I am a PhD student in Computer Science at the University of Texas at El Paso, specializing in Large Language Models (LLMs), multimodal learning, efficient deep-learning architectures, and scalable model training pipelines. My research spans transformer optimization, robustness, alignment, self-supervised learning, active learning, and real-world AI deployment, with multiple publications across major venues including PoPETs, AAAI (ICWSM), HyperText, and ASONAM. I currently work as a Research Assistant in the SUPREME Lab, where I build and evaluate LLMs, develop distributed training workflows (FSDP), design multimodal architectures, and explore techniques such as PEFT/LoRA, RAG, generative modeling, and behavior modeling. My work focuses on creating reliable, efficient, and trustworthy AI systems. Previously, I spent three years as a Software Engineer at Samsung R&D, delivering large-scale software systems, collaborating across global teams, and contributing 800+ commits and 300+ code reviews. I received Samsung’s Outstanding Collaborator Award and was selected for a technical trip to South Korea for technology transfer. Across academia and industry, I bring a blend of research depth and engineering discipline, with a strong foundation in Python, C++, PyTorch, data pipelines, and large-scale ML development. I enjoy solving challenging problems, working on frontier-model research, and building AI systems that make a real impact. I am passionate about: Large Language Models (LLMs), multimodal & foundation models Scalable deep learning systems & distributed training Generative AI, retrieval-augmented systems, and evaluation science Safety, robustness, and trustworthy AI Applying AI to socially meaningful domains like healthcare, security, and autonomy I’m always open to connecting with researchers, engineers, and collaborators working in AI, ML, NLP, LLMs, foundation models, and applied research.