1. Psychological Steering in LLMs: An Evaluation of Effectiveness and Trustworthiness, arXiv, 2025.
2. Mechanistic Interpretability of Emotion Inference in Large Language Models, ACL, 2025.
3. Theoretical Insights into Overparameterized Models in Multi-Task and Replay-Based Continual Learning, TMLR, 2025.
4. GABRIL: Gaze-Based Regularization for Mitigating Causal Confusion in Imitation Learning, IROS 2025.
5. Hybrid Learners Do Not Forget: A Brain-Inspired Neuro-Symbolic Approach to Continual Learning, arXiv, 2025.
6. A Distinct Unsupervised Reference Model from The Environment Helps Continual Learning, arXiv, 2023.
7. Generative vs. Discriminative: Rethinking The Meta-Continual Learning, NeurIPS, 2021.
Research Experience
1. Conducting research on the trustworthiness of large language models at USC.
2. Developed brain-inspired algorithms for the meta-continual learning problem at Sharif University of Technology.
Education
1. Ph.D. student in Computer Science at the University of Southern California (USC), advised by Sai Praneeth Karimireddy.
2. Master's in Computer Engineering from Sharif University of Technology, supervised by Mahdieh Soleymani.
3. B.Sc. in Electrical Engineering from Sharif University of Technology, jointly supervised by Mahdi Shabany and Zahra Kavehvash.
Background
Research interests include the trustworthiness of large language models (LLMs) and agentic systems, particularly inference-time failure modes such as hallucinations, insufficient diversity, and reasoning breakdowns. Through data-centric analysis, interpretability tools, and targeted synthetic data generation, aims to establish principled mechanisms for diagnosing, attributing, and correcting these weaknesses, thereby improving real-world robustness and safety.
Miscellany
Completed an automatic object detection project under clothing in millimeter-wave images as a senior AI researcher at Basir Wave Tech.