[PAST EVENT] Mining Social Sensing Data: Representation, Modeling, and Applications
Mining Social Sensing Data: Representation, Modeling, and Applications
Huajie Shao, UIUC
Monday, 3/1/2021, Noon 12:00 PM
Social sensing has emerged as a new knowledge acquisition paradigm due to the increasing connections among humans, intelligent devices, and the physical world. It uses humans as sensors to collect information about external physical events, such as disasters, traffic, and protests. However, social sensing data is noisy, multi-modal, and correlated. My research goal is to learn the latent variables from social sensing data with unsupervised learning to advance various applications, including misinformation detection, link prediction, and disentangled representation learning. In this talk, I will first introduce unsupervised truth discovery that extracts reliable information from the observations on social media. Then I will present a new controllable deep representation learning model to disentangle latent variables from unstructured data. Finally, I envision my future work on social sensing.
Huajie Shao is currently a Ph.D. student of Computer Science at University of Illinois at Urbana Champaign (UIUC). His research interests lie at the intersection of data mining, machine learning, and NLP, with particular focus on mining social media data. His research work has resulted in more than 30 research papers in top-tier international conferences and journals, including ICML, WWW, VLDB, SenSys, INFOCOM, ICDCS, TOC, TPDS, and TSP. He received SenSys’20 Best Paper Award, FUSION’19 Student Paper Award, UbiComp’19 Distinguished Paper Award, and ICCPS’17 Best Paper Award. In addition, his paper published in Trans. on Signal Processing (TSP) is nominated for the best paper in the past six years in the Signal Processing Society.
Prof. Qun Li