报告题目：Deep Learning's non-traditional applications
时间地点：2019年5月9日（周四）上午10:30-11:30，计算机楼313会议室
报告摘要：Deep learning, also known as deep structured learning or hierarchical learning, is part of a broader family of machine learning methods based on learning data representations. In recent years, deep learning has been mainly applied to a few fields, including computer vision, speech recognition and audio processing etc. In this talk, I will focus on some non-traditional applications that deep learning can also be applied to and generated good results. These applications include sensor data analytics for equipment remaining useful life (RUL) prediction and human activity recognition, traffic crowd density prediction and traffic anomaly detection.
报告人简介：Dr. Li Xiaoli is Head of Data Analytics Department and a principal scientist at Institute for Infocomm Research, A*STAR Singapore. He also holds adjunct position at School of Computer Science and Engineering, Nanyang Technological University. His research interests include data mining, machine learning, AI, and bioinformatics. He has served as (senior) PC member/workshop chair/session chair in leading data mining and AI related conferences, such as KDD, ICDM, SDM, PKDD/ECML, WWW, IJCAI, AAAI, ACL and CIKM. He has published more than 180 peer-reviewed papers, including top tier conferences, such as KDD, ICDM, SDM, PKDD/ECMLICDE, ICML, IJCAI, AAAI, ACL, SIGIR, EMNLP, CIKM, UbiCom etc, as well as some top tier journals such as IEEE Transactions TKDE, IEEE Transactions on Reliability, Bioinformatics. Some of his representative research publications include: positive unlabelled based learning (more than 2000 citations), social/biological network mining (more than 1000 citations). He also received 4 Best Paper Awards from reputable international conferences and 2 Best Performance Awards from international benchmark competitions. With rich translational experience in working with industry, Dr Li has led over 10 R&D projects in collaboration with industry partners across sectors, including leading aerospace companies, banks, telecom companies, insurance companies etc.