Artificial Intelligence • Computer Vision • Vision-Language Models • Reinforcement Learning
About Me
My research lies at the intersection of Artificial Intelligence, Machine Learning, Computer Vision, Vision-Language Models, and Reinforcement Learning. I am particularly interested in developing intelligent systems that combine perception, reasoning, and decision-making to solve real-world problems. My recent work spans visual reasoning, multimodal AI, autonomous perception, and intelligent surveillance.
Master's Thesis
Gains and Trade-offs of Reinforcement Learning for Reasoning in Vision-Language Models
This research investigates how reinforcement learning can improve reasoning capabilities in Vision-Language Models (VLMs). The work explores modern post-training techniques, including Group Relative Policy Optimization (GRPO), to enhance multimodal reasoning while analyzing the trade-offs between reasoning performance, computational efficiency, and model generalization. The study evaluates various prompting strategies, reward designs, and fine-tuning methods across visual reasoning benchmarks to better understand their impact on accuracy and inference efficiency.
Relevant Coursework
- Machine Learning Principles and Techniques
- Deep Learning: Theory and Practice
- Simulation Modeling and Analysis
- Engineering Statistics
- Probability and Stochastic Processes
- Mathematical Optimization Techniques
- Applied Integer Programming
- Quantitative Models for Supply Chain Management
