Founded in December 2017, the Center on Frontiers of Computing Studies (CFCS), Peking University (PKU), is a new university initiative co-founded by Professor John E. Hopcroft (Turing Laureate) and Professor Wen Gao (CAE, ACM/IEEE Fellow). The center stands in the frontier of computer science, working closely with top universities and research institutions across the world, to build the world-class computation theory research center and a support center for interdisciplinary application. Research areas range from computation theory, artificial intelligence, computing and economics, bioscience, to medical health, etc.

[CVPR 2024] MaskClustering: View Consensus based Mask Graph Clustering for Open-Vocabulary ...
5:04
[CVPR 2024] MaskClustering: View Consensus based Mask Graph Clustering for Open-Vocabulary ...
[ICRA 2024] Discuss Before Moving: Visual Language Navigation via Multi-expert Discussions
5:01
[ICRA 2024] Discuss Before Moving: Visual Language Navigation via Multi-expert Discussions
[ICRA 2024] GAMMA: Graspability-Aware Mobile MAnipulation Policy Learning based on...
3:22
[ICRA 2024] GAMMA: Graspability-Aware Mobile MAnipulation Policy Learning based on...
[ICRA 2024] PixNav: Bridging Zero-Shot Object Navigation and Foundation Models through...
3:37
[ICRA 2024] PixNav: Bridging Zero-Shot Object Navigation and Foundation Models through...
[NeurIPS 2023] ChimpACT: A Longitudinal Dataset for Understanding Chimpanzee Behaviors
6:40
[NeurIPS 2023] ChimpACT: A Longitudinal Dataset for Understanding Chimpanzee Behaviors
[NeurIPS 2023] GenPose: Generative Category-level Object Pose Estimation via Diffusion Models
5:18
[NeurIPS 2023] GenPose: Generative Category-level Object Pose Estimation via Diffusion Models
[NeurIPS 2023] GraspGF: Learning Score-based Grasping Primitive for Human-assisting Dexterous...
5:16
[NeurIPS 2023] GraspGF: Learning Score-based Grasping Primitive for Human-assisting Dexterous...
[NeurIP 2023] Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation
4:58
[NeurIP 2023] Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation
[NeurIP 2023] Social Motion Prediction with Cognitive Hierarchies
4:37
[NeurIP 2023] Social Motion Prediction with Cognitive Hierarchies
Lecture 10: Network flow: Brief further improvements, bipartite matching, disjoint paths (2)
50:01
Lecture 10: Network flow: Brief further improvements, bipartite matching, disjoint paths (2)
Lecture 13: Approximation algorithms (3)
49:19
Lecture 13: Approximation algorithms (3)
Lecture 10: Network flow: Brief further improvements, bipartite matching, disjoint paths (3)
21:14
Lecture 10: Network flow: Brief further improvements, bipartite matching, disjoint paths (3)
Lecture 13: Approximation algorithms (2)
51:40
Lecture 13: Approximation algorithms (2)
Lecture 12: NP-Completeness (3)
52:04
Lecture 12: NP-Completeness (3)
Lecture 13: Approximation algorithms (1)
50:35
Lecture 13: Approximation algorithms (1)
Lecture 12: NP-Completeness (2)
52:33
Lecture 12: NP-Completeness (2)
Lecture 11: NP and computational intractability: Polynomial-time reductions, definition of P, NP (3)
51:25
Lecture 11: NP and computational intractability: Polynomial-time reductions, definition of P, NP (3)
Lecture 11: NP and computational intractability: Polynomial-time reductions, definition of P, NP (2)
51:03
Lecture 11: NP and computational intractability: Polynomial-time reductions, definition of P, NP (2)
Lecture 11: NP and computational intractability: Polynomial-time reductions, definition of P, NP (1)
47:02
Lecture 11: NP and computational intractability: Polynomial-time reductions, definition of P, NP (1)
Lecture 10: Network flow: Brief further improvements, bipartite matching, disjoint paths (1)
50:02
Lecture 10: Network flow: Brief further improvements, bipartite matching, disjoint paths (1)
Lecture 12: NP-Completeness (1)
52:43
Lecture 12: NP-Completeness (1)
Lecture 8: Dynamic programming: Sequence alignment, shortest path, negative cycles in a graph (3)
50:22
Lecture 8: Dynamic programming: Sequence alignment, shortest path, negative cycles in a graph (3)
Lecture 8: Dynamic programming: Sequence alignment, shortest path, negative cycles in a graph (1)
50:08
Lecture 8: Dynamic programming: Sequence alignment, shortest path, negative cycles in a graph (1)
Lecture 9: Network flow: Max-flow problem, min-cut problem, Ford-Fulkerson algorithm (2)
50:59
Lecture 9: Network flow: Max-flow problem, min-cut problem, Ford-Fulkerson algorithm (2)
Lecture 9: Network flow: Max-flow problem, min-cut problem, Ford-Fulkerson algorithm (3)
51:18
Lecture 9: Network flow: Max-flow problem, min-cut problem, Ford-Fulkerson algorithm (3)
Lecture 6: Divide-and-conquer: Integer and matrix multiplication, Fast Fourier transform (3)
44:44
Lecture 6: Divide-and-conquer: Integer and matrix multiplication, Fast Fourier transform (3)
Lecture 6: Divide-and-conquer: Integer and matrix multiplication, Fast Fourier transform (2)
49:56
Lecture 6: Divide-and-conquer: Integer and matrix multiplication, Fast Fourier transform (2)
Lecture 7: Dynamic programming: Weighted interval scheduling, segmented least squares (3)
51:12
Lecture 7: Dynamic programming: Weighted interval scheduling, segmented least squares (3)
Lecture 6: Divide-and-conquer: Integer and matrix multiplication, Fast Fourier transform (1)
50:30
Lecture 6: Divide-and-conquer: Integer and matrix multiplication, Fast Fourier transform (1)
Lecture 8: Dynamic programming: Sequence alignment, shortest path, negative cycles in a graph (2)
51:03
Lecture 8: Dynamic programming: Sequence alignment, shortest path, negative cycles in a graph (2)