Xiaoxia Wu


Xiaoxia (Shirley) Wu (吴晓霞)

Email: my first name and my last name AT microsoft dot com

Google Scholar

About me

I am currently a researcher at Microsoft, where I work on exciting and cutting-edge methods to reduce the time/budget in large-scale neural networks training. More information, please check deepspeed.ai. My research interests are in the areas of large-scale optimization and, more broadly, machine learning. My Ph.D. study is about efficient and robust methods (to hyperparameter tuning) such as adaptive gradient descent and batch normalization. I am always interested in chatting about research opportunities and collaboration.

I was a postdoctoral research fellow mentored by Rebecca Willett at University of Chicago and Toyota Techonological Institute at Chicago. I have successfully completed the Ph.D. program at The University of Texas at Austin, where I was fortunately advised by Rachel Ward and informally co-advised by Léon Bottou. I was a research intern at Facebook AI Research (New York office) during Fall 2017, and a research intern at Google working with Ethan Dyer and Behnam Neyshabur during Summer 2020.

I hold an M.Sc. with Distinction at the University of Edinburgh in Financial Mathematics. Before that, I spent a wonderful four-year in the Department of Mathematics and Applied Mathematics at Shantou University where I was awarded Li-Kashing Scholarship to participate in Semester at Sea. I am from Guangdong, China, speaking Cantonese and Hakka.

Papers and Preprints (updated on Nov 2021)

*: indicating equal contribution.

Teaching Assistant at UT Austin