I am a Computer Engineering graduate from Isfahan University of Technology (IUT), ranked 1st in Software Engineering Specialization and 3rd among all Computer Engineering peers. I have been fortunate to work with leading research groups, including the Trustworthy Machine Learning and Reasoning (TMLR) Group at Hong Kong Baptist University under Prof. Bo Han and Dr. Qizhou Wang, the Structured Machine Learning Lab at Simon Fraser University under Prof. Oliver Schulte, and a Research Intern at the Max Planck Institute for Software Systems (MPI-SWS) working with Prof. Sadegh Soudjani and closely collaborating with Prof. Ashutosh Trivedi. Since July 2026, I have joined Nanyang Technological University (NTU), Singapore, as a Research Assistant in the School of Electrical and Electronic Engineering (EEE), under the supervision of Prof. Wanhua Li.
Research Interests
Trustworthy Machine Learning, Agentic AI, Safety AI, Cyber-Physical Systems, Formal Methods, Formal Verification
Honors and Awards
- J2C Certification - Transactions on Machine Learning Research (TMLR) - 2026
- B.Sc. Rank: 1st/20 in Software Engineering Specialization, 3rd/89 among all CE students - 2025
- Highest Grade for Undergraduate Thesis Project - Isfahan University of Technology, 2025
- 1st Place in ICPC - Isfahan University of Technology, 2023
- 10th Place in the 46th ICPC West Asia Regional Contest - 2023
- 3rd Place in National Machine Learning Competition - 2022
- 1st Place in ICPC - Isfahan University of Technology, 2022
Bachelor of Computer Engineering [2021–2025]
Isfahan University of Technology (IUT)
GPA: 18.63/20, Ranked 1st in Software Engineering Specialization and 3rd overall
Research Experience
Developed LLM-guided agentic frameworks and benchmarks for barrier certificate synthesis in the safety verification of dynamical systems, and contributed to other work on formal methods and temporal logic.
Working on multi-label event detection for the ROAD-R autonomous driving dataset, focusing on improving label relationship modeling through graph-based correlation learning
Proposed a forgetting mechanism for LLM fine-tuning that categorizes tokens into positive/negative sets via influence scores, learning from positive while unlearning negative tokens to improve generalization
Exploring and analyzing the capabilities and challenges of both Large Language Models and Vision-Language Models. The work contributed to understanding how these models can be improved for trustworthy and reliable AI applications
