Topic in Machine Learning

Generative & foundation models

154 labs at 28 universities work on generative & foundation models (part of Machine Learning). Most at SJTU, CMU and NTU. 122 published in 2026.

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I want to research generative & foundation models in anywhere.

154 labs at 28 universities work on generative & foundation models. Most at 18, 13, 10. 122 of them published in 2026, mostly at NeurIPS, ICLR and CVPR. Earliest deadline: , Dec 1.

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154 labs

HKCUHKDepartment of Information EngineeringDec 1

Dahua LinAssociate Professor

Works on computer vision and machine learning, including large-scale visual understanding and generative models.

Recognition, detection & segmentation
Generative & foundation models

2026Achieving Olympia-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement LearningICLR
2026Advancing Complex Video Object Segmentation via Progressive Concept ConstructionICLR

179 T1 · 210 papers · Group size not listed

CNSJTUSchool of Artificial IntelligenceMay 31

Yanfeng Wang教授

Works on AI for media and healthcare, including medical foundation models.

Medical imaging & healthcare AI
Generative & foundation models
Large language models

2026Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMsACL
2026GenMask: Adapting DiT for Segmentation via Direct Mask GenerationCVPR

119 T1 · 180 papers · Group size not listed

SGNTUCollege of Computing and Data Science

Chen Change LoyProfessor

Works on computer vision and deep learning, focusing on image and video restoration and enhancement, creative content generation and representation learning.

Video understanding
Computational imaging & photography
Generative & foundation models

20264RC: 4D Reconstruction via Conditional Querying Anytime and AnywhereICML
2026AITTI: Learning Adaptive Inclusive Token for Text-to-Image GenerationIJCV

117 T1 · 130 papers · 12 members · 36 alumni

USUIUCSchool of Information SciencesDec 1

Jingrui HeProfessor

Studies heterogeneous machine learning, active learning, neural bandits and self-supervised learning, with applications in security, healthcare and agriculture.

Reinforcement learning
Generative & foundation models

2026AdaFuse: Adaptive Ensemble Decoding for Large Language ModelsACL
2026Auto-regressive In-context Demonstration SelectionICML

114 T1 · 141 papers · 26 members · 41 alumni

CNZhejiangCollege of Computer Science and TechnologyFeb 28

Chunhua ShenChair Professor

Researches computer vision and machine learning, including dense prediction, detection and segmentation, and vision foundation models.

Recognition, detection & segmentation
Generative & foundation models
Large language models

2026\(\pi^3\): permutation-equivariant visual geometry learningICLR
2026ACTIVE-O3: empowering multimodal large language models with active perception via GRPOICML

109 T1 · 116 papers · 15 members · 12 alumni

GBImperialDepartment of ComputingDec 1

Jiankang DengAssistant Professor in Computing

Directs the MVP Lab on multi-modal foundation models for visual, acoustic and tactile signals and generative modelling of the physical world.

Vision-language & multimodal
Generative & foundation models
Large language models

2026Beyond Description: Cognitively Benchmarking Fine-Grained Action for Embodied AgentsECCV
2026CASteer: Cross-Attention Steering for Controllable Concept ErasureICLR

97 T1 · 107 papers · 6 members · 4 alumni

USStanfordComputer ScienceDec 8

Stefano ErmonAssociate Professor of Computer Science and Senior Fellow at the Woods Institute for the Environment

Develops probabilistic and generative models such as diffusion models, with applications to sustainability.

Generative & foundation models
Probabilistic & causal inference
Image & video generation

2026Adaptive Spectral Feature Forecasting for Diffusion Sampling AccelerationCVPR
2026Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular DynamicsICLR

97 T1 · 114 papers · Group size not listed

USCMUDepartment of PhilosophyDec 9

Kun ZhangProfessor

Develops methods for causal discovery and causal representation learning, aiming at explainable and controllable AI from a causal perspective.

Probabilistic & causal inference
Generative & foundation models
Human-AI collaboration & trust

2026Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-MakingICLR
2026Beyond DAGs: A Latent Partial Causal Model for Multimodal LearningICLR

90 T1 · 143 papers · Group size not listed

CNPekingSchool of Computer ScienceJan 15

Hao DongAssociate Professor with Tenure

Researches embodied AI and intelligent robots, with deep generative models and computer vision for robot manipulation.

Manipulation & robot learning
Robot perception & SLAM
Generative & foundation models

2026A3D: Adaptive Affordance Assembly with Dual-Arm ManipulationAAAI
2026AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action ModelsCVPR

87 T1 · 120 papers · Group size not listed

GBUCLDepartment of Computer Science

Jun WangProfessor of Information and Data Science

Researches multi-agent reinforcement learning, deep generative models and information retrieval.

Reinforcement learning
Generative & foundation models
Planning, control & multi-robot systems

2026A Benchmark for Deep Information SynthesisICLR
2026Bottlenecked Transformers: Periodic KV Cache Consolidation for Generalised ReasoningICLR

86 T1 · 135 papers · Group size not listed

CNTsinghuaTsinghua Shenzhen International Graduate SchoolFeb 28

Chun YuanProfessor

Works on computer vision, multimedia, and machine learning, including video object segmentation, detector distillation, and compatible representation learning.

Recognition, detection & segmentation
Video understanding
Generative & foundation models

2026Enhancing Geometric Perception in VLMs via Translator-Guided Reinforcement LearningICLR
2026FlashVSR: Towards Real-time Diffusion-Based Streaming Video Super ResolutionCVPR

82 T1 · 129 papers · Group size not listed

CNPekingSchool of Computer ScienceJan 15

Hao TangAssistant Professor / Researcher

Researches generative AI, world models, spatial intelligence and embodied AI with multimodal large models.

Vision-language & multimodal
Embodied & active vision
Generative & foundation models

2026AllRestorer: All-in-One Transformer for Image Restoration under Composite DegradationsTPAMI
2026EvoVLA: Self-Evolving Vision-Language-Action ModelECCV

82 T1 · 103 papers · 8 members

USUCSDElectrical and Computer EngineeringDec 15

Xiaolong WangAssociate Professor

Studies self-supervised visual learning, video understanding and embodied AI for robot manipulation.

Embodied & active vision
Video understanding
Generative & foundation models

20263D Aware Region Prompted Vision Language ModelICLR
2026Cross-embodied Co-design for Dexterous HandsICLR

78 T1 · 82 papers · 9 members · 73 alumni

CNZhejiangCollege of Computer Science and TechnologyFeb 28

Shouling JiProfessor

Directs the NESA lab, which works on AI security (adversarial robustness, LLM and generative model safety), data-driven security, software and system…

Trustworthy & safe ML
Generative & foundation models
Data management & big data

2026Angel or Demon: Investigating the Plasticity Interventions' Impact on Backdoor Threats in Deep Reinforcement LearningICML
2026APIEcho: Training-less Anomaly Detection via Intra-API Behavioral Comparison for Web ApplicationsS&P

71 T1 · 133 papers · Group size not listed

USStanfordComputer ScienceDec 8

Percy LiangThomas M. Siebel Professor of Machine Learning and Senior Fellow at the Stanford Institute for Human-Centered AI

Studies foundation models and their holistic evaluation, including language model transparency and agents.

Large language models
Reasoning & agents
Generative & foundation models

2026Comparing AI Agents to Cybersecurity Professionals in Real-World Penetration TestingICLR
2026Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and PlanningICLR

68 T1 · 88 papers · Group size not listed

CNPekingInstitute for Artificial IntelligenceJan 15

Muhan Zhang助理教授

Researches graph machine learning, graph neural networks and graph foundation models, and efficient LLM architectures and reasoning.

Graph learning
Generative & foundation models
Efficient training & inference

2026CanvasMAR: Improving Masked Autoregressive Video Prediction With CanvasNeurIPS
2026Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent ReasonerICML

61 T1 · 77 papers · 9 members · 4 alumni

CATorontoComputer ScienceDec 1

Sanja FidlerAssociate Professor

Studies 3D computer vision and generative models for content creation and simulation.

3D vision & neural rendering
Generative & foundation models
Physics-based simulation & animation

2026ChronoEdit: Towards Temporal Reasoning for In-Context Image Editing and World SimulationICLR
2026DiffusionHarmonizer: Bridging Neural Reconstruction and Photorealistic Simulation with Online Diffusion EnhancerCVPR

61 T1 · 63 papers · 21 members · 61 alumni

CNSJTUSchool of Computer ScienceMay 31

Weinan ZhangProfessor

Works on reinforcement learning, agentic AI and physical AI, focusing on foundation models for physical agents.

Reinforcement learning
Generative & foundation models
Large language models

2025Action First: Leveraging Preference-Aware Actions for More Effective Decision-Making in Interactive Recommender SystemsSIGIR
2025AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent SystemsNeurIPS

58 T1 · 71 papers · Group size not listed

USCMUMachine Learning DepartmentDec 9

Eric XingJohn F. Nash, Jr. Professor of Machine Learning, Director of MBZUAI-CMU Institute of Virtual and Programmable Cells, Associate Department Head

Studies machine learning and AI systems, including large foundation models, distributed learning and computational biology.

Generative & foundation models
Distributed, cloud & edge systems
Large language models

2026In-context Learning of Evolving Data Streams with Tabular Foundational ModelsKDD
2026Vision-G1: Towards General Reasoning Vision-Language Models via Reinforcement LearningAAAI

55 T1 · 77 papers · 16 members · 60 alumni

USNYUComputer Science (Courant)Dec 12

Yann LeCunJacob T. Schwartz Chaired Professor in Computer Science

Works on deep learning and self-supervised learning, including convolutional networks, energy-based models and joint-embedding world models.

Generative & foundation models

2026Attention Sinks and Compression Valleys in LLMs are Two Sides of the Same CoinICLR
2026Cambrian-S: Towards Spatial Supersensing in VideoICLR

51 T1 · 66 papers · Group size not listed

Where they areClick a university to narrow the list

Where they areClick a university to narrow the list

Other topics in Machine Learning

I want to research generative & foundation models in anywhere.

Dahua LinAssociate Professor

Yanfeng Wang教授

Chen Change LoyProfessor

Jingrui HeProfessor

Chunhua ShenChair Professor

Jiankang DengAssistant Professor in Computing

Stefano ErmonAssociate Professor of Computer Science and Senior Fellow at the Woods Institute for the Environment

Kun ZhangProfessor

Hao DongAssociate Professor with Tenure

Jun WangProfessor of Information and Data Science

Chun YuanProfessor

Hao TangAssistant Professor / Researcher

Xiaolong WangAssociate Professor

Shouling JiProfessor

Percy LiangThomas M. Siebel Professor of Machine Learning and Senior Fellow at the Stanford Institute for Human-Centered AI

Muhan Zhang助理教授

Sanja FidlerAssociate Professor

Weinan ZhangProfessor

Eric XingJohn F. Nash, Jr. Professor of Machine Learning, Director of MBZUAI-CMU Institute of Virtual and Programmable Cells, Associate Department Head

Yann LeCunJacob T. Schwartz Chaired Professor in Computer Science

Where they areClick a university to narrow the list

Other topics in Machine Learning

I want to research generative & foundation models in anywhere.

Dahua LinAssociate Professor

Yanfeng Wang教授

Chen Change LoyProfessor

Jingrui HeProfessor

Chunhua ShenChair Professor

Jiankang DengAssistant Professor in Computing

Stefano ErmonAssociate Professor of Computer Science and Senior Fellow at the Woods Institute for the Environment

Kun ZhangProfessor

Hao DongAssociate Professor with Tenure

Jun WangProfessor of Information and Data Science

Chun YuanProfessor

Hao TangAssistant Professor / Researcher

Xiaolong WangAssociate Professor

Shouling JiProfessor

Percy LiangThomas M. Siebel Professor of Machine Learning and Senior Fellow at the Stanford Institute for Human-Centered AI

Muhan Zhang助理教授

Sanja FidlerAssociate Professor

Weinan ZhangProfessor

Eric XingJohn F. Nash, Jr. Professor of Machine Learning, Director of MBZUAI-CMU Institute of Virtual and Programmable Cells, Associate Department Head

Yann LeCunJacob T. Schwartz Chaired Professor in Computer Science