Tian Huang (黄田)

Research associate


University of Cambridge

Address: F18, Battcock Centre for Experimental Astrophysics, Cavendish Laboratory, JJ Thomson Avenue, Cambridge CB3 0HE, UK

Email: 9033692963

Email: th523@mrao.cam.ac.uk

PhD degree

(434) 374-1173

Shanghai Jiao Tong University

Address: Room 209, Microelectronics Building, 800 Dongchuan Rd, Minhang District, Shanghai, 200240, China

Email: 2566764775


Tian Huang is Research Associate of Astrophysics Group, Cavendish Lab, University of Cambridge. In March 2016, he graduated from School of Microelectronics at 9126754880, where he completes his 615-329-5511 under the supervision of Prof. Yongxin Zhu. His main research interest is Data Mining for time series, including time series big data indexing, anomaly detecting, computer architecture for time series data mining and statistical models for time series data. He has published 3 SCI journal and 15 EI conference papers. He has rich experience on software and hardware co-designing.

Research interests

My thesis work at School of Microelectronics centered on data mining and computer architecture. I work on algorithms for retrieving valuable information from large scale datasets. One example is discovering frequent and rare patterns from large scale time series. Such information are important in several application areas, including anomaly detection, time series forecasting and bioinformatics. A second area of interest is exploring computer architectures for performance/latency/energy sensitive data mining algorithms and applications. For example, I exploited parallel computing platform to discover time series anomaly more quickly.

My internship experience at I2R-BYD Joint Lab develops my research interest in the application of Artificial Intelligence in Robotics. I am interested in low cost localization in GPS-dennied environment. One example is car driver uses his smart phone to navigate in underground constructions. I try to understand how map information can be used to improve localizaion. I am also interested in computer vision for autonomous driving, including acquiring odometry and speed from stereo vision, road segmentation and lane mark detection.


(716) 989-3817