Hello! I'm Naveen Mysore, a Research and Software Engineer with 8+ years of interdisciplinary experience bridging scientific research and production software engineering across machine learning, reinforcement learning, distributed systems, and causal inference. At UC Santa Barbara, I trained reasoning LLM models using reinforcement learning, and worked on causal structure discovery using graph neural networks, interpretable AI, Bayesian updates, and time-series forecasting. Previously at Salesforce and Dell EMC, I designed and shipped large-scale enterprise software systems: backend services, cloud infrastructure (AWS, Terraform), data pipelines, and distributed systems serving millions of users in production. What drives my research is a core belief: the hardest problems in AI for Science and AI for Health demand models that are not just powerful but trustworthy. The AI Alignment problem and the need for Interpretable AI are what drew me to causal reinforcement learning, building agents that reason about cause and effect, not just correlations, so we can deploy AI safely in domains like healthcare, climate, and scientific discovery. I bring the full stack to my research, from writing CUDA kernels and building distributed training infrastructure to deploying models behind production APIs. My work sits at the intersection of reasoning-capable AI systems, strong algorithmic foundations, and rigorous software engineering, grounded in a master's in Computer Science from UNC Charlotte and a bachelor's in Electrical Engineering from PES University. I'm always open to collaborations. If you're working on interpretable AI, causal reasoning, RL, or AI for scientific discovery and would like to work together, feel free to reach out at nmysore.work [at] gmail.com.