About me

I am currently in the job market, if you are interested in my research and work experience, please feel free to contact me at xli82@tulane.edu!

I am Xingyu Li, a Postdoctoral Fellow in the Department of Computer Science, Tulane University, collaborating with Prof. Lu Peng in pioneering research related to Secure and Private Machine Learning, specifically tailored for Medical and Healthcare applications.

I earned my Ph.D. degree in the Department of Electrical and Computer Engineering, Mississippi State University in May 2023, where I was under the distinguished supervision from Prof. Bo Tang, my dessertation title is . Prior to my doctoral studies, I completed my Master’s degree from Stevens Institute of Technology in 2017 and obtained my Bachelor’s degree from Xiamen University in 2015.

Between March and August 2023, I undertook a role as a Machine Learning Research Intern at Bosch. My contributions there centered on advancing foundational models for distributed and heterogeneous machine learning scenarios. Additionally, I gained practical experience as a Machine Learning Engineer Intern at Devron from August and December 2022, where I worked on federated learning applications for financial fraud detection and COVID-19 tracking.

My scholarly contributions primarily revolve around the following domains:

1. On-Device Learning on Foundational Models: Assessing the adaptability, scalability, and deployment of foundation models to on-device applications.
2. Generalization on Large Language and Vision-Language Models:
3. Federated Learning: Investigating the decentralized architecture for machine learning to enhance optimization robustness, data privacy, and model efficacy.
4. Continual Lifelong Learning: Developing bio-inspired artificial intelligence systems that adapt and learn throughout their lifetimes.

Please visit my Curriculum Vitae and Publications for more information!