The 2016 Winner of the MLconf Industry Impact Student Research Award, Sponsored by Google, Has Been Announced!


The winner of the 2016 MLconf Industry Impact Student Research Award, which is sponsored by Google has been announced. Our committee has reviewed several nominees and found Tianqi Chen’s research on XGBoost and MXNet to be the most impactful and interesting for future developments in industry.

Tianqi Chen is the winner of the 2016 MLconf Industry Impact Student Research Award! This announcement was made on Friday, November 11th, 2016 in San Francisco. Tianqi accepted via a video acceptance speech (available here).

In 2015, there were 2 winners of the award, including Viriginia Smith (UC Berkeley) whom presented on November 11, 2016 at MLconf SF and Furong Huang (UC Irvine)  whom presented at MLconf NYC in April 2016.

Tianqi has been invited to present his work on XGBoost in Seattle at MLconf in 2017. His advisor, Dr. Carlos Guestrin, has presented at MLconf numerous times as well.

Tianqi works at the intersection of learning and systems. He has built many scalable learning systems. His research focuses on scalable boosted trees and work on a package XGBoost, which is widely used for competitive ML and in the industry for supervised learning problems where you train data to predict another variable because it provides parallelized boosted trees that run in an efficient and accurate way. XGBoost is available in many distributed environments for production such as Hadoop, MPI, SGE, Flink, & Spark, and in many preferred languages such as python, R, Julia, java, scala. The framework constructs tree ensembles. It is not easy to train trees at once, so XGBoost takes an additive model and trains one tree, uses the information from it and adds another tree. Then, after the tree ensembles are created, the model needs to be regularized. First, the complexity is defined in order to regularize the model and better understand what information is being learned. Regularization is one part most tree packages treat less carefully, or ignore. This is because the traditional treatment of tree learning emphasized improving impurity, while complexity control was left to heuristics. By defining it formally, we understand it better and it works well in practice. One can derive a structure score and a goodness-of-fit measure for the tree ensemble.

Tianqi is also well-known for his contribution to work on MXNet. MX stands for mix and minimize and is a dynamic dependency scheduler that automatically parallelizes both declarative and imperative operations. The heart of MXNet is NNMVM an intermediate layer just like LLVM. the abstraction to NNVm allows several just in time code optimization s that significantly boost the performance. MXNet as a competitor to TensorFlow is widely recognized as it has been heavily invested in by Amazon.



Tianqi holds a bachelor’s degree in Computer Science from Shanghai Jiao Tong University, where he was a member of ACM Class, now part of Zhiyuan College in SJTU. He did his master’s degree at Changhai Jiao Tong University in China on Apex Data and Knowledge Management before joining the University of Washington as a PhD. He has had several prestigious internships and has been a visiting scholar including: Google on the Brain Team, at Graphlab authoring the boosted tree and neural net toolkit,  at Microsoft Research Asia in the Machine Learning Group, and the Digital Enterprise Institute in Galway Ireland. What really excites Tianqi is what processes and goals can be enabled when we bring advanced learning techniques and systems together. He pushes the envelope on deep learning, knowledge transfer and lifelong learning. His PhD is supported by a Google PhD Fellowship.