Research

As an AI Research Scientist, my work focuses on advancing the capabilities of Large Language Models (LLMs), Generative AI, and Natural Language Processing (NLP) to build intelligent, scalable, and deployable systems. I bridge cutting-edge research with real-world applications—developing models and frameworks that make AI more adaptive, reliable, and trustworthy in production environments.

I specialize in transformer-based architectures, foundation model development, and LLM fine-tuning, with a focus on practical innovations that improve efficiency, robustness, and interpretability across diverse domains.

Core Focus Areas

Large Language Models & Generative AI Designing and fine-tuning LLMs through supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and instruction tuning. My work emphasizes production-ready generative models optimized for accuracy, latency, and scalability.

Generative Architectures Working with transformers, diffusion models, GANs, and VAEs to create multimodal and text-to-X generation pipelines for content synthesis, simulation, and creative AI applications.

Model Training & Optimization Applying advanced training strategies—including transfer learning, knowledge distillation, and parameter-efficient fine-tuning—to maximize model performance while reducing compute cost and deployment overhead.

AI Interpretability & Transparency Developing explainability tools for model introspection through influence analysis, feature attribution, and attention visualization, enabling developers and stakeholders to better understand and trust model decisions.