Passionate about optimizing AI models and solving performance bottlenecks, I have experience in collaborating across teams to enhance verification coverage. My interest extends but not limited to the AI compiler stack, MLIR, and code generation. In my career, I have demonstrated proficiency in modifying and contributing to open-source codebases, including ONNXRuntime, PyTorch, MxNet, Caffe, and NetworkX. My contributions include implementing improvements to the static scheduler, resulting in a notable reduction in the time required to generate the final schedule. In addition to hands-on experience, I stay current with the latest advancements in the field by actively engaging with research papers. This commitment allows me to approach challenges with a fresh perspective and continuously contribute to the general problem solving perspective. I am enthusiastic about leveraging my skills and knowledge to drive innovation in AI, particularly in the areas of AI compiler stack, MLIR, and code generation. Let's connect and explore how we can collaborate to push the boundaries of what's possible in the world of artificial intelligence.