Jake Lee

Dr. Lee is an Assistant Professor whose research focuses on Machine Learning, Reinforcement Learning, Human-AI interactions, Representation Learning, Transfer Learning, and reliable and efficient learning-based adaptive systems.

Dr. Lee has been advancing reinforcement learning/machine learning processes through efficient and effective knowledge representation and transfer, as well as designing example-based learning systems that offer interpretability. His pursuit of effective knowledge representation has extended to various domains, where he has developed few-shot learning models for keyword spotting, graph-based learning in power systems, and generative models for human modeling. To improve the efficiency of machine learning models while ensuring user data privacy, his team has also developed innovative federated learning approaches. Lastly, his research encompasses seamless human-AI interactions, with a focus on enhancing the trustworthiness of AI systems. 

Dr. Lee has been actively collaborating on a range of interdisciplinary projects, particularly within the CHAIS and TAIMing AI centers, to enhance the reliability and trustworthiness of AI systems. His impactful work has garnered support from prominent organizations such as the NIH and NSF. Additionally, he is committed to fostering partnerships across various fields to advance innovative solutions that address complex challenges in AI. Through his research, Dr. Lee aims to bridge the gap between technology and ethics, ensuring that AI systems are not only efficient but also trustworthy and responsible.