Xi Ye

Area of Study / Keywords

natural language processing machine learning artificial intelligence

About

Xi Ye obtained his PhD from the Department of Computer Science at UT Austin, and his Bachelor's degree from the School of Software, Tsinghua University.

Research

My research is primarily in the field of Natural Language Processing and Machine Learning, with an emphasis on improving the explainability of large language models and enhancing their reasoning capabilities. I also work on program synthesis.

Recently, I am interested in the these directions:

• Make LMs better at reasoning over general, open-ended tasks beyond verifiable domains
• Improve robustness and efficiency of RL training for LM reasoning

• Build stronger coding agents leveraging symbolic tools

CMPUT 267 - Machine Learning I

This course introduces the fundamental statistical, mathematical, and computational concepts in analyzing data. The goal for this introductory course is to provide a solid foundation in the mathematics of machine learning, in preparation for more advanced machine learning concepts. The course focuses on univariate models, to simplify some of the mathematics and emphasize some of the underlying concepts in machine learning, including: how should one think about data, how can data be summarized, how models can be estimated from data, what sound estimation principles look like, how generalization is achieved, and how to evaluate the performance of learned models. Prerequisites: CMPUT 174 or 274, or ENCMP 100; one of MATH 100, 114, 117, 134, 144, or 154. Corequisites: CMPUT 175 or 275; CMPUT 272; MATH 102, 125, 126, or 127; one of STAT 151, 161, 181, 235, 265, SCI 151, or MATH 181.

CMPUT 656 - Topics in Artificial Intelligence

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