جستجوی استاد راهنمای دانشگاهی

• Computer Vision
• Artificial Intelligence
• Multimodal Learning
• +۷ مورد دیگر

Dr. Hilde Kuehne is a Professor at the University of Tuebingen and a key researcher at the Tuebingen AI Center. She holds affiliations with MIT-IBM Watson AI Lab and Goethe University Frankfurt, with a focus on computer vision, multimodal learning, and explainable AI. Her work bridges vision-language models, audio-visual alignment, and self-supervised methods. Co-organizer of the New Frontiers in Associative Memories workshop @ ICLR 2025 Member of the Scientific Advisory Board of the Carl-Zeiss-Foundation Contributor to CVPR 2025's UTD dataset for unbiased video benchmarks Her research addresses critical challenges in: Explainability for Vision Transformers (LeGrad) Fine-grained audio-visual alignment (CAV-MAE Sync) Zero-shot visual recognition automation (Meta-Prompting) Spatio-temporal grounding without annotations Recent collaborative work spans 15+ publications across CVPR, NeurIPS, ICCV, and ICLR, with emphasis on multimodal foundation models, dataset bias mitigation, and differentiable logic networks. She actively contributes to workshop organization and peer review as evidenced by her involvement in CVPR 2025 and ICLR 2025 program committees.

• Natural Language Understanding
• Discourse
• Knowledge Representation
• +۵ مورد دیگر

James F. Allen is a Professor of Computer Science at the University of Rochester, holding the John H. Dessauer Professorship since 1992. He also serves as Senior Research Scientist (since 2002) and Associate Director (since 2006) at the Florida Institute for Human and Machine Cognition. His academic career at Rochester spans from Assistant Professor (1979-1984) to Department Chair (1987-1990) and full Professor (1987-present). His educational background includes a Ph.D. from the University of Toronto (1979). Research interests focus on natural language understanding, discourse, knowledge representation, common-sense reasoning, and planning, with emphasis on the intersection of deep language processing and reasoning. Allen advocates for semantically-rich formalisms over purely statistical approaches in NLP. Allen's publication trends reveal sustained focus on dialogue systems and deep language understanding, with recent work emphasizing task learning, semantic representation, and multi-domain applications. His TRIPS project has driven innovations in collaborative dialogue systems for over a decade. Award highlights include: Presidential Young Investigator (1984-1989) Founding Fellow of the AAAI Best Paper Award at AAAI 2007 for PLOW He has secured significant research funding through the Presidential Young Investigator award and maintains active grants via his IHMC leadership role. Allen founded and led the TRIPS project, developing a robust framework for dialogue systems that enables rapid deployment in new domains without extensive corpora. His team has produced influential work in speech-act modeling, temporal reasoning, and task-learning systems.

Brendan Jubaمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Theoretical Computer Science
• Machine Learning
• +۴ مورد دیگر

Brendan Juba is an Associate Professor in the Department of Computer Science & Engineering at Washington University in St. Louis's McKelvey School of Engineering. His research focuses on theoretical approaches to artificial intelligence, emphasizing algorithms for integrated learning and reasoning, particularly in common sense reasoning, and communication protocols without standardized frameworks. He holds a PhD from MIT (2010) and dual BS/MS degrees from Carnegie Mellon University (2005). Education: PhD, Massachusetts Institute of Technology, 2010 MS & BS, Carnegie Mellon University, 2005 His expertise spans theoretical computer science, with notable work on AI algorithms and the NSF CAREER Award-funded study of relationships and generalization in AI. He has been affiliated with MIT’s CSAIL and Harvard University during postdoctoral research. His lab explores foundational AI challenges, and his work is indexed on Google Scholar. Awards: NSF CAREER Award (2020) Grants include the $543,000 NSF award (2020–2025) for AI generalization research. His lab engages in advancing autonomous systems' decision-making through improved environmental understanding. He is part of the Division of Computational & Data Sciences, fostering interdisciplinary research in computational theory and applications.

Brendan Jubaمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Theoretical Computer Science
• Machine Learning
• +۲ مورد دیگر

Brendan Juba is an Associate Professor at Washington University in St. Louis , affiliated with the School of Engineering and the Department of Computer Science and Engineering . He is also associated with the Division of Computational & Data Sciences and the Machine Learning & Artificial Intelligence group. Brendan earned his PhD from MIT in 2010 under Madhu Sudan , followed by a postdoctoral fellowship at Harvard University with Leslie Valiant until 2014. He was also jointly affiliated with CSAIL and the Center for Science of Information through 2012. His PhD thesis, Universal Semantic Communication , was published by Springer. Research Interests His work focuses on theoretical approaches to artificial intelligence , particularly algorithms for integrated learning and reasoning (e.g., common sense reasoning ), and computational complexity . He has also explored communication protocols without standards and the science of information in contexts like data mining and logical reasoning. His research spans the intersection of machine learning, formal logic, and algorithmic efficiency. Scientific Awards & Collaborations While specific awards are not listed, his work has been supported by the NSF Big Data Analytics REU site grant (IIS-1560191) and collaborations with researchers like Loizos Michael and Manuel Blum . He has presented tutorials at AAAI-18 and seminars at institutions like the Center for Computational Intractability and the IAS CSDM seminar . Teaching & Supervision He has taught courses such as CSE 347 (Analysis of Algorithms) , CSE 513T (Theory of AI & Machine Learning) , and CSE 519T (Advanced Machine Learning) . His supervision includes PhD students like Golnoosh Dehghanpoor and Zihao Deng , as well as Masters students (e.g., Roger Iyengar , Phoebe Chua ).

Nima Akbarzadehمشاهده پروفایل

پژوهشگر ارشد

• Game theory
• Optimal order
• Mathematical modeling
• +۳ مورد دیگر

Nima Akbarzadeh is a Research Fellow at HEC Montréal since May 2023, co-supervised by Erick Delage and Yossiri Adulyasak. He previously completed his Doctorate (2017–2022) in Electrical and Computer Engineering at McGill University under Aditya Mahajan's supervision. Current affiliation: HEC Montréal (Postdoctoral Research Fellow) PhD: McGill University (Electrical and Computer Engineering) Research focus areas: Game theory, Mathematical modeling, and Optimization without derivatives Nima's work bridges algorithm development with risk-aware reinforcement learning (RL), particularly in restless multi-arm bandits and weakly coupled Markov decision processes (MDPs). His recent publications at AISTATS 2025 highlight advancements in sequential decision-making frameworks and fair resource allocation strategies. Scientific awards include: 2024 GERAD Postdoctoral Fellowship 2020–2022 FRQNT Doctoral Scholarship 2017–2019 McGill Engineering Doctoral Award He actively participates in academic workshops and seminars, including IVADO's Risk-Aware/Safe RL Workshop (2023) and McGill's Informal Systems Theory Seminar (2021).

Esther Dermanمشاهده پروفایل

پژوهشگر ارشد

• Game theory
• Optimal order
• Mathematical modeling
• +۳ مورد دیگر

Esther Derman is a Research Fellow at MILA, affiliated with the Canada Research Chair in Decision-Making Under Uncertainty. Her work involves game theory, mathematical modeling, and optimization methods, with a focus on decision-making under uncertainty and reinforcement learning. She collaborates with co-directors Erick Delage and Pierre-Luc Bacon. Research Interests: Game theory, optimal order modeling, simulation, and derivative-free optimization methods. Scientific Recognition: Holds the prestigious Canada Research Chair in Decision-Making Under Uncertainty. On October 23, 2023, she presented a seminar at GERAD titled 'Regularization and Robustness in Reinforcement Learning'.