM) and coordinated the Knowledge Transfer Unit in Data Science/AI. His academic background includes a Ph.D. in Mathematical Engineering (2010) from Universidad Carlos III de Madrid and postdoctoral research at CSIRO (Australia). His research focuses on statistical methods for complex data, including penalized splines, tensor product smooths, and applications in biomedicine, epidemiology, environmental science, and sports analytics. He has led multidisciplinary projects funded by public and industry grants, collaborating globally with experts across fields like engineering, medicine, and biology. Key research themes include predictive modeling for health outcomes (e.g., SARS-CoV-2 pneumonia severity), sports injury prevention, and AI in healthcare. His work integrates machine learning with traditional statistical techniques, addressing real-world challenges like pedestrian dynamics simulations and automated medical diagnostics. He is actively involved in scientific organizations, including the Spanish Biostatistics Society and the Statistical Modelling Society. His recent publications highlight innovations in growth curve modeling, AI ethics, and spatiotemporal data analysis, reflecting his commitment to advancing both theoretical and applied statistics.
Tom Mitchell is the Fredkin Professor of AI and Learning and Director of the Center for Automated Learning and Discovery (CALD) at Carnegie Mellon University's School of Computer Science. His research focuses on machine learning, computational neuroscience, and their applications in neuroimaging and natural language processing. He is renowned for pioneering work in developing algorithms to decode brain activity and for contributions to foundational machine learning theory, including co-training and explanation-based learning. Mitchell authored the seminal textbook *Machine Learning* (McGraw Hill, 1997) and has led projects like Never-Ending Learning (NELL), an AI system that autonomously learns from web content. His work bridges computer science and cognitive science, exploring how machines can learn from data and human interaction. Notable research interests include brain-computer interfaces, automated knowledge extraction, and ethical AI. Mitchell's publications span influential journals like *Science* and *Nature*, and he has been recognized for advancing interdisciplinary research in AI and neuroscience. He has advised numerous students and contributed to initiatives like the AAAI Presidential Address on AI and brain sciences. Mitchell's current projects include studying the neural basis of language and developing AI tools for education and healthcare.
Yuval Noah Harari is a historian, philosopher, and lecturer at the Department of History at the Hebrew University of Jerusalem. Born in Haifa, Israel in 1976, he received his PhD from the University of Oxford in 2002. He is best known as the author of the internationally acclaimed books Sapiens: A Brief History of Humankind (2014), Homo Deus: A Brief History of Tomorrow (2016), 21 Lessons for the 21st Century (2018), Nexus: A Brief History of Information Networks from the Stone Age to Artificial Intelligence , and the Unstoppable Us children's series. Harari's research focuses on macro-historical questions including the relationship between history and biology, the essential difference between Homo sapiens and other animals, justice in history, historical directionality, human happiness throughout history, and the ethical questions raised by science and technology in the 21st century. His work bridges history, biology, philosophy, and economics, taking both macro and micro perspectives to understand not only what happened and why, but also how it felt for individuals. His books have sold over 45 million copies in 65 languages, with Sapiens alone selling 25 million copies since its publication. He is considered one of the world's most influential public intellectuals today. Harari has given keynote speeches at major international forums including the World Economic Forum in Davos and has presented as a digital avatar in a TED talk. Sapiens spent 96 consecutive weeks in the top 3 of the Sunday Times bestseller list Co-founded Sapienship, an international social impact company focused on education and storytelling, with his husband Itzik Yahav Regularly speaks at major international events on topics of technology, history, and the future of humanity Harari's recent work focuses on the challenges of the information age, particularly the crisis of truth and trust in an era of disinformation and artificial intelligence. He explores how humans can navigate the complex ethical questions raised by emerging technologies while maintaining democratic values and human dignity.
مدرس ارشد
PD Dr. Jannis Lennartz is a Senior Lecturer in Public Law at Humboldt University Berlin, specializing in constitutional law, administrative law, and digital legal issues. He holds a habilitation (2022) and has held visiting professorships at Göttingen and Cambridge. His research focuses on intersections of law and technology, fundamental rights in the digital age, and legal theory. He has published extensively on AI copyright challenges, algorithmic governance, and constitutional limits in administrative contexts. Education: BA Political Science (Erfurt, 2008), dual法学博士 (Göttingen/Berlin, 2013-2017), habilitation (Berlin, 2022). Key awards include the Leibniz Prize. Teaching includes Constitutional History (10 006), New Developments in Law (10 603), and General Administrative Law (10 450). Research highlights: Over 30 peer-reviewed articles since 2015 across constitutional law, intellectual property, and digital governance. Notable works include Chancellor Democracy (2023) and Forbidden Fruits? (2025), examining AI's impact on artistic creation and copyright frameworks. Recent publications address algorithmic filtering, digital trust mechanisms, and AI-generated content regulation. Awards: Leibniz Prize (specific details not provided but denotes Germany's top academic recognition). Active in interdisciplinary projects like LLCon and OSAIC initiatives. Serves as associated scientist under Prof. Möllers' chair, contributing to constitutional law research and academic leadership.
استادیار
Ian Hawkins is an Assistant Professor at the University of Alabama at Birmingham , focusing on Media Psychology , Intergroup Conflict , and Collective Action . He holds a Ph.D. in Communication and Media from the University of Michigan, with prior M.S. and B.S. in Psychology from Central Michigan University. His research employs social scientific methods to analyze how media representations of marginalized groups shape societal attitudes and policy preferences. Current work explores cross-device news consumption dynamics using eye-tracking to assess attention patterns in headline processing. He publishes in New Media and Society , Journal of Communication , and Psychology of Popular Media . Teaching responsibilities include Mass Communication History and Effects and Social Media Use courses. Contact: ihawkins@uab.edu
استادیار
Yu Meng is an Assistant Professor in the Department of Computer Science at the University of Virginia (UVA), part of the School of Engineering and Applied Science. He joined UVA in 2024 as a tenure-track faculty member. His research focuses on machine learning, natural language processing (NLP), and data mining, with recent emphasis on large language models (LLMs), alignment, reliability, and ethical AI development. Educated at the University of Illinois Urbana-Champaign (UIUC), Meng earned his Ph.D. in 2023 under advisor Jiawei Han. His doctoral thesis, Efficient and Effective Learning of Text Representations , received the ACM SIGKDD 2024 Dissertation Award. He also held a visiting researcher position at Princeton University under Danqi Chen and was a Google PhD Fellow. His work has been recognized with awards including the Superalignment Fast Grant from OpenAI and notable publications at venues like NeurIPS, ICLR, and ACL. Meng’s research explores topics such as preference optimization (SimPO), retrieval-augmented generation (InstructRAG), and zero-shot learning. He actively serves on program committees for top conferences (ICLR, ICML, NeurIPS) and as an action editor for Transactions of Machine Learning Research (TMLR) . He teaches graduate-level courses on NLP, emphasizing cutting-edge LLM topics like architecture design, instruction tuning, and ethical considerations. Key achievements include contributions to LLM alignment via retrieval optimization, efficient pretraining techniques, and foundational work on weakly supervised learning. His research bridges theory and practice, addressing both technical challenges and societal impacts of AI systems.
استادیار
Dr. Vivek Nallur is an Assistant Professor in the School of Computer Science at University College Dublin. His research focuses on Machine Ethics, Multi-Agent Systems, emergence in socio-technical systems, and decentralized adaptation mechanisms. He holds a PhD from the University of Birmingham and advanced certifications in university teaching from UCD. Education: BBS, Delhi University Post-Graduate Diploma in Advanced Software Technology, National Centre for Software Technology MS, Carnegie Mellon University PhD, University of Birmingham Prof Cert University Teaching & Learning, University College Dublin Research Interests: Machine Ethics explores ethical decision-making in autonomous systems, while Multi-Agent Systems (MAS) are his primary tool for studying self-adaptation and emergence. He serves on key committees like IEEE P7008 (Ethically Driven Nudging) and the AAAI 2021 Spring Symposium on AI Ethics. His work bridges computer science with philosophy, law, and social sciences. Advising & Grants: Supervises three PhD students focusing on ethical AI, elder-care robotics, and AI governance. Leads the Machine Ethics Research Group and participates in the ML-Labs SFI grant (2019–2027). Professional Roles: Senior Member of IEEE, organizer for AAAI ethics symposia, and contributor to OpenAAL's ELSI panel. Teaches modules on Machine Learning, Operating Systems, and Web Development.
Jimmy Huang is a Full Professor and Tier 1 York Research Chair in Big Data Analytics at the School of Information Technology, York University. His research focuses on information retrieval, AI, NLP, and big data analytics in healthcare and web systems. He has published 360+ papers in top venues like SIGIR and ACL, and leads grants totaling $4M+. Huang chairs IEEE's Technical Community on Intelligent Informatics and serves on numerous conference committees. Education: PhD in Information Science (City, University of London), M.Eng and B.Eng in Computer Science Roles: Chair of IEEE TCII, General Chair of SIGIR 2020 and CIKM 2008 Research interests span task-oriented IR, conversational search, healthcare analytics, and graph-based models. His work on hypergraph collaborative filtering (SIGIR 2022) was named a top influential paper. Current projects include NSERC Discovery Grants ($384K) and ADERSIM CREATE ($1.65M). Award highlights include Fellowships from ACM, IEEE, and Canadian Academy of Engineering. Supervised over 90 students, currently mentoring 12 PhD/MSc candidates and 3 postdocs. Active in surgical safety checklist research and medical data analytics. Labs include the IRLab focused on IR and NLP innovations. Major grants include ORF-RE ($3.5M), NSERC CREATE, and multiple CRD partnerships with industry.
Laura K. Nelson is an Associate Professor of Sociology at the University of British Columbia , where she also directs the Centre for Computational Social Science . Her work bridges computational methods with sociological inquiry, focusing on gender inequality, social movements, and organizational dynamics. She previously held faculty roles at Northeastern University and affiliated with institutions like the NULab for Texts, Maps, and Networks and the Network Science Institute . Education: PhD in Sociology (2014), University of California, Berkeley MA in Sociology (2009), University of California, Berkeley BA in Sociology (2006), University of Wisconsin-Madison (Phi Beta Kappa) Research Interests span computational sociology, social movement strategy, intersectionality, and STEM equity. She pioneered frameworks like computational grounded theory and radical objectivity , integrating machine learning with qualitative paradigms. Recent publications analyze gender dynamics in emergency medicine, feminist movement histories, and the NSF ADVANCE program’s impact on equity. Her 2024 Social Science Quarterly paper quantifies ADVANCE’s interdisciplinary reach. Awards include the 2020 Best Meta-Reviewer at SocInfo20 and Outstanding Faculty of the Year at Northeastern University. She serves on editorial boards for American Journal of Sociology , Poetics , and Acta Sociologica . She co-PIs a National Science Foundation grant studying gender-equity dissemination in higher education networks and supervises graduate student Jinyang Yu . Her lab, Centre for Computational Social Science , drives open-source methodological innovation.
استادیار
Ruixiang Tang is an Assistant Professor at Rutgers, The State University of New Jersey. His research focuses on artificial intelligence, machine learning, and natural language processing, with an emphasis on multimodal learning, model security, and ethical AI. He explores topics such as adversarial robustness, bias mitigation, and applications in healthcare and robotics. Key research interests include developing robust algorithms for vision-language models, analyzing model vulnerabilities like backdoors and hallucinations, and designing trustworthy AI systems. His work bridges theoretical advancements and practical applications, addressing challenges in healthcare data augmentation, copyright infringement detection, and cognitive reasoning. His recent publications highlight contributions to multimodal in-context learning, counterfactual reasoning benchmarks, and secure model optimization. Tang's research also intersects with fairness in AI, such as mitigating bias in NLP models and ensuring equitable outcomes in medical applications.
Alexandra BELIBOU is a PhD Lecturer at the Department of Music Performance and Pedagogy, Faculty of Music, Transilvania University of Brașov. Her work bridges musicology with modern interdisciplinary approaches, focusing on music education technology, therapeutic applications, and 20th-century compositional analysis. She holds a prominent position in advancing digital pedagogy through software integration. Research Interests: Music therapy methodologies emphasizing pluralistic approaches in clinical settings AI applications in music composition and education Analysis of 20th-century composers like Schoenberg, Reich, and Pärt Cultural studies of folk music traditions (e.g., Irish dance music, Romanian psalmic liturgy) Publications Trends: Recent work explores pandemic-era musician well-being, AI-assisted composition tools, and the intersection of music with medicine/physics. Earlier research delves deeply into psalmic text settings across centuries, minimalist techniques, and numerical symbolism in atonal music. Her teaching and research emphasize bridging traditional musicology with emerging technologies, as seen in proposals for software-based creativity workshops and interdisciplinary curricula.
پژوهشگر ارشد
Aileen Nielsen is a Ph.D. Candidate at ETH Zurich's Center for Law & Economics and a Fellow in Law & Tech. She holds a J.D. from Yale Law School, a B.A. in Anthropology from Princeton University, and advanced degrees in Applied Physics (Columbia) and Comparative Human Development (University of Chicago). Her research focuses on regulatory and judicial responses to technological innovation, particularly in AI governance, data privacy, and medical technology. She has practiced law in NYC and worked in tech startups across healthcare and political organizations. Education: Ph.D. Candidate (ETH Zurich), J.D. (Yale), M.S. Applied Physics (Columbia), M.A. Comparative Human Development (Chicago), B.A. Anthropology (Princeton) Her work combines empirical and experimental methods to address challenges like algorithmic fairness, AI liability in medicine, and public perceptions of data markets. Recent findings explore regulatory frameworks for AI systems and the ethical implications of algorithmic surveillance. She teaches courses on algorithms and fairness, law & tech research, and authentication security. Publications span law journals, cybersecurity white papers, and AI ethics conferences, with a focus on balancing innovation with societal accountability. Her books Practical Fairness and Practical Time Series Analysis further bridge technical and legal domains.
Meeyoung Cha is a Professor at KAIST and Scientific Director of the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. Her research focuses on Data Science for Humanity, encompassing computational social science, misinformation dynamics, and human-machine interaction. She holds a PhD in Computer Science from KAIST (2008) and previously served as Chief Investigator at the Institute for Basic Science and Visiting Professor at Facebook. Her work addresses societal challenges such as poverty mapping, fraud detection, and AI ethics. Key achievements include best paper awards and recognition like the Hong Jin-Ki Creator Award (2024) and Test-of-Time Awards (ACM IMC 2022, AAAI ICWSM 2020). Research interests span AI ethics, social media analysis, and interdisciplinary applications of machine learning. Notable projects include modeling climate risks via satellite imagery and analyzing chatbot interactions' societal impacts. She leads the MPI-SP's Data Science for Humanity Group, mentoring over 20 students across PhD and postdoc programs. Education: PhD in Computer Science (KAIST, 2008) Affiliations: MPI-SP (Germany), KAIST Key Awards: Hong Jin-Ki Creator Award, Korean Young Information Scientist Award, Test-of-Time Awards Her publications bridge computational methods with societal issues, including climate modeling, protein engineering, and algorithmic fairness. Current projects explore geospatial AI for economic development and ethical AI design frameworks.
دانشیار
Dr. Ting Hu is an Associate Professor in the School of Computing at Queen's University, affiliated with the Faculty of Arts and Science. She leads the Machine Intelligence & Biocomputing (MIB) Laboratory, focusing on bio-inspired AI and bioinformatics. Her research bridges evolutionary computing, machine learning, and biomedical data analysis. Dr. Hu holds a PhD in Computer Science from Memorial University and completed postdoctoral training at Dartmouth College. She teaches courses with strong student evaluations, winning the Howard Staveley Teaching Award (2019-2020) and recognition as a Mental Health Champion (2023). Education: B.Sc. in Computational Mathematics, Wuhan University M.Sc. in Computer Science, Wuhan University PhD in Computer Science, Memorial University Postdoctoral Fellowship, Geisel School of Medicine, Dartmouth College Research Interests: Evolutionary algorithms and genetic programming Interpretable and explainable AI Biomedical data mining (metabolomics, genomics) Complex network analysis Applications in precision medicine and disease prediction Awards & Recognition: Queen's AMS Undergraduate Mentorship Award (2025) IEEE CIBCB Best Student Award (2022) Howard Staveley Teaching Award (2019-2020) NSERC Discovery Grant Reviewer (2019) Memorial University's Best Professor Award (2016) Lab & Collaborations: MIB Lab develops tools like geneDRAGNN (graph neural networks for gene-disease prioritization) Active roles in IEEE Computational Intelligence Society and EuroGP Advances include vaccination strategies via graph-RL and interpretable clustering methods
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