research

 

Introduction

I am now working in Alibaba Group @Beijing Wangjing.

I obtained my Ph.D from Department of Computer Science at UC Berkeley, where I worked with Professor John Canny.

Prior to Berkeley, I studied Computer Science at Tsinghua University

You can download my CV here

Email: bjiang[at]cs.berkeley.edu

 


 

 

We have intern/full-time positions for student to work on challenge research/industry problems. Please drop me an email with your resume if you are interested.
New! We are organizing a summit on AI System/Framework (in Chinese). My slides (Eng)
New! We are hosting the 3rd Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD 2021
I shared our experience of online inference optimization at DataFunTalk 2020. My slides (Chn)
We hosted the 2nd Workshop on Visualization for Deep Learning at ICML2017

Research Interest

Machine learning algorithm and system (deep or not), Data Visualization

I am interested in building toolkits for modern data scientists who will usually work on prototyping new models or running experiments on large scale dataset. Our methodology includes but not limit to using hardware accelerations like GPU, providing implementation framework for machine learning algorithms, building visual interface for real-time control and monitoring.
I am also interested in interpreting deep learning algorthims (Either their behavior or training process).

Check out our BIDMach project and X-DeepLearning project.


Recent Papers

Truncation-Free Matching System for Display Advertising at Alibaba Jin Li, Jie Liu, Shangzhou Li, Yao Xu, Ran Cao, Qi Li, Biye Jiang, Guan Wang, Han Zhu, Kun Gai and Xiaoqiang Zhu [2021]
DCAF: A Dynamic Computation Allocation Framework for Online Serving System Biye Jiang, Pengye Zhang, Rihan Chen, Binding Dai, Xinchen Luo, Yin Yang, Guan Wang, Guorui Zhou, Xiaoqiang Zhu and Kun Gai [2020]
COLD: Towards the Next Generation of Pre-Ranking System Zhe Wang, Liqin Zhao, Biye Jiang, Guorui Zhou, Xiaoqiang Zhu and Kun Gai [2020]
XDL: an industrial deep learning framework for high-dimensional sparse data Biye Jiang, Chao Deng, Huimin Yi, Zelin Hu, Guorui Zhou, Yang Zheng, Sui Huang, Xinyang Guo, Dongyue Wang, Yue Song, Liqin Zhao, Zhi Wang, Peng Sun, Yu Zhang, Di Zhang, Jinhui Li, Jian Xu, Xiaoqiang Zhu, Kun Gai [2019]
Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics Biye Jiang, David M. Chan, Tianhao Zhang, John F. Canny [2018]