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Update time:2016-09-13
High Dimensional Information Processing Lab

Research Directions: big data/graphical data visualisation, high dimensional visual analysis, visual information retrieval, image/video processing, visual languages,  computational aesthetics, generative art, human-computer interaction, information visualisation, deep learning theory, computer simulation of water colour painting, optimisation of cloud computing systems, network and SDN
Application Areas: art, design, marketing, architecture, software engineering, social networks, human-computer interaction, medical/astronomical big data analysis and visualisation
R & D Successes: In last five years, the laboratory has made a speedy progress directed by three Tianjin “1000 Talent Plan” holders. It has hosted over 30 postgraduate students. Nearly 100 papers have been published in international high ranked journals and conferences. It also hosts 14 projects with total  funding close to tens of millions, including technology support plan of the Ministry of Science and Technology, projects of 863 Plan, NSFC small projects and young scientist projects, and projects of the Ministry of Education.
 
Data Science Lab
 
Research directions: Machine Learning, social network analysis, Text mining, knowledge modeling, Data Governance and feature engineering, distributed computing (Hadoop, Spark), software testing, security and data protection (cryptography, coding theory).
Research and Innovation: Hosted by Tianjin 1000-talents program, Professor Francoise Soulie heads the data science laboratory. The research team includes three associate professors and one lecturer. The team develops research on the theory, methods and technology of data science, including the theoretical algorithms of big data, data modeling and data visualization. The team is involved in various national 863 projects, National Natural Science projects and the project supported by Foundation for Returned Overseas Chinese Scholars of Ministry of Education, etc. It has published more than 50 academic papers.
Application areas: recommender systems, intelligent transportation, automatic debugging, voice information security, ocean data processing, wind speed forecasting, astronomical image knowledge discovery, etc.
 
Embedded Software Engineering Lab
 
Research Interests: Embedded system, energy-efficient computing, approximate computing
Our lab focused on embedded software engineering modeling, reliability theory, software testing methods, and energy-efficient computing. We proposed a method of designing hardware-in-the-loop simulation platform based on automotive ECU, which has been already successfully applied to the manufacturers of new energy vehicles. Besides, we proposed a novel power management policy based on pulsed operating mode, and a novel device management solution of Linux based on power supply tree.
Our research has sponsored by CAEP and other companies. Moreover, the lab has established a long-term relationship with several IT enterprises, such as Intel, Microsoft, IBM CDL, etc.