Since 2005
Established in 2005, the AI and Big Data Laboratory is a research and education center focused on developing future technology leaders and advancing innovative applications. Its work covers artificial intelligence, big data analysis and algorithm development, combining academic training with practical experience.
The laboratory also applies AI and big data techniques to sports science. Its research examines how to analyze and improve athletic performance through data collected during training and competition, and how those findings can inform individualized training plans and strategies.
Through continued learning and collaboration with industry, the laboratory develops information technology expertise and helps train data analysis professionals.
Technical expertise
Areas of study include artificial intelligence, big data, algorithms, database management, programming, network management and optimization. Students connect theoretical knowledge with practical applications and learn to use technology to address real problems.
Educational goals
The laboratory aims to prepare information analysis professionals who can solve complex problems with algorithms and data. Industry collaboration gives students opportunities to work on site, improve processes, and develop data integration and analysis skills.
Related courses
Database management
Students learn to design, build and manage modern database systems. Topics include relational databases, database design, SQL, performance optimization, data modeling and normalization. Security and backup strategies support data integrity and availability. Practical work uses systems such as SQL Server and PostgreSQL.
Business intelligence
This course introduces data analysis techniques for business decisions, including data mining, visualization, reporting and performance management. Students learn to combine heterogeneous data into useful insights, analyze and present it with business intelligence tools, and consider its role in competitiveness, operations and innovation.
Machine learning and big data
Topics include machine learning fundamentals, algorithm design, model training and evaluation, and big data processing. Practical cases help students learn how to extract useful information from large datasets.
Sports science
This course studies physiological and mechanical phenomena during movement. Topics include biomechanics, measurement and data collection, data analysis and computer vision. Students learn to analyze athletic performance and understand individualized training strategies.
Laboratory advisor
See the advisor profile for expertise and the talks listed on the original site.