Artificial Intelligence and Machine Learning Laboratory
Location:
Room C501, at the 5th floor of Block C.
Description:
The AI and ML laboratory was established to explore the theories and technologies on the AI-related areas, with a focus on the researches and applications of cutting-edge technologies such as machine learning, natural language processing, computer vision, robotics, intelligent manufacturing and so on.
Equipment:
This laboratory has built a new generation AI super-computing system, designed mainly for the scientific computing, such as deep learning, machine learning and big data analysis. This system is consisted of three high performance GPU-based server clusters that equipped with 32 NVIDIA V100 GPU cards. It can provide powerful parallel processing capabilities reaching 4 PETA (4x10^15) FLOPS (floating-point computing per second). These supercomputers are also equipped with a high-capacity, high-speed SSD flash storage system over 100TB. Such improved performance enables lots of users to run their programs concurrently through the Docker containers technology.
Research Projects:
Open World Learning Theory, Methods, and Industrial Applications in Complex Scenarios, 2024-2026.
STEP Perpetual Learning based Collective Intelligence: Theories and Methodologies,2019~2022.
Face Structuralization Analysis Based on Video Spatial-temporal Information Modeling, 2019.08~2022.09
Research on complex data analysis and knowledge acquisition based on multiple subtleties and three decision-making, 2019.08~2022.09
Particle Computing Method for Information Fusion and Knowledge Acquisition of Multi-source Fuzzy Paste Data Set, 2019.08~2022.09
Multi-modal human feature recognition algorithm and system, 2019.06~2022.06
Intelligent Big Data Analysis for Biomedical and Astronomical Applications,2018.11~2019.11
Research on living detection techniques in face recognition, 20118.10~2021.10
Permanent learning based on overcoming contradictions, 2016.11~2019.11
Research on the Theory and Method of Incremental Calculation of Attribute Reduction Based on Rough Set Model, 2016.03~2019.03
