Cong Fu – Deep learning research – In the field of deep learning research, Cong Fu has made significant contributions to various areas, including graph deep learning, robotic grippers, artificial intelligence for science, and more. This article explores the groundbreaking work and innovations of Cong Fu in these domains, highlighting his contributions and their impact on the respective fields. By delving into the research papers authored by Cong Fu, we can gain valuable insights into the advancements he has made and the potential applications of his work.
Graph Deep Learning Research
Graph deep learning has gained significant attention in recent years due to its applications in various domains such as social network analysis, recommendation systems, and drug discovery. Cong Fu has played a pivotal role in advancing this field through his research and innovative approaches.
One of Cong Fu’s notable contributions to graph deep learning is the development of Dig, a turnkey library that facilitates diving into graph deep learning research (M Liu et al., 2021). Dig provides a comprehensive set of tools and algorithms for researchers to explore and experiment with graph neural networks. With Dig, researchers can easily develop and compare different models, making the process of graph deep learning research more accessible and efficient.
In addition to Dig, Cong Fu has also contributed to the development of quantum property prediction using deeper 2D and 3D graph networks (M Liu et al., 2021). This research focuses on leveraging graph neural networks to predict quantum properties accurately. By utilizing deeper networks, Cong Fu and his team have achieved faster predictions without compromising accuracy, opening up new possibilities in quantum research.
Robotic Grippers
Robotic grippers play a crucial role in enabling robots to interact with their environment effectively. Cong Fu has made significant contributions to this area through his research on reconfigurable robotic grippers and their applications.
In a research paper co-authored by Cong Fu, a reconfigurable three-finger robotic gripper is presented (G Li et al., 2015). This gripper design offers versatility and adaptability by allowing the fingers to adjust their shape and configuration based on the object being grasped. The reconfigurability of the gripper enables it to handle a wide range of objects with varying shapes and sizes, enhancing the robot’s dexterity and manipulation capabilities.
Furthermore, Cong Fu and his team have explored the use of hydraulic-actuated biped robots (Y Lei et al., 2015). The mechanical design and gait plan of these robots are meticulously developed to achieve stable and efficient locomotion. By combining hydraulic actuators with advanced control systems, these biped robots demonstrate improved agility and balance, paving the way for more advanced humanoid robots in the future.
Artificial Intelligence for Science
Artificial intelligence (AI) has revolutionized various scientific fields by enabling efficient data analysis, prediction, and modeling. Cong Fu has made notable contributions to the application of AI in quantum systems, atomistic simulations, and continuum systems.
In a research paper co-authored by Cong Fu, the use of AI for science in quantum, atomistic, and continuum systems is explored (X Zhang et al., 2023). The paper discusses how AI techniques, such as graph neural networks, can be leveraged to predict and analyze properties of quantum systems, atomistic structures, and continuous materials. By incorporating AI into scientific research, Cong Fu and his team have achieved faster and more accurate predictions, leading to advancements in various scientific domains.
CITATIONS
| TITLE |
CITED BY
|
YEAR |
|---|---|---|
| Dig: A turnkey library for diving into graph deep learning research
M Liu, Y Luo, L Wang, Y Xie, H Yuan, S Gui, H Yu, Z Xu, J Zhang, Y Liu, …
Journal of Machine Learning Research 22 (240), 1-9
|
97* | 2021 |
| A reconfigurable three-finger robotic gripper
G Li, C Fu, F Zhang, S Wang
2015 IEEE International Conference on Information and Automation, 1556-1561
|
20 | 2015 |
| Artificial intelligence for science in quantum, atomistic, and continuum systems
X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y Xie, M Liu, Y Lin, Z Xu, K Yan, …
arXiv preprint arXiv:2307.08423
|
18 | 2023 |
| FPGA-based power efficient face detection for mobile robots
C Fu, Y Yu
2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), 467-473
|
10 | 2019 |
| Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
M Liu, C Fu, X Zhang, L Wang, Y Xie, H Yuan, Y Luo, Z Xu, S Xu, S Ji
NeurIPS 2021 AI for Science Workshop
|
8 | 2021 |
| Group Equivariant Fourier Neural Operators for Partial Differential Equations
J Helwig, X Zhang, C Fu, J Kurtin, S Wojtowytsch, S Ji
arXiv preprint arXiv:2306.05697
|
6 | 2023 |
| Lattice convolutional networks for learning ground states of quantum many-body systems
C Fu, X Zhang, H Zhang, H Ling, S Xu, S Ji
arXiv preprint arXiv:2206.07370
|
5 | 2022 |
| Mechanical design and gait plan of a hydraulic-actuated biped robot
Y Lei, J Luo, C Fu, JW Yang, Y Fu
2015 IEEE International Conference on Mechatronics and Automation (ICMA …
|
5 | 2015 |
| A Latent Diffusion Model for Protein Structure Generation
C Fu, K Yan, L Wang, WY Au, M McThrow, T Komikado, K Maruhashi, …
The Second Learning on Graphs Conference
|
3 | 2023 |
| A Latent Diffusion Model for Protein Structure Generation
C Fu, K Yan, L Wang, WY Au, M McThrow, T Komikado, K Maruhashi, …
arXiv preprint arXiv:2305.04120
|
3 | 2023 |
| A LESS robotic arm control system based on visual feedback
T Ma, C Fu, H Feng, Y Lv
2015 IEEE International Conference on Information and Automation, 2042-2047
|
3 | 2015 |
| Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction
C Fu, J Helwig, S Ji
The Second Learning on Graphs Conference
|
2023 |
Conclusion
Cong Fu’s research contributions in the fields of graph deep learning, robotic grippers, and artificial intelligence for science have significantly advanced these areas. Through the development of tools like Dig and innovative designs of robotic grippers, Cong Fu has made graph deep learning research and robot manipulation more accessible and adaptable. Furthermore, his work on applying AI techniques to scientific domains has opened up new possibilities for faster and more accurate predictions and analysis.
With his expertise and dedication to research, Cong Fu continues to push the boundaries of these fields, inspiring future advancements and applications. As the intersection of deep learning and robotics continues to evolve, Cong Fu’s contributions will undoubtedly play a crucial role in shaping the future of these domains.
