LLM Reasoning: Currently working on this area.
LLM for Semiconductors: As a key strategic focus in Singapore, I have been involved in a series of semiconductor-related projects [16–18]. In collaboration with AMD, our DefectGPT [18] is pioneering work that utilizes LLMs for defect detection with limited electrical data.
NP-hard Optimization: I have worked on applying reinforcement learning [14] and LLMs [a few papers under review] to routing problems (e.g., the traveling salesman problem and vehicle routing) and robotics navigation [13]. This line of work could form the foundation for logistics and scientific discovery problems. One of the works was highlighted by A*STAR Research Magazine.
AI for Scientific Discovery: At A*STAR, I worked closely with talented researchers and international institutions (e.g., MIT and Cambridge) to apply cutting-edge AI technologies to important scientific fields such as computational chemistry [internal IPs], and parameter extraction for solar cells [under review].
Recommender Systems: This was my PhD topic at NTU. I applied graph-based algorithms [1, 7] and deep learning techniques (i.e., CNNs [10] and attentions [6, 9]) to achieve more personalized recommendations. In recent work, we systematically investigated the contribution of text reviews to recommender systems in the era of LLMs [to be released].
Deep Learning for Healthcare and Bioinformatics: I also enjoy supporting healthcare applications [15] and bioinformatics research [5, 8, 11] in my spare time. For example, I contributed to antibody discovery for potential mutated variants of COVID-19 [12].
"Double Blind" (LLM for Optimization)
Jianghan Zhu, Yaoxin Wu, Zhuoyi Lin*, Zhengyaun Zhang, Haiyan Yin, Zhiguang Cao, J.Senthilnath, Xiaoli Li.
[Under review]
"Double Blind" (LLM for Optimization)
Shengkai Chen, Zhiguang Cao, Jianan Zhou, Yaoxin Wu, J.Senthilnath, Zhuoyi Lin*, Xiaoli Li, Shili Xiang.
[Under review]
"Double Blind" (LLM for Recommendation)
Chee-Heng Tan, Huiying Zheng, Jing Wang, Zhuoyi Lin*, Shaodi Feng, Huijing Zhan, Xiaoli Li, J.Senthilnath.
[Under review]
"Double Blind" (LLM for Optimization)
Shaodi Feng, Zhuoyi Lin*, Yaoxin Wu, Haiyan Yin, Yan Jin, Kuan-Wen Chen, J.Senthilnath.
[Under review]
"Double Blind" (LLM for Optimization)
Shaodi Feng, Zhuoyi Lin*, Jianan Zhou, Cong Zhang, Jingwen Li, Kuan-Wen Chen, J. Senthilnath, Yew-Soon Ong.
[Under review]
"Double Blind" (Generalization in Deep Learning)
Zhuoyi Lin, Mohamed Ragab, Zekun Ren, Ji Wei Yoon, Tonio Buonassisi, Vijila Chellappan, and J. Senthilnath.
[Under review]
Feature Shapley: A Game-theoretic Approach to Discovering Arbitrary-order Feature Interactions
Zhuoyi Lin, Biao Ye, Xu He, Shuo Sun, Rundong Wang, J. Senthilnath, Rui Yin, Chi Xu, and Chee-Keong Kwoh.
[Under review]
High Temporal-Lateral Resolution Photoacoustic Microscopy Imaging with Dual Branch Graph Induced Fusion Network.
Zhengyuan Zhang, Xiangjun Yin, Zhuoyi Lin, Haoran Jin, Wenwen Zhang, Feng Qin, Arunima Sharma, Manojit Pramanik, Yuanjin Zheng.
[Under review]
Physics-Guided Proximal Diffusion Sampling for Enhanced Photoacoustic Microscopy
Zhengke Sun, Zhuoyi Lin, Zhengyuan Zhang, Huazhu Fu.
[Under review]
18. DefectGPT: Towards Multi-Class Defect Detection with Limited Electrical Samples
Zhuoyi Lin, Kaixin Xu, Aye Phyu Phyu Aung, Bernice Zee, Wen Qiu, JM Chin, J.Senthilnath.
The British Machine Vision Conference (BMVC' 25) (Acceptance rate: 31.9%)
17. Defect Detection and Localization using 2D Slicing Method on 3D X-ray Microscopy
A.P.P. Aung, Zhuoyi Lin, R.S. Pahwa, R.I. Made, and J. Senthilnath,
AI4X 2025 International Conference (AI4X'25)
16. A Spatial-Physics Inspired Model for 3D Spiral Sample Scanned by SQUID Microscopy
J. Senthilnath, J. Jayabalan, Zhuoyi Lin, A.P.P. Aung, C. Hao, K. Xu, Y.K. Lim, and F.C. Wellstood
IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA’25)
Zhengyuan Zhang, Zuozhou Pan, Zhuoyi Lin, Feng Qin, Arunima Sharma, Manojit Pramanik, Yuanjin Zheng
IEEE Transactions on Image Processing (TIP) (Impact Factor 2023: 10.8)
14. Cross-problem Learning for Solving Vehicle Routing Problems
Zhuoyi Lin, Yaoxin Wu, Bangjian Zhou, Zhiguang Cao, Wen Song, Yingqian Zhang, J.Senthilnath.
International Joint Conference on Artificial Intelligence (IJCAI' 24) (Acceptance rate: 14%）
Haoge Jiang, Niraj Bhujel, Zhuoyi Lin, Kong-Wah Wan, Jun Li, J. Senthilnath, Xudong Jiang
IEEE Transactions on Intelligent Transportation Systems (TITS) (Impact Factor 2022: 9.551)
12. PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction
Zhang Wan, Zhuoyi Lin, Shamima Rashid, Shaun Yue-Hao Ng, Rui Yin, Jayavelu Senthilnath, and Chee-Keong Kwoh
IEEE International Conference on Bioinformatics and Biomedicine (BIBM' 23) (Acceptance rate: 19.5%)
Rui Yin, Zihan Luo, Pei Zhuang, Min Zeng, Min Li, Zhuoyi Lin, Chee Keong Kwoh
Journal of Biomedical Informatics (JBI) (Impact Factor 2022: 8)
10. COMET: Convolutional Dimension Interaction for Deep Matrix Factorization
Zhuoyi Lin, Lei Feng, Xingzhi Guo, Yu Zhang, Rui Yin, Chee-Keong Kwoh, and Chi Xu
ACM Transactions on Intelligent Systems and Technology (TIST) (Impact Factor 2021: 10.489)
Zhuoyi Lin, Sheng Zang, Rundong Wang, Zhu Sun, J. Senthilnath, Chi Xu, and Chee-Keong Kwoh.
IEEE Transactions on Knowledge and Data Engineering (TKDE) (Impact Factor 2021: 9.235)
8. IAV-CNN: A 2D Convolutional Neural Network Model to Predict Antigenic Variants of Influenza A Virus
Rui Yin, Nyi Nyi Thwin , Pei Zhuang , Zhuoyi Lin, and Chee-Keong Kwoh
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) (Impact Factor 2018: 2.896)
7. GLIMG: Global and Local Item Graphs for Top-N Recommender Systems
Zhuoyi Lin, Lei Feng, Rui Yin, Chi Xu, and Chee-Keong Kwoh
Information Sciences (INS) (Impact Factor 2020: 6.795)
6. Embedding-Augmented Generalized Matrix Factorization for Recommendation with Implicit Feedback
Lei Feng, Hongxin Wei, Qingyu Guo, Zhuoyi Lin, and Bo An
IEEE Intelligent Systems (IEEE IS) (Impact Factor 2018: 4.464)
Rui Yin, Zihan Luo, Pei Zhuang, Zhuoyi Lin, Chee-Keong Kwoh
Bioinformatics (Impact Factor 2018: 4.531)
4. Learning from Multi-Class Positive and Unlabled Data
Senlin Shu, Zhuoyi Lin, Yan Yan, and Li Li
IEEE International Conference on Data Mining (ICDM' 2020) (Acceptance rate: 19.7%)
3. Can Cross Entropy Loss be Robust to Label Noise?
Lei Feng, Senlin Shu, Zhuoyi Lin, Fengmao Lv, Li Li, and Bo An
International Joint Conferences on Artificial Intelligence (IJCAI' 20) (Acceptance rate: 12.6%)
2. Information Theory-based Feature Selection: Minimum Distribution Similarity with Removed Redundancy
Yu Zhang, Zhuoyi Lin, and Chee-Keong Kwoh
International Conference on Computational Science, (ICCS' 20)
1. Fast Top-N Personalized Recommendation on Item Graph
Zhuoyi Lin, Lei Feng, Chi Xu, and Chee-Keong Kwoh
IEEE International Conference on Big Data, (Big Data' 19), 5th IEEE Workshop on Big Data Analytics in Supply Chains and Transportation
User-specific Recommender Systems: From Data to Model.
Nanyang Technological University, Singapore, 2022
Supervisors: Assoc. Prof. Kwoh Chee Keong, Dr. Xu Chi
Committee Members: Prof. Sinno Jialin Pan, Dr. Allan Neng-Sheng Zhang
Here is my E-mail: zhuoyi.lin@outlook.com