Contact Information
Email:
cssun@u.nus.edu
Office:
Computing 3 #02-20
11 Research Link
Singapore 119391
Lab:
PLSE@NUS
I am a Ph.D. candidate at the PLSE Lab, NUS School of Computing, working at the intersection of Trustworthy AI, LLMs, and Graph Learning. My research aims to make learning systems reliable and auditable by developing principled methods for robustness against adversarial threats and distribution shifts. Currently, I focus on LLM safety and explainable graph modeling, with specific interests in directionality- and risk-aware explanations. My work spans NLP, geometric learning, and large-scale time-series data analysis.
🎓 I am expected to graduate in early 2026 and am actively looking for job opportunities in (but not limited to) Singapore.
Research Interests
- Trustworthy AI
- Graph Representation Learning
- LLM Safety
- Explainability (XAI)
Education
- Ph.D. in Computer Science (Jan 2022 - Present, Expected Early 2026)
National University of Singapore
Thesis: Trustworthy Geometric Learning: From Structural Biases to Risk-Aware Robustness
Advisor: Prof. Dong Jin Song - Master of Computing in Artificial Intelligence (Aug 2020 - Dec 2021)
National University of Singapore - Bachelor of Engineering in Computer Science (Sep 2015 - Aug 2019)
Xidian University, Xi'an, China
Work Experience
- Research Assistant (Jan 2026 - Present)
NUS School of Computing
Working on AISG project: Trustworthy Geometric Learning
Host: Prof. Dong Jin Song and Dr. Zhang Yedi - Research Assistant (July 2021 - Dec 2021)
NUS School of Computing
Built and maintained a reproducible evaluation pipeline for trustworthiness assessment of deep learning systems, emphasizing uncertainty-aware reliability analysis. Contributed to work published at ASE 2022 (InputReflector).
Host: Prof. Dong Jin Song and Prof. Xiao Yan - Research Intern (Jan 2020 - June 2020)
NUS-Singtel Cyber Security R&D Lab
Developed a white-box testing approach for deep neural networks by leveraging intermediate-layer signals and density estimation for reliability assessment. Contributed to work published at ICSE 2021 (Self-Checking Deep Neural Networks).
Host: Prof. David S. Rosenblum - Research Intern & Algorithm Engineer (Jan 2018 - June 2018)
JD Intelligent Cities Research, JD.com
Prototyped a distributed DQN research system on spatiotemporal data using the Ray execution framework; focused on training scalability and system performance profiling.
Host: Prof. Yu Zheng and Dr. Junbo Zhang
Skills
Programming Languages: Python
Frameworks: PyTorch, TensorFlow, JAX, Ray, TorchGeometric
Services & Awards
- Reviewer/Program Committee
- NeurIPS, ICML, AAAI, WWW, FSE, ASE (2022-2026)
- Teaching Assistant
- CS5232 Formal Specification and Design Techniques (2024)
- CS4218 Software Testing (2023)
- CS4211 Formal Methods for Software Engineering (2022)
- Awards and Grants
- Graduate Student Travel Grant 2025: IEEE Symposium on Security and Privacy 2025
- Graduate Student Travel Grant 2024: AAAI Conference on Artificial Intelligence 2024
- Graduate Student Travel Grant 2022: 37th IEEE/ACM Automated Software Engineering (ASE)
- National University of Singapore Research Scholarship 2022-2026