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Da Li - Samsung AI Centre Cambridge
Google Scholar
Zeyuan Wang, Da Li, Yulin Chen, Ye Shi, Liang Bai, Tianyuan Yu, Yanwei Fu
"A single-step flow policy that works well with q-learning."
Accepted in AAAI 2026
HierarchicalPrune: Position-Aware Compression for Large-Scale Diffusion Models
Young D Kwon, Rui Li, Sijia Li, Da Li, Sourav Bhattacharya, Stylianos I Venieris
"An effective structural pruning for large diffusion models."
Accepted in AAAI 2026
TFAR: A Training-Free Framework for Autonomous Reliable Reasoning in Visual Question Answering
Zhuo Zhi, Chen Feng, Adam Daneshmend, Mine Orlu, Andreas Demosthenous, Lu Yin, Da Li, Ziquan Liu, Miguel RD Rodrigues
"A training-free tool calling framework for VQA."
Accepted in TMLR 2025
Da Li (李达)
Senior Research Scientist @ Samsung AI Centre, Cambridge
E-mail: dali.academic(at)gmail.comGoogle Scholar
About Me
Da Li is a Senior Research Scientist within the Machine Learning and Data Intelligence group in Samsung AI Centre Cambridge.
Before pursuing his doctoral training in the UK, Da worked as a Software Engineer at Autodesk China in Shanghai for developing the 3D design software Inventor.
News
- **Note** Previous 'PACS' google drive link has expired due to google's security update.
The
download
link is updated now.
- [11/2025] Two papers accepted in AAAI 2026!
- [08/2025] A paper of visual reasoning for VQA is accepted in TMLR 2025!
- [08/2025] I will serve as an Area Chair in ICLR 2026!
- [01/2025] ConceptPrune is accepted in ICLR 2025!
- [01/2025] Finished my Area Chair job for ICLR 2025!
- [05/2024] One paper about federated learning among heterogenous clients is accepted in ICML
2024!
- [01/2024] Our intern's work about "a hierarchical NAS for few shot learning" got accepted in
ICLR 2024 as Oral!
- [09/2023] Our intern's work "Better Practice DA" got Best Paper Award in AutoML 2023!
Publication
Recent highlights
One‑Step Generative Policies with Q‑Learning: A Reformulation of MeanFlowZeyuan Wang, Da Li, Yulin Chen, Ye Shi, Liang Bai, Tianyuan Yu, Yanwei Fu
"A single-step flow policy that works well with q-learning."
Accepted in AAAI 2026
HierarchicalPrune: Position-Aware Compression for Large-Scale Diffusion Models
Young D Kwon, Rui Li, Sijia Li, Da Li, Sourav Bhattacharya, Stylianos I Venieris
"An effective structural pruning for large diffusion models."
Accepted in AAAI 2026
TFAR: A Training-Free Framework for Autonomous Reliable Reasoning in Visual Question Answering
Zhuo Zhi, Chen Feng, Adam Daneshmend, Mine Orlu, Andreas Demosthenous, Lu Yin, Da Li, Ziquan Liu, Miguel RD Rodrigues
"A training-free tool calling framework for VQA."
Accepted in TMLR 2025
Services
Reviewers: BMVC, NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, TPAMI, JMLR, IJCV, Machine Learning.
Senior Program Committee (SPC): AAAI 2022.
Area Chair: ICLR 2025, 2026.