جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Deep Learning
• AI Safety
• +۱۲ مورد دیگر

Tim G. J. Rudner is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, a Faculty Member at the Vector Institute, and a Title A Fellow at Trinity College, University of Cambridge. He was previously an Assistant Professor and Faculty Fellow at New York University. University: University of Toronto School: Faculty of Arts and Science Department: Department of Statistical Sciences Affiliation: Vector Institute, Trinity College (Cambridge) He holds a PhD in Computer Science and an MSc in Statistics from the University of Oxford, where he was advised by Yee Whye Teh and Yarin Gal, and a BS in Applied Mathematics and Economics from Yale University. PhD: Computer Science, University of Oxford MSc: Statistics, University of Oxford BS: Applied Mathematics and Economics, Yale University His research focuses on building robust, transparent, and trustworthy machine learning systems, particularly for high-stakes applications. He develops probabilistic models that improve generalization under distribution shifts, provide reliable uncertainty estimates, and enable fair and interpretable predictions. His work spans generative models, large language models, healthcare, and biomedical discovery. The recent publications highlight a strong trend toward function-space modeling, Bayesian regularization, and AI safety. Tim's work emphasizes principled uncertainty quantification, robustness to subpopulation and semantic shifts, and the development of frameworks for AI governance and specification. His research bridges theoretical advances with real-world applications, especially in safety-critical domains like medicine and defense. Tim has received numerous accolades including being named a Rhodes Scholar, Qualcomm Innovation Fellow, and 2024 Rising Star in Generative AI. He was awarded a $700,000 Foundational Research Grant and a $30,000 Apple Seed Grant for improving LLM trustworthiness. Rhodes Scholar Qualcomm Innovation Fellow AISTATS Notable Paper Award (2024) Outstanding Paper Award, ICLR GenAI4DM Workshop (2024) Apple Seed Grant ($30,000) Foundational Research Grant ($700,000) NeurIPS Spotlight Talk 2024 Rising Star in Generative AI He actively mentors students, particularly first-generation and low-income scholars, and has contributed to major policy frameworks including the OECD AI Classification Framework and a series of CSET issue briefs on AI safety. His work demonstrates a strong commitment to responsible AI development, combining technical rigor with societal impact. Tim leads research efforts at the intersection of machine learning theory and practical deployment, with ongoing projects in generative modeling, reliable LLMs, and AI governance. His lab produces high-impact work regularly published at top-tier conferences such as NeurIPS, ICML, and AISTATS.

Tianmin Shuمشاهده پروفایل

استادیار

• Human-centered AI
• Machine social intelligence
• Theory of mind
• +۷ مورد دیگر

Tianmin Shu is an Assistant Professor in the Department of Computer Science at Johns Hopkins University , with a joint appointment in the Department of Cognitive Science . He is the founding director of the Social Cognitive AI (SCAI) Lab and was previously a Research Scientist at MIT, working with Josh Tenenbaum and Antonio Torralba. His research goal is to advance human-centered AI by engineering machine social intelligence —building systems that understand, reason about, and interact with humans in real-world settings. His work is inherently interdisciplinary, integrating machine learning, computer vision, robotics, and social cognition. Research Interests: Theory of Mind Reasoning: Developing models that infer human mental states from multimodal behavioral data. Embodied Assistance: Creating agents capable of assisting humans in physical environments through verbal and non-verbal collaboration. Learning from Human Feedback: Extracting reward-relevant preferences from rich human input to guide agent behavior. Social Scene Understanding: Recognizing and reasoning about group activities and social roles from visual and physical cues. Computational Social Cognition: Modeling how humans perceive and interpret social and physical interactions. Scientific Awards: Outstanding Paper Award at ACL 2024 for "MMToM-QA: Multimodal Theory of Mind Question Answering" Grants & Collaborations: Tianmin Shu has led or co-organized several high-impact workshops and tutorials, including the NeurIPS 2023 Tutorial on "Language Models Meet World Models" and the RSS 2024 Workshop on "Social Intelligence in Humans and Robots". His lab has also developed open-source platforms like VirtualHome-Social and SimWorld for multi-agent interaction research. Lab & Team: As director of the Social Cognitive AI (SCAI) Lab at Johns Hopkins University, Tianmin Shu leads a multidisciplinary team focused on building socially intelligent systems. His lab is located in Malone Hall 213 and collaborates closely with the Departments of Computer Science and Cognitive Science.

Chuang Ganمشاهده پروفایل

استادیار

• Artificial Intelligence
• Robotics
• Computer Vision
• +۷ مورد دیگر

Chuang Gan is an Assistant Professor at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences and the Department of Computer Science. His work focuses on advancing artificial intelligence, robotics, computer vision, and embodied agents through interdisciplinary research combining neural networks, physical simulations, and multimodal learning. Research interests include generative models, reinforcement learning, vision-language integration, and scalable autonomous systems. He explores topics like world modeling for robots, adaptive policy learning, and physics-driven AI. His projects often involve creating systems that learn from visual, auditory, and tactile inputs to perform complex tasks such as object manipulation, navigation, and decision-making in dynamic environments. Recent research trends emphasize embodied AI systems capable of long-horizon planning, compositional reasoning, and efficient learning from limited data. His work bridges theory and practice, with applications in robotics, simulation platforms, and multimodal generation. Key contributions include frameworks for 3D scene understanding, adaptive world models, and novel training paradigms for large language models. His research has been applied to robotics platforms like RoboDreamer and UBSoft, focusing on unbounded soft environments. Collaborations involve designing benchmarks for physical scene understanding (e.g., Physion++), and creating tools like DiffTactile for tactile simulation. His work often integrates principles from differential geometry, PDE dynamics, and game theory. Chuang Gan’s research group develops open-source tools and benchmarks, such as the SoftZoo robot co-design platform and the SOK-Bench situated reasoning benchmark. His team emphasizes scalable alignment methods beyond human supervision and explores ethical AI through principles like symmetry-enhanced training.

LEE Wee Sunمشاهده پروفایل

استاد

• Machine Learning
• Planning Under Uncertainty
• Approximate Inference
• +۳ مورد دیگر

LEE Wee Sun is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he previously served as Head of Department, Vice Dean of Undergraduate Studies, and Vice Dean of Research. His academic journey began with a B.Eng. in Computer Systems Engineering from the University of Queensland (1992) and a Ph.D. from the Australian National University (1996), followed by research roles at the Australian Defence Force Academy and MIT. Education: Ph.D., Australian National University, Canberra, Australia (1996) B.Eng. in Computer Systems Engineering, University of Queensland, Brisbane, Australia (1992) Research Focus: Professor Lee pioneers work in Machine Learning , Planning Under Uncertainty , and Approximate Inference , with emphasis on integrating AI subfields for holistic reasoning. His current projects include "Learning to Decompose for Reasoning and Planning" (enhancing LLMs via self-supervised problem decomposition) and "Learning to Reason with Visual-Linguistic Inputs" (unifying vision, language, and reasoning in single architectures). Publication Trends: Recent work (2023-2025) centers on bridging LLMs with classical AI techniques, featuring breakthroughs in uncertainty quantification, multi-task optimization, and graph-based reasoning. Key themes include sparsity-aware vehicle routing, epistemic uncertainty for reliable LLMs, and differentiable neural solvers for combinatorial problems. Awards: IJCAI-JAIR Best Paper Prize (2022) RSS Test of Time Award (2021) RoboCup Best Paper Award (IROS 2015) HRATC 1st Place (2015) IPPC POMDP Track 1st Place (2011, 2014) UAI Google Best Student Paper (2014) Semeval-1 1st/2nd Place (2007) J.G. Crawford Prize (ANU 1996) Leadership & Service: As steering committee chair for ACML and area chair for NeurIPS/ICML/AAAI/IJCAI, Professor Lee shapes global AI discourse. His administrative roles at NUS and collaborations with MIT/Singapore-MIT Alliance demonstrate commitment to advancing AI education and research infrastructure. While student advisees aren't listed, his leadership positions imply extensive mentoring. Research Ecosystem: His work drives NUS's AI initiatives including Knowledge@Computing projects on reasoning frontiers. Current efforts focus on making AI systems robust through uncertainty-aware planning and multi-modal integration, with applications in robotics, verification systems, and combinatorial optimization.

• Computational Linguistics
• Computer Science
• Machine Learning
• +۱ مورد دیگر

Noah A. Smith is an Adjunct Professor of Computer Science and Engineering at the University of Washington. His work focuses on computational linguistics, machine learning, and natural language processing. He holds a Ph.D. in Computer Science from Johns Hopkins University (2006). His research explores ethical AI applications, multimodal systems, and foundational aspects of language models. Key research areas include: Ethical considerations in NLP, such as detecting rights abuses through text analysis Efficient decoding and alignment strategies for large language models Large-scale evaluation frameworks for multitask and multimodal generation Understanding pretraining dynamics and data composition effects Recent work emphasizes transparency in language models (e.g., tracing outputs to training data) and improving alignment through human feedback. He has contributed to open-source projects like OLMo and Dolma, advancing reproducibility in NLP research. No awards explicitly listed in provided texts. No specific advising or grant details available, though extensive publication output indicates active research involvement.

Bruce Draperمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Visual Learning
• +۷ مورد دیگر

Bruce Draper is a Professor and Chair of the Department of Computer Science in the College of Natural Sciences at Colorado State University. His work bridges artificial intelligence, machine learning, and computer vision, with a strong emphasis on real-world applications involving visual data and intelligent systems. Research Interests: Draper's research centers on machine learning with a focus on visual learning, adversarial AI, and visual agents. He investigates how AI systems can perceive, interpret, and interact with visual environments through technologies like facial recognition, object tracking, augmented reality, and automated visual communication. His work addresses both the capabilities and vulnerabilities of modern AI, particularly in defending systems against adversarial attacks. Publication Trends: His recent scholarly output reflects a consistent trajectory in advancing computer vision and AI robustness. The articles span topics from adversarial defense mechanisms and visual agent autonomy to scalable learning frameworks and real-time video analysis. Collectively, they emphasize secure, efficient, and context-aware visual intelligence systems grounded in deep learning and representation learning. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: While no students or grants are explicitly listed, his leadership role as department chair and prior experience as a DARPA program manager suggest extensive involvement in research funding, mentorship, and high-impact project direction. His background indicates likely supervision of graduate students and management of federally funded research initiatives in AI and computer vision. Labs and Teams: Although no specific lab or research group is named, his research scope implies leadership or affiliation with interdisciplinary teams working on AI security, computer vision, and augmented reality systems within the Department of Computer Science at CSU.

• Robot Learning under Uncertainty
• Reward Inference from Human Input
• AI Safety
• +۵ مورد دیگر

Daniel S. Brown is an Assistant Professor at the University of Utah’s Robotics Center and School of Computing. His research focuses on robot learning under uncertainty, reward inference from human input, and AI safety. He completed his PhD in Computer Science at UT Austin (2020) under Prof. Scott Niekum and was a postdoc at UC Berkeley working with Anca Dragan and Ken Goldberg. He leads the Aligned, Robust, and Interactive Autonomy (ARIA) Lab, which explores human-robot collaboration, safe AI systems, and emergent swarm behaviors. His work emphasizes practical methods for ensuring robustness and safety in autonomous systems. Education: PhD in CS (UT Austin, 2020) Research interests include: Bayesian methods for reward learning, interactive imitation learning, and scalable human-swarm interaction. His NIH-funded projects integrate AI with assistive technologies like powered neck exoskeletons. Key publications focus on Bayesian inverse reinforcement learning, demonstration sufficiency, and robust policy optimization. His NIH Trailblazer Award (2023) supports adaptive control systems for medical exoskeletons. Awards: RSS Pioneer (2021), NeurIPS Workshop Best Paper (2020) Teaching includes courses like 'Human-AI Alignment' and collaborative work on interdisciplinary projects combining robotics with game design and healthcare.

• Robot Learning
• Reward Inference
• Imitation Learning
• +۵ مورد دیگر

Daniel Brown is an Assistant Professor at the University of Utah's Robotics Center and School of Computing . Previously, he completed a postdoc at UC Berkeley (2020–2022) and earned his PhD in Computer Science at UT Austin in 2020 under Scott Niekum . He leads the Aligned, Robust and Interactive Autonomy (ARIA) Lab , focusing on human-robot interaction and AI safety. Research interests include: Robot learning under uncertainty Reward inference from human input Robust policy optimization Bayesian reward inference High-confidence performance bounds Assistive AI systems Awards : NIH Trailblazer Award (2024) RSS Pioneer (2021) Teaching : CS 5955/6955 Advanced Artificial Intelligence (Spring 2025) CS 6300 Artificial Intelligence (Spring 2024) CS 5960/6960 Human-AI Alignment (Fall 2023)

Brenden M. Lakeمشاهده پروفایل

دانشیار

• Computational cognitive modeling
• Machine learning
• Structured probabilistic models
• +۱۶ مورد دیگر

Brenden M. Lake is an Associate Professor of Psychology and Data Science at New York University, transitioning to Princeton University in Fall 2025 as an Associate Professor of Computer Science and Psychology. His work bridges human cognition and machine intelligence , focusing on computational models that explain cognitive phenomena while advancing AI capabilities. Research Themes: Few-shot concept learning, neuro-symbolic modeling, grounded language acquisition from child input, systematic generalization in neural networks, and human-inspired inductive biases. Lab Affiliation: Human & Machine Intelligence Lab at Princeton (formerly NYU’s Human & Machine Learning Lab), utilizing child-centric data and meta-learning frameworks. Scientific Recognition: MIT Technology Review 35 Under 35 (2019) Society of Mathematical Psychology Outstanding Paper Award (2018) Academic Contributions: Pioneering research on few-shot learning, program induction, and systematic generalization in humans and machines. His Nature (2023) and Science (2024) publications demonstrate how human cognitive mechanisms can enhance AI systems.

Joe Watsonمشاهده پروفایل

پژوهشگر

• Robot Learning
• Machine Learning
• Bayesian Inference
• +۵ مورد دیگر

Joe Watson is a Postdoctoral Researcher at the University of Oxford within the Applied Artificial Intelligence Lab at the Oxford Robotics Institute . He earned his PhD in 2024 from the Technical University of Darmstadt , supervised by Prof. Jan Peters, and holds an MEng in Information & Computer Engineering from University of Cambridge . His research focuses on statistical methods for robot learning , particularly the duality between entropy-regularized optimization and Bayesian inference. Key interests include uncertainty quantification , policy optimization , and incorporating structural knowledge into robotics. Scientific contributions include Coherent Soft Imitation Learning (NeurIPS 2023, spotlight) and Monte Carlo Posterior Policy Iteration (CoRL 2022, oral). Recent publications address safe foundation models and tensor motion planning in 2025. 2022 : R:SS Pioneer for robotics research 2016 : Charles Babbage Senior Scholarship at Cambridge He has contributed to open-source projects like i2c and monte-carlo-posterior-policy-iteration , with 534 GitHub contributions in the last year.