Deep neural networks have revolutionalized machine learning, natural language processing, and computer vision in the past 6 years. But what exactly are the innovations and the impacts? Can deep learning help computational social science? What are some issues and challenges at the intersection of machine learning and computational social science? In this talk, I will give a gentle introduction to the recent advances of deep neural network techniques. I will describe some of our key studies that leverage big data and machine learning for computational social science in a bottom-up fashion. Finally, I will introduce the on-going studies at UCSB's Natural Language Processing lab (http://nlp.cs.ucsb.edu/),

and discuss some of the key research challenges ahead of us. 

 

William Wang is an assistant professor in the Department of Computer Science at the University of California, Santa Barbara. His involvement with CITS stems from his research interests in computational social science and the study of the dissemination of misinformation. He has broad interests in machine learning approaches to data science, including statistical relational learning, information extraction, computational social science, speech, and vision. William has over 40 papers in leading conferences and journals, with numerous best paper awards. An alumnus of Columbia University, he received his Ph.D. in Computer Science at Carnegie Mellon University. He has also garnered an IBM Faculty Award, and the Richard King Mellon Presidential Fellowship in 2011. He has also worked for Yahoo! Labs, Microsoft Research Redmond, and University of Southern California. In addition to research, William enjoys writing scientific articles that impact the broader online community: His microblog has more than 2,000,000 views each month, and his opinions have appeared in major outlets such as Wired, VICE, Fast Company, and Mental Floss.