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نمایش ۱۶-۳۰ از ۱٬۹۲۳ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Paola Cascante-Bonilla is an Assistant Professor in the Department of Computer Science at Stony Brook University, with expertise in computer vision, natural language processing, and embodied AI. Her research focuses on developing systems for compositional reasoning, common-sense inference, and trustworthy AI using vision-language models, while addressing cultural bias and explainability challenges.
Julian Jara-Ettinger is an Associate Professor of Psychology and Computer Science at Yale University. He holds a Ph.D. from MIT (2016). His research focuses on understanding the cognitive and computational mechanisms underlying human social behavior, including fairness, linguistic communication, gesture, moral reasoning, and pedagogy. He employs interdisciplinary methods such as computational modeling, eye-tracking, cross-cultural studies, and developmental research to bridge psychology and artificial intelligence. Key research areas include the development of social cognition in children, the integration of theory of mind with communication, and the application of cognitive science principles to build socially intelligent machines. His work emphasizes how humans infer others' knowledge, intentions, and desires, with implications for AI safety and ethical systems design. Publications span topics like epistemic inference, moral judgments, and the computational foundations of social interaction. His lab's research often intersects with evolutionary simulations, neural modeling, and cultural psychology. No scientific awards are explicitly mentioned in the provided text. Collaborations involve cross-disciplinary teams addressing challenges in developmental science, AI ethics, and cognitive robotics. His work has practical applications in educational strategies, social policy, and human-AI collaboration frameworks.
Pedro Neves is a Full Professor in the Department of Management and Organizations at Nova School of Business and Economics (Nova SBE), Universidade Nova de Lisboa. His academic background includes a PhD in Industrial and Organizational Psychology/Organizational Behavior from ISCTE-IUL and post-doctoral research at the University of Delaware. He teaches courses in Organizational Behavior, Persuasion and Negotiation, and Entrepreneurship, and actively contributes to executive education programs. His research focuses on leadership processes , particularly the dark side of leadership , interpersonal relationships , organizational change management (especially resistance anticipation), occupational health (including stress perception), and entrepreneurial behavior . His work appears in leading journals such as the Journal of Applied Psychology , The Leadership Quarterly , and Human Resource Management . His recent publications show a consistent focus on leadership dynamics, organizational change, employee well-being, and ethical behavior, often using multi-level and cross-cultural frameworks. He has also edited key volumes such as the Handbook of Methods in Leadership Research and the Research Handbook on Destructive Leadership . He currently serves as Associate Editor of the Journal of Applied Behavioral Science and is on the Editorial Board of The Leadership Quarterly and other journals, reflecting his scholarly impact. Pedro Neves has collaborated with doctoral students and researchers globally and has contributed to both theoretical and applied aspects of organizational behavior. He has not received explicitly mentioned scientific awards in the text, but his editorial roles and publication record indicate significant recognition in the field. He is involved in the Leadership for Impact Knowledge Center and contributes to open and customized executive education programs at Nova SBE.
دانشیار
Rui Li is an Associate Professor in the Ph.D. program at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. She directs the Lab for Use-inspired Computational Intelligence (LUCI), focusing on AI applications in computational biology and medical imaging. Education includes: B.Sc. in Computer Science, Harbin Institute of Technology M.Sc. in Computer Science, Tianjin University of Technology Ph.D. in Computing and Information Sciences, RIT Research integrates statistical machine learning with computational biology, medical image analysis, and human visual attention modeling. Current projects include deep learning for histopathology, multimodal medical image registration, and gene network inference. Publications demonstrate consistent focus on medical AI applications, with recent advances in unsupervised image registration, interactive segmentation, and multimodal fusion techniques. Key trends include self-supervised learning, uncertainty-aware models, and human-AI collaboration frameworks. Awards include the NSF CAREER Award for developing adaptive machine intelligence systems. Advises multiple PhD students on projects spanning deep learning architectures, biomedical image analysis, and biological network modeling. Leads several NSF-funded projects including human-centered image understanding systems and gene-protein network inference tools. Directs LUCI lab investigating machine learning for healthcare applications and teaches graduate courses in Statistical Machine Learning and Deep Learning.
Assoc. Prof. Dr. Sema Alaçam Doğan has been affiliated with Istanbul Technical University since 2014, serving as an Associate Professor in the Department of Architecture . She has held administrative roles including Deputy Head of Department and Erasmus Coordinator. Education : PhD in Informatics in Architectural Design (2008-2014), MS in Informatics in Architectural Design (2005-2008), and BS in Architecture (1999-2005) from Istanbul Technical University. Her research explores Computational Design , Artificial Intelligence in Architecture , and Sustainable Material Innovation . She investigates digital tools for heritage preservation, daylight optimization in BIM, and cognitive development in architecture students. Recent publications analyze AI-assisted design literacy , machine learning for Sinan mosques , and environmental comfort in Harran houses . Her work integrates algorithmic frameworks with sustainable practices. Scientific awards include multiple ITU Publication and Performance Awards (2021-2024), FABFEST Prizes , and the 2024 Artemis Educator Award from NASA. Active projects like "Physical Computation in Architectural Drawing" and "Robotic Fabrication with Recycled Wind Turbine Blades" demonstrate her leadership in computational and sustainable research.
Naoki Yoshinaga is a tenured Associate Professor at the Institute of Industrial Science, The University of Tokyo, with extensive experience in natural language processing and computational linguistics. He has held academic positions since 2008 and currently leads research on pragmatic NLP models and multilingual systems. PhD in Computer Science, The University of Tokyo (2005-2008) MSc in Information Science (2000-2002) BSc in Information Science (1996-2000) His research focuses on mechanistic interpretability in NLP models, multilingual/multimodal NLP , and efficient model design using trie structures and conjunctive features. He also investigates knowledge acquisition from social data and evaluation metrics for language generation . Recent publications include work on neuron empirical gradient analysis (ACL-25), multilingual knowledge representation (EACL-24), and compact embedding methods (CoNLL-24). His research has been funded by multiple grants, including the University of Tokyo Excellent Young Researcher program and JSPS fellowships. Committee Special Award, Association for NLP (2023) JSAI SIG Research Award (2022) Best Interactive Award, DEIM Forum (2019, 2016) He developed widely-adopted NLP tools like pecco (fast classification library), RenTAL (LTAG-to-HPSG grammar converter), and J.DepP (Japanese dependency parser). His lab emphasizes strong equivalence in formalism comparisons and pragmatic model design .
Heng Ji is a Professor at the Siebel School of Computing and Data Science , affiliated with the Department of Computer Science , Electrical and Computer Engineering Department , and multiple research labs including the Coordinated Science Laboratory and Carl R. Woese Institute for Genomic Biology at the University of Illinois Urbana-Champaign. She serves as an Amazon Scholar and Founding Director of the Amazon-Illinois Center on AI for Interactive Conversational Experiences (AICE) and CapitalOne-Illinois Center on AI Safety and Knowledge Systems (ASKS) . B.A. and M.A. in Computational Linguistics from Tsinghua University M.S. and Ph.D. in Computer Science from New York University Her research bridges Natural Language Processing with Vision-Language Models , Knowledge-Enhanced LLMs , and AI for Science (e.g., chemical language modeling). She leads major multi-institutional projects such as DARPA ECOLE MIRACLE , KAIROS RESIN , and DEFT Tinker Bell , while advising governments (U.S. Air Force Data Analytics Expert Panel) and industry (Amazon, Google, IBM). Her work on multimodal reasoning, agent-based systems, and chemical language models (e.g., mCLM ) has been supported by NSF, DARPA, and corporate partners. Recent publications (2025) focus on LLM agents , vision-language integration , and scientific knowledge acquisition . Awards include NSF CAREER , IEEE Intelligent Systems' AI's 10 to Watch , and multiple Outstanding Paper Awards at ACL/NAACL. She advises students like Chi Han (ACL/NAACL awardee) and post-docs Xiusi Chen and Yuji Zhang , and leads the BLENDER Lab , which develops frameworks like WiNELL (Wikipedia updating) and ProteinZero (protein generation). She has also served as NAACL Secretary and Program Co-Chair for ACL-IJCNLP2022. Outstanding Paper Award at ACL2024 Two Outstanding Paper Awards at NAACL2024 Young Scientist by World Laureates Association (2023-2024) AI's 10 to Watch by IEEE (2013) NSF CAREER (2009) Google/IBM/Bosch Research Awards
Federica De Stefano is an Associate Professor of Organisation Studies at Saïd Business School, University of Oxford, and a Research Fellow of Green Templeton College. She serves on editorial boards for Organization Science , Human Resource Management , and Human Resource Management Review , while holding roles in the Strategic Management Society (SMS). Previously, she was an Assistant Professor at HEC Paris and a Post-Doctoral Fellow at Wharton People Analytics. She earned a PhD from Bocconi University, with additional degrees from Fudan University and Bocconi. Her research focuses on people management's impact on organizational and individual outcomes, including employee mobility, workplace safety, and social sustainability. Using quantitative methods and large datasets, her work has been published in top journals like Academy of Management Journal and Strategic Management Journal . She has received the Marie Curie Individual Fellowship and recognition as a top 40-under-40 MBA professor. Teaching focuses on people analytics and management, with a 2023 completion of the International Teachers Program (ITP). Her research grants and collaborative work at Wharton People Analytics underscore her commitment to advancing strategic human capital practices. Federica's work bridges academic rigor with real-world organizational challenges, emphasizing ethical and sustainable workforce strategies.
Melissa Koenig serves as Professor and Director of Graduate Studies at the University of Minnesota's Institute of Child Development (ICD), where her research examines social and testimonial learning mechanisms in children through investigations of trust, cultural learning, memory, language, and cognitive development. As a first-generation college student, she prioritizes supporting students navigating academic systems through collaborative mentorship. Education PhD (2002), University of Texas at Austin Her research program spans cognitive development, cross-cultural studies, early childhood, language acquisition, theory of mind, and social-emotional development across peer, parent, and romantic contexts. The lab employs interdisciplinary, multi-method approaches with explicit cultural analysis to advance developmental science, particularly examining how children evaluate information sources and integrate testimony into moral frameworks. Analysis of her 2021-2024 publications reveals dominant themes in epistemic trust development, including how children assess informant credibility through moral behavior, group affiliation, and cultural context. Key trajectories show increasing sophistication in distinguishing epistemic versus practical reasoning, cross-cultural variations in trust attribution, and the developmental emergence of resistance to power-based corruption like bribery. Advising & Mentorship Accepts new PhD students for Fall 2026 cohort Requires prospective students to contact prior to December 1 application deadline Public advising statement details mutual expectations and support structures Specializes in guiding students through academic navigation and hidden curriculum challenges She directs the Early Language and Experience Lab, which maintains a collaborative research environment focused on uncovering constraints and supports for children's social learning across diverse contexts, with particular attention to cultural and socioeconomic variables in developmental processes.
Professor Eero Vaara, currently at Saïd Business School, University of Oxford, is a globally recognized expert in organizational theory, strategic change, and institutional dynamics. With 39 publications in Financial Times top 50 journals between 2008-2022, his work explores historical perspectives, discursive processes, and paradoxes in organizational transformations. Affiliation: Saïd Business School, University of Oxford Research Focus: Strategic change, institutional work, narrative theory, and historical embeddedness Eero’s research bridges macro-institutional and micro-practice approaches, emphasizing how unmaterialized decisions, discursive struggles, and temporal dynamics shape organizational trajectories. His work spans multinational corporations, public sector governance, and extreme contexts, with a growing interest in historical analysis and critical discourse studies. His recent publications highlight themes such as strategy-as-practice , national identity , and temporal intentionality . Articles like Near-histories and strategy emergence and Discursive legitimation demonstrate his interdisciplinary approach combining institutional theory, paradox theory, and narrative analysis. Scientific Awards: Academy of Management fellowship Ranked 11th most published management scholar in FT top 50 journals (2008-2022) Eero’s collaborative research style and resilience in the publication process have led to influential contributions in strategic management and organizational studies. His colleague Eric Zhao notes that his work “opens up new ways of thinking about organizations, strategy, and change,” reflecting Oxford Saïd’s commitment to research excellence.
Song Ma is a Professor of Finance and Entrepreneurship at Yale School of Management and a Faculty Research Fellow at the National Bureau of Economic Research (NBER). He is also an affiliated faculty member at Yale Law School Center for the Study of Corporate Law and Yale SOM Program on Entrepreneurship, having joined Yale SOM Faculty in 2016. His educational background includes: PhD in Finance from Duke University's Fuqua School of Business (2016) BA in Economics from Zhejiang University (2010) Professor Ma's research primarily focuses on innovation economics, entrepreneurship, financial economics, AI, and big data. His work extends to corporate strategy, industrial organization, antitrust, labor, and business law. He has made significant contributions to understanding how innovation interacts with financial markets, corporate strategy, and competition policy, particularly through his influential 'Killer Acquisitions' paper which has been cited in Congressional antitrust reports and lawsuits against major tech companies. His recent publications demonstrate an interdisciplinary approach combining finance, economics, and data science methodologies. Many papers examine the intersection of innovation and corporate finance, with increasing incorporation of AI and big data techniques as seen in his video analysis research. His work shows evolution from traditional finance topics toward more policy-relevant research with real-world impact on antitrust regulation and innovation policy. Professor Ma has received numerous prestigious awards: 2023 Best Paper Award, China International Conference in Finance 2022 Best Paper on Competition Economics, Association of Competition Economics 2022 Jerry S. Cohen Award for Antitrust Scholarship 2021 GARP Best Paper in Risk Management Award 40 Under 40 Best Business School Professors by Poets & Quants (2021) Robert F. Lanzillotti Prize for Antitrust Economics (2020) Jensen Prize for Best Paper on Corporate Finance (2019) In teaching, Professor Ma delivers popular courses including 'Entrepreneurial Finance,' 'Venture Capital and Private Equity,' and 'Finance and the Society.' He co-organizes WEFI (Workshop on Entrepreneurial Finance and Innovation), a bi-weekly virtual research forum. His research has been referenced by major regulatory bodies worldwide including the FTC, EU Competition Commission, and UK Competition and Markets Authority, and featured in leading media outlets like Wall Street Journal and New York Times. Professor Ma actively incorporates new data science technologies into his empirical economic research, focusing on unstructured data analysis and machine learning applications.
Prof. Sebastian Rudolph is a Professor of Computational Logic at the Institute for Artificial Intelligence , Faculty of Computer Science , TU Dresden. Since 2021, he has been an Affiliate Member of the Faculty of Mathematics. His research spans theoretical and applied artificial intelligence, focusing on Knowledge Representation and Reasoning through formalisms like Description Logics, Existential Rules, and Formal Concept Analysis, with applications in Semantic Technologies. 2017 : ERC Consolidator Grant for decidability principles in logic-based knowledge representation 2006-2013 : Postdoctoral researcher, project leader, and Privatdozent at KIT's Institute AIFB 2011 : Habilitation at KIT Earlier : PhD in Algebra and teaching qualification in mathematics, physics, and computer science at TU Dresden His recent publications address decidability of logical reasoning, non-monotonic extensions in formal concept analysis, standpoint logics, and multiagent systems. He supervises the DeciGUT and KIMEDS projects, and is involved in the SECAI and ScaDS.AI centers. Teaching activities include courses on Theoretical Computer Science, Existential Rules, and Formal Concept Analysis.
پژوهشگر
Dr. Anett Hoppe is a research staff member at the Leibniz Information Centre for Science and Technology (TIB) in Hannover, Germany, where she works in the Visual Analytics research group. Her research focuses on the intersection of artificial intelligence, education technology, and information science, with particular emphasis on how people learn through search processes and educational video consumption. Dr. Hoppe completed her academic journey with: Ph.D. in Semantic Web technologies for online user profiles from the University of Burgundy, Dijon, France Her primary research interests span Search as Learning, software-based support for scientific reproducibility, and ethical considerations in computer-based decision making. She investigates how visual elements, reading sequences, and AI technologies impact knowledge acquisition during web search and educational video consumption. Her work bridges human-computer interaction, educational psychology, and information retrieval to create more effective learning experiences, with recent publications examining the role of large language models, vision-language models, and visual complexity in educational contexts. Analysis of her recent publications (2024-2025) reveals a strong interdisciplinary focus combining computer science, educational psychology, and information science. Her research examines video-based learning effectiveness, knowledge gain prediction, educational resource discovery, and the impact of visual elements on learning outcomes. She consistently explores how AI technologies can be leveraged to enhance educational experiences while maintaining attention to ethical considerations and scientific reproducibility. Dr. Hoppe maintains active collaborations with researchers across multiple institutions, with frequent co-authorship patterns indicating strong research partnerships, particularly with Ralph Ewerth and other members of the Visual Analytics group at TIB. Her work supports TIB's mission to advance knowledge infrastructure and scholarly communication through innovative technological solutions while directly addressing practical challenges in educational technology and information retrieval.
Monica Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in Stanford University's School of Engineering and holds a courtesy professorship in Electrical Engineering. She leads the Stanford Open Virtual Assistant Laboratory and has pioneered work in virtual assistants, privacy protection, and compiler design. Her research includes the Almond virtual assistant, privacy-preserving IoT systems, and the ThingTalk programming language. She co-authored the seminal 'dragon book' on compilers and co-founded Tensilica (now part of Cadence). Education: Bachelor of Science (Honors), Computer Science, University of British Columbia, 1980 Master of Science, Computer Science, Carnegie Mellon University, 1982 Doctor of Philosophy (PhD), Computer Science, Carnegie Mellon University, 1987 Research Interests: Dr. Lam's work focuses on conversational AI with privacy guarantees, compiler optimization for parallel computing, and open-source virtual assistant ecosystems. She is a leader in decentralized systems, having developed frameworks like SociaLite for large-scale graph analysis and Musubi for mobile social networking without centralized platforms. Article Trends: Recent publications emphasize multimodal interactions, multilingual dialogue systems, and LLM-driven applications in areas like question answering, persuasive chatbots, and adaptive assistants. Her work bridges foundational AI research (e.g., semantic parsing) with real-world deployments (e.g., privacy-compliant IoT). Awards & Honors: Member of the National Academy of Engineering ACM Fellow Popular Science's Best of What's New Award (Security, 2019) Advising & Grants: Lam oversees the Open Virtual Assistant Initiative, a collaborative project to build open-source semantic models. Her NSF CNS grant (CNS Core) focuses on federated privacy systems. She has advised over 50 students in AI and systems research, though specific names are not listed in the provided texts. Labs & Teams: Directs the Stanford Open Virtual Assistant Laboratory, collaborates with the Stanford NLP group, and maintains ties to industry through former startup Tensilica's legacy in embedded processors.
HARADA Tatsuya is a Professor at the Research Center for Advanced Science and Technology (RCAS), University of Tokyo. His research focuses on intelligent robotics , real-world image processing , and human-informatics AI systems . Degree: PhD Research Themes: Harada investigates real-world intelligent information processing, fast image recognition and retrieval, and AI applications in pathology diagnostics. His work spans explainable AI systems for cancer analysis, tactile sensor integration for humanoid robots, and knowledge acquisition via dialog systems. Research Categories: His projects have been funded through multiple Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) grants, including Grant-in-Aid for Scientific Research (A/B/C) and Innovative Areas programs. Specific projects include "Explainable AI diagnostic system for breast cancer" (2020-2021) and "Behavior Capture Suit with Motion Sensors" (2010-2013). Scientific Awards & Grants: Recipient of competitive JSPS grants for interdisciplinary research bridging robotics, computer vision, and medical diagnostics.
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