Tianqi Chen

Tianqi Chen is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. His research focuses on Artificial Intelligence, with particular emphasis on machine learning, deep learning, knowledge transfer, and lifelong learning. He is also interested in the intersection of machine learning and systems. Chen is a proponent of open source and open science, and his work includes the development of widely adopted machine learning systems such as Apache TVM, XGBoost, and Apache MXNet (co-creator). His research statement highlights his commitment to publishing algorithms in openly accessible mediums and building open-source systems that are widely adopted.

Tianqi Chen is a researcher specializing in Machine Learning and Systems. His work focuses on developing scalable and efficient machine learning systems, with a particular emphasis on tree boosting, deep learning optimization, and distributed computing. Chen has contributed to the creation of XGBoost, a widely used tree boosting system, and TVM, an automated end-to-end optimizing compiler for deep learning. His research also includes flexible and efficient machine learning libraries like MXNet, as well as advancements in stochastic gradient methods and collaborative filtering. Chen's publications span topics such as low-bit quantization for efficient large language model serving and hardware-software co-design for deep learning specialization.