As a graduate researcher at the Pilanci Research Group at Stanford University, I oversee diffusion model training experiments on large datasets for controllable musical audio generation. I also design new AI model architectures and leverage LLMs for synthetic data generation, advancing the state-of-the-art in deep generative models for music and art.In addition, I guide and mentor students through independent projects spanning many domains, such as musical audio, computer vision, and NLP, as an Inspirit AI mentor. I also teach several courses on algorithms, natural language understanding, and randomized algorithms and probabilistic analysis as a course assistant at Stanford, sharing my passion and knowledge with over 1000 students.I graduated from Stanford University with a Bachelor's and am currently completing a Master's degree in Computer Science, specializing in artificial intelligence. I am interested in using machine learning and AI to enhance the quality of human life, as well as exploring AI alignment and interpretability.