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Kaiming HeView profile

Associate Professor

• Computer Vision
• Deep Learning
• Machine Learning
• +6 more

Kaiming He is an Associate Professor with tenure in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), holding the Douglas Ross (1954) Career Development Professor of Software Technology chair. He also works part-time as a Distinguished Scientist at Google DeepMind. Prior to joining MIT in 2024, he was a research scientist at Facebook AI Research (FAIR) from 2016 to 2024, and a researcher at Microsoft Research Asia (MSRA) from 2011 to 2016. Dr. He received his PhD from the Chinese University of Hong Kong in 2011 and his Bachelor of Science from Tsinghua University in 2007. His academic journey reflects a strong foundation in computer science and engineering that has led to transformative contributions in artificial intelligence. His research primarily focuses on computer vision and deep learning, with pioneering work on deep residual networks (ResNets), visual object detection and segmentation, and self-supervised learning. He is best known for his work on Deep Residual Networks (ResNets), recognized as the most-cited paper of the twenty-first century. The residual connections he pioneered are now fundamental components in modern deep learning architectures including Transformers, AlphaGo Zero, AlphaFold, and various generative AI models. His recent publications demonstrate continued innovation across generative models, transformer architectures, and cross-disciplinary AI applications. His work bridges theoretical advances in neural network design with practical implementations that address real-world challenges in physics, biology, and other scientific domains. PAMI Young Researcher Award (2018) Best Paper Award, CVPR (2009, 2016) Best Paper Award, ICCV (2017) Best Student Paper Award, ICCV (2017) Everingham Prize, ICCV (2021) Most-cited paper of the twenty-first century Dr. He advises graduate students including Jake Austin, Xingjian Bai, and Mingyang Deng, and teaches advanced courses such as "6.S978: Deep Generative Models" (Fall 2024) and "6.8300/6.8301: Advances in Computer Vision" (Spring 2024). His research group actively explores how AI can serve as a unifying framework across scientific disciplines, breaking down traditional barriers between fields through shared methodologies and tools.

• Machine Learning
• Computer Vision
• Data Mining
• +6 more

Dr. Chang Xu is an Associate Professor in Machine Learning and Computer Vision at the University of Sydney's School of Computer Science. He holds a Bachelor of Engineering from Tianjin University and a PhD from Peking University. His research focuses on machine learning, data mining, and their applications in AI and computer vision, including multi-view learning, visual search, and face recognition. He is an ARC Future Fellow and a member of the Sydney Southeast Asia Centre and The Net Zero Institute. Education: B.E. in Engineering (Tianjin University), Ph.D. in Computer Science (Peking University). His research interests emphasize handling heterogeneous data, exploring data variety, and developing algorithms for robust AI systems. His work includes adversarial robustness, neural architecture search, and efficient deep learning models. Research trends in his articles include adversarial robustness in neural architectures, efficient vision transformers, multimodal 3D style transfer, and underwater image restoration. Key contributions span image restoration, video super-resolution, and lightweight network design. He has advised multiple PhD and master's students on topics like diffusion models, radar image synthesis, and graph similarity. Awards: ARC Future Fellow. Collaborations focus on cross-domain data integration and AI applications. His labs and teams explore generative models, robust learning, and scalable robotics policies. Recent work includes diffusion models for action segmentation and robust vision-language systems.

Stefano ErmonView profile

Associate Professor

• Machine Learning
• Generative AI
• Computational Sustainability
• +7 more

Stefano Ermon is an Associate Professor in the Department of Computer Science at Stanford University, affiliated with the Artificial Intelligence Laboratory and a Senior Fellow at the Woods Institute for the Environment. His research focuses on advancing machine learning and generative AI techniques to address societal and environmental challenges, including computational sustainability, geospatial analysis, and climate science. He holds a Ph.D. from Cornell University (2015). Education: Ph.D. in Computer Science, Cornell University (2015). Research Interests: Ermon’s work bridges foundational machine learning (e.g., diffusion models, generative AI, and optimization) with applications in sustainability, geospatial analysis (via satellite imagery), and earth observation systems. Notable contributions include predicting poverty using satellite data and developing scalable methods for molecule generation. Articles Trends: His recent work emphasizes diffusion models for generative tasks (e.g., text-to-image, molecule design), geospatial AI (e.g., environmental monitoring), and ethical AI (e.g., bias mitigation in LLMs). He also explores applications in robotics and scientific computing. Awards: He has received prestigious awards, including the ICML 2024 Best Paper Award, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, and the IJCAI Computers and Thought Award. Advising and Grants: Ermon teaches courses like Probabilistic Graphical Models (CS228) and has secured grants from NSF, ONR, AFOSR, and private foundations. His lab develops tools for climate science and sustainable development. Labs/Teams: Leads the Stanford AI Lab group focused on computational sustainability and generative AI, collaborating with institutions like the Woods Institute for environmental applications.

• Robotics
• Artificial Intelligence
• Machine Learning
• +4 more

Prof. Ingmar Posner is a leading figure in applied artificial intelligence at the University of Oxford, where he serves as Principal Investigator for the Applied Artificial Intelligence Lab (A2I) and founding Director of the Oxford Robotics Institute. His work focuses on enabling robots to operate effectively in complex real-world environments through experience-driven learning. Key research areas: robot learning, scene interpretation, data-efficient learning, and transfer learning Applications in manipulation, autonomous driving, logistics, and space exploration His team has produced groundbreaking work in world models, sim-to-real transfer, and constraint-based manipulation systems (e.g., COMBO-Grasp). Notable contributions include the TWIST distillation framework and foundational research in tactile data generation (TactGen). He has received multiple best paper awards at top robotics venues. Publications reveal evolving research themes: 2025 work emphasizes language-conditioned learning (Lumos) and multi-agent decision-making, while 2024 focused on diffusion models for locomotion and differentiable simulators. Earlier work spans from urban scene analysis to physically plausible scene synthesis (RELATE).

Christian TheobaltView profile

Professor

• Computer Science
• Artificial Intelligence
• Computer Vision
• +5 more

Christian Theobalt is a Professor of Computer Science at Saarland University and Scientific Director of the Visual Computing and Artificial Intelligence Department at the Max Planck Institute for Informatics . He leads the Saarbruecken Center for Visual Computing as a strategic partnership between Google and MPI. PhD in Computer Science (2005) from MPI-INF/Saarland University Postdoctoral Researcher at MPI (2005-2007) Visiting Assistant Professor at Stanford (2007-2009) His research focuses on the intersection of Computer Graphics, Computer Vision, and Artificial Intelligence , with specializations in: 3D/4D Human Reconstruction Neural Rendering Performance Capture Geometric Deep Learning Volumetric Video Quantum Visual Computing Recent article trends show emphasis on: Quantum computing applications in visual reconstruction Neural rendering with radiance fields Human motion capture from egocentric views Gesture-language interaction modeling Multi-modal scene understanding Real-time free-viewpoint rendering Scientific Awards : Fellow of EUROGRAPHICS (2022) CVPR Best Student Paper Honorable Mention (2020) ERC Consolidator Grant (2017) Karl Heinz Beckurts Award (2017) Busy Beaver Teaching Award (2016) ERC Starting Grant (2013) Advising over 50 students and researchers including: Current researchers: Viktor Rudnev, Linjie Lyu, Mohit Mendiratta Postdocs: Kwang In Kim, Kiran Varanasi Alumni: Franziska Mueller (Google), Dushyant Mehta (Qualcomm), Ayush Tewari (MIT) Grants include ERC grants, Google Glass Research Award, and multiple industry partnerships. His lab maintains cutting-edge facilities with: Multi-camera capture systems Quantum annealing infrastructure HDR radiance field technology GPU clusters for AI research Time-of-flight imaging systems Event camera arrays

• Computer Vision
• Machine Learning
• Visual Reasoning
• +7 more

Justin Johnson is an Assistant Professor at the University of Michigan's College of Engineering, Department of Electrical Engineering and Computer Science, and a Research Scientist at Facebook AI Research (FAIR). His work bridges computer vision, machine learning, and deep learning, focusing on visual reasoning, vision-language tasks, image generation, and 3D reasoning using neural networks. PhD, Stanford University (advised by Fei-Fei Li) His research interests span visual reasoning , vision and language , 3D vision , and image generation , with a focus on innovative applications of deep neural networks. Recent publications highlight work on 3D consistency, self-supervised learning, and multimodal integration of vision and text. Notable contributions include PyTorch3D for 3D data processing, and foundational work in visual question answering , neural style transfer , and scene graph-based image generation . Publications span top conferences like ICCV, CVPR, and NeurIPS. He teaches courses including EECS 498/598: Deep Learning for Computer Vision and EECS 442: Computer Vision at University of Michigan, with prior involvement in Stanford's CS 231N in co-teaching roles. Software projects include open-source frameworks like fast-neural-style for real-time artistic style transfer, and PyTorch3D for efficient 3D deep learning. These tools demonstrate his commitment to practical implementations and community-driven research.

Thomas HofmannView profile

Professor

• Machine Learning
• Deep Learning
• Natural Language Understanding
• +3 more

Prof. Dr. Thomas Hofmann is a Full Professor and Head of the Department of Computer Science at ETH Zurich since 2014. He also leads the Institute for Machine Learning. His research focuses on machine learning, deep learning, natural language understanding, and text understanding. Hofmann holds a Ph.D. from the University of Bonn (1997) and has held academic positions at Brown University (1999–2004) and TU Darmstadt. He transitioned to industry as Director of Engineering at Google (2006–2014), leading the Zurich R&D center, before returning to academia. He co-founded Recommind (2000) and 1plusX (Swiss marketing tech company), currently serving as Chief Scientist and board member at 1plusX. Education: Ph.D. in Computer Science, University of Bonn (1997) Postdoctoral Work: MIT (CBCL/AI Lab), UC Berkeley (EECS/ICSI) His research explores advanced machine learning techniques, including diffusion models, generative adversarial networks, and optimization dynamics. Hofmann’s entrepreneurial ventures reflect his focus on applying AI to real-world challenges, such as e-discovery and marketing technology. His work spans theoretical contributions (e.g., neural network training dynamics, continual learning) and applied innovations (e.g., image editing, portrait generation). Hofmann actively bridges academia and industry, influencing both research and commercial AI applications.

• Reinforcement Learning
• Multi-Agent Systems
• Deep Learning
• +2 more

Shimon Whiteson is Professor of Computer Science at the University of Oxford, leading the Whiteson Research Lab focused on reinforcement learning, multi-agent systems, and deep learning. His research develops algorithms for efficient learning in complex environments. Current work explores meta-reinforcement learning frameworks that enable agents to rapidly adapt to new tasks, with applications in autonomous driving simulation and robotics. Recent innovations include novel methods for offline reinforcement learning, multi-agent coordination, and morphology-aware control. Publications demonstrate advances in: Meta-RL algorithm design for few-shot adaptation Multi-agent reinforcement learning environments and benchmarks Imitation learning in autonomous driving Bayesian methods for sample-efficient learning Research outputs include widely used benchmarks and tools including JaxMARL for accelerated multi-agent RL research. Current doctoral supervision focuses on temporal abstraction in RL, multi-agent coordination, and reinforcement learning theory.

Roland BüchiView profile

Professor

• Control Systems Engineering
• Robotics
• Artificial Intelligence
• +4 more

Prof. Dr. Roland Büchi is a Professor at the School of Engineering, Zurich University of Applied Sciences (ZHAW), specializing in control systems engineering and applied AI. His affiliation includes leadership roles in research projects such as the ongoing 'Digital Bridge to Computer Science' initiative. With a career spanning decades, he maintains active research output and industrial collaborations. Research Interests: Büchi focuses on control systems optimization , particularly PID controller tuning using AI methods, system identification, and applications in robotics and drone technology. His work bridges theoretical control theory with practical implementations in mechatronics. Recent efforts explore machine learning for hysteresis modeling and swarm optimization for control systems. Publication Trends: His 15 most recent works (2018-2024) emphasize AI-driven control optimization , drone technology advancements, and engineering education. Dominant themes include PID parameter tuning for time-delayed systems, drone telemetry, and adaptive learning algorithms. A notable shift toward AI/ML applications in control engineering emerged post-2020. Labs and Teams: Büchi collaborates with researchers like Lukas Gruber and has historical ties to ETH Zurich’s robotics projects. His work involves experimental validation at ZHAW’s engineering facilities, with patents in turbocharger magnetic bearing systems.

• Machine Learning
• Statistics
• Theoretical Machine Learning
• +7 more

Fanny Yang is an Assistant Professor in the Computer Science Department at ETH Zurich . She previously held postdoctoral positions at Stanford University and a Junior Fellowship at the Institute for Theoretical Studies at ETH Zurich, advised by Nicolai Meinshausen . Her PhD was completed at the EECS Department of UC Berkeley , supervised by Martin Wainwright . Research Interests : Theoretical foundations of machine learning and statistics , particularly focusing on overparameterized models and high-dimensional data . Developing trustworthy ML models with emphasis on distributional robustness , domain generalization , and interpretability . Applications in medical diagnostics and treatment effect analysis , aiming to address reliability issues in real-world domains. Recent Work Trends : 2025 Articles : Theoretical analysis of semi-supervised multi-objective learning , test-time scaling with verifiers, and foundation models for efficient randomized experiments . 2024 Articles : Studies on robust mixture learning , privacy-preserving data synthesis via optimal transport , confounding quantification in causal inference , and semi-private learning frameworks. 2023 Articles : Investigations into active vs. passive learning in high dimensions, inductive bias in noisy interpolation , and semi-supervised novelty detection using model ensembles .

Kristen GraumanView profile

Professor

• Artificial Intelligence
• Computer Vision
• Graphics and Visualization
• +8 more

Kristen Grauman is a Full Professor in the Department of Computer Science at the University of Texas at Austin, where she leads the UT Computer Vision Group. Her research focuses on computer vision and machine learning, with applications in visual recognition, video analysis, and multi-modal perception. She received her B.A. from Boston College and her Ph.D. from MIT. Her research interests span visual recognition, image and video search, video analysis, first-person vision, embodied and multi-modal perception, and interactive machine learning. She has made significant contributions to the field, particularly in developing algorithms for understanding visual content and human activities from video, including foundational work on the Pyramid Match Kernel and relative attributes. Her recent publications reveal a strong emphasis on egocentric (first-person) vision, audio-visual learning, and view-invariant representations. There is a clear trajectory toward multi-modal integration (vision, audio, language) and real-world applications in instructional videos, human activity understanding, and embodied AI systems. She has received numerous awards including: AAAI Fellow (2019) J. K. Aggarwal Prize, International Association for Pattern Recognition (2018) Helmholtz Prize (2017) UT Austin Academy of Distinguished Teachers (2017) Best Paper Award, Asian Conference on Computer Vision (2016) Presidential Early Career Award for Scientists and Engineers (2014) Computers and Thought Award, International Joint Conferences on Artificial Intelligence (2013) Pattern Analysis and Machine Intelligence Young Researcher Award (2013) Alfred P. Sloan Research Fellow (2012) Marr Prize (2011) Prof. Grauman serves as Associate Editor-in-Chief for the IEEE Transactions on Pattern Analysis and Machine Intelligence. She has secured substantial research funding including the Presidential Early Career Award, NSF grants, and industry partnerships. Her advising has produced numerous influential publications and students who are now leaders in computer vision. She leads the UT Computer Vision Group, which collaborates closely with the Electrical and Computer Engineering Department. The group is pioneering large-scale egocentric video research through projects like Ego4D and Ego-Exo4D, focusing on real-world applications in human activity understanding, audio-visual perception, and interactive systems.

• Robotics
• Robot Learning
• Machine Learning
• +7 more

Lerrel Pinto is an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University (NYU), where he leads the General-purpose Robotics and AI Lab (GRAIL) as part of the CILVR research group. His work bridges the gap between theoretical machine learning and practical robotics applications, with a focus on enabling robots to generalize and adapt in real-world environments. Dr. Pinto received his undergraduate degree from IIT Guwahati, followed by a PhD from the Robotics Institute at Carnegie Mellon University (CMU). He then completed a postdoctoral fellowship at the University of California, Berkeley before joining NYU as faculty. His research program centers on robot learning and decision making, with several key thrusts that demonstrate his innovative approach to robotics. Pinto's work emphasizes large-scale learning techniques that leverage both extensive data and sophisticated model architectures. A significant portion of his research focuses on representation learning for sensory data, particularly developing methods that enable robots to make sense of visual, tactile, and auditory inputs. His lab has made notable contributions to reinforcement learning algorithms that allow robots to adapt to new scenarios with minimal retraining. Pinto also champions open-source robotics , developing affordable robot platforms that democratize access to robotics research. Analysis of Pinto's recent publications reveals a strong trend toward multimodal perception in robotics, integrating visual, tactile, and auditory information to create more robust robot systems. His work increasingly focuses on zero-shot and few-shot learning capabilities, enabling robots to handle novel situations without extensive retraining. There's also a clear progression toward general-purpose robotics , moving away from task-specific solutions toward more flexible systems that can handle diverse real-world challenges. Dr. Pinto's scientific contributions have been recognized with several prestigious awards: Sloan Research Fellowship (2025) NSF CAREER Award (2024) RAL Early Career Award (2024) Best Student Paper Award at ICRA (2016) Outstanding Paper Award at MFM-EAI workshop at ICML (2024) Best Paper Award at NGSM workshop at ICML (2024) Best Student Paper Award at RSS (2023) As an advisor, Pinto has mentored numerous students who have gone on to impactful careers in both academia and industry. His former PhD student Denis Yarats co-founded Perplexity.AI, while Mahi Shafiullah became a postdoc at UC Berkeley and Meta AI. Many of his Masters students have pursued PhDs at top institutions like CMU, MIT, and Stanford, or joined leading robotics companies including 1X, Fauna Robotics, and NVIDIA. Pinto's lab has secured significant research funding, including the NSF CAREER award and likely other grants supporting his robotics research program. The General-purpose Robotics and AI Lab (GRAIL) that Pinto leads brings together a diverse team of researchers working on cutting-edge robotics challenges. The lab maintains strong collaborations with industry partners and other academic institutions, facilitating technology transfer and real-world impact. GRAIL's research spans multiple robotics platforms and focuses on developing algorithms that enable robots to learn from diverse experiences and generalize across environments.

Kyros KutulakosView profile

Professor

• Computational Imaging
• Computer Vision
• 3D Sensing
• +9 more

Kyros Kutulakos is a Professor in the Department of Computer Science at the University of Toronto, where he leads research in computational imaging and 3D sensing. His affiliations include the Toronto Computational Imaging Group, Computer Vision Group, Dynamic Graphics Project (DGP), and Vector Institute Group. He teaches graduate and undergraduate courses such as CSC320 (Introduction to Visual Computing) and CSC2530 (Computational Imaging & 3D Sensing). His research interests span computational imaging, non-line-of-sight imaging, single-photon detectors, 3D sensing, and neural rendering. Notable contributions include advancements in structured-light imaging, time-of-flight systems, and super-oscillatory microscopy. He has advised numerous PhD and MSc students, fostering cutting-edge research in imaging technologies. Kutulakos has received prestigious awards, including the Dean’s Research Excellence Award (2023) and multiple best paper prizes (e.g., Marr Prize at ICCV 2023). He has served as program chair for ICCV 2013, ICCP 2010, and CVPR 2003, contributing to academic leadership in computer vision. His work bridges optics, photonics, and computation, with applications in autonomous systems, medical imaging, and astronomy. Current research focuses on extreme imaging scenarios, such as imaging in pitch-black environments and around corners, leveraging novel sensor designs and computational techniques.

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +3 more

Geoffrey E. Hinton is a distinguished Professor in the Department of Computer Science at the University of Toronto. He is renowned for his foundational contributions to machine learning, particularly in the development of deep learning and neural networks. His research focuses on understanding learning processes in both artificial and biological systems, with key contributions including Boltzmann machines, backpropagation, and deep belief networks. He teaches advanced machine learning courses such as CSC2535, emphasizing topics like graphical models, variational inference, and deep learning architectures. His work has been published extensively in top journals and conferences, with recent papers exploring forward-forward algorithms, analog diffusion models, and scalable neural network training methods. Hinton has advised numerous PhD and master's students and collaborates with institutions like Vector Institute. He is a central figure in the global AI community, regularly presenting at conferences (e.g., 2023 talks on CBS 60 Minutes, BBC, and PBS). His lab focuses on advancing machine learning theory and applications, addressing challenges in vision, language, and generative models.

Osbert BastaniView profile

Associate Professor

• Machine Learning
• Neurosymbolic AI
• Program Synthesis
• +10 more

Osbert Bastani is an Associate Professor at the Department of Computer and Information Science, University of Pennsylvania, leading the trustml@Penn research group. He is affiliated with the ASSET , PRECISE , and PRiML centers, and the PLClub research group. His research focuses on Trustworthy Neurosymbolic Systems , Synthesizing Neurosymbolic Programs , and Machine Learning for Programmer Productivity , with applications in verification, fairness, and human-AI collaboration. He received the NSF CAREER Award in 2023. His recent publications (2024-2025) emphasize AI Safety , LLM Robustness , and Algorithmic Fairness , including work on adversarial robustness, conformal prediction, and program synthesis. Students he has advised include Sagnik Anupam, Stephen Mell, Jason Ma, Shuo Li, and others. Awards: NSF CAREER Award (2023)