Eric P. Xing

About

Eric P. Xing is a leading figure in machine learning, with key research areas spanning statistical learning theory, deep learning, and natural language processing. His major contributions include pioneering work on scalable deep kernels with recurrent structure, which addresses the critical challenge of modeling sequential data—where order matters—across diverse fields like speech, robotics, finance, and biology. By developing expressive closed-form kernel functions, Xing enabled more efficient and accurate learning from time-series and ordered data, bridging gaps between kernel methods and deep architectures. His most-cited paper on this topic has garnered 42 citations, reflecting its influence in advancing both theory and application. Xing’s work is notable for its interdisciplinary impact, providing foundational tools that empower researchers to tackle complex sequential problems. As a professor at Carnegie Mellon University and a former president of the International Machine Learning Society, his achievements include mentoring numerous students who have become leaders in AI, and his research continues to shape how we model dynamic systems. For students and researchers, Xing’s contributions exemplify how elegant mathematical frameworks can drive practical breakthroughs in data-driven science.

Research Focus

Key Achievements

Top Papers

1. 1
   Learning Scalable Deep Kernels with Recurrent\nStructure

   42 citations · 2017
2. 2
   Learning Scalable Deep Kernels with Recurrent Structure

   22 citations · 2016

Key Collaborators

Contact & Links