Sheng Yang currently serves as a Principal Machine Learning Engineer at Palo Alto Networks, where he leverages his extensive expertise in full-stack data science and machine learning to drive innovative solutions in cybersecurity. With a strong focus on Retrieval-Augmented Generation (RAG) and fine-tuning large language...
Sheng Yang currently serves as a Principal Machine Learning Engineer at Palo Alto Networks, where he leverages his extensive expertise in full-stack data science and machine learning to drive innovative solutions in cybersecurity. With a strong focus on Retrieval-Augmented Generation (RAG) and fine-tuning large language models (LLMs), Sheng is at the forefront of developing advanced chatbots that enhance user interaction and streamline security operations. His work involves integrating generative AI techniques to create intelligent systems capable of understanding and responding to complex queries, thereby improving the overall efficiency of cybersecurity measures.
Sheng's proficiency in programming languages such as C++, Scala, and Python, combined with his deep knowledge of frameworks like TensorFlow, Keras, and PyTorch, allows him to build robust machine learning models that can handle vast amounts of data. His experience with Monte Carlo simulations and molecular dynamics further enriches his analytical capabilities, enabling him to model and predict system behaviors with high precision.
At Palo Alto Networks, Sheng is also involved in key projects that focus on enhancing threat detection and response through AI-driven insights. His ability to bridge the gap between theoretical machine learning concepts and practical applications makes him a vital asset to the team. As the landscape of cybersecurity continues to evolve, Sheng Yang remains committed to pushing the boundaries of what is possible with AI, ensuring that organizations can stay one step ahead of emerging threats.