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Interdisciplinary Approaches to Computational Social Science

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Interdisciplinary Approaches to Computational Social Science

  • 講者王威廉 先生 (PhD candidate in the Language Technologies Institute of School of Computer Science at Carnegie Mellon University)
    邀請人:馬偉雲
  • 時間2015-10-29 (Thu.) 10:00 ~ 12:00
  • 地點資訊所新館106演講廳
摘要

The rapid development of machine learning techniques and the abundantly available data are making many fundamental changes to how scientific research is conducted in many disciplines, from finance to law to behavioral science. However, the complex nature of these problems require interdisciplinary approaches from many areas, including statistics, natural language processing, data mining, and domain knowledge. In this talk, I will introduce some of my recent work on statistical machine learning methods for data science, including 1) joint work with economists from Columbia and London School of Economics on Bayesian inference and latent variable models for studying the slavery-related United States property law judgements, with a special focus on shifts in opinions on controversial topics across different regions; 2) a nonparanormal model that can predict financial risks from earning calls, as well as its multimodal extension where we generate viral Internet memes descriptions from images; 3) and a simple embedding based data augmentation method for computational behavior analysis and supervised text classification using Twitter data.

 

BIO

William Wang (@王威廉) is a final-year PhD student at the Language Technologies Institute (LTI) of the School of Computer Science, Carnegie Mellon University. He works with William Cohen on designing scalable learning and inference algorithms for statistical relational learning, knowledge reasoning, and information extraction. He has published about 30 papers at leading conferences and journals including ACL, EMNLP, NAACL, IJCAI, CIKM, COLING, SIGDIAL, IJCNLP, INTERSPEECH, ICASSP, ASRU, SLT, Machine Learning, and Computer Speech & Language. He receives best paper awards (or nominations) at ASRU 2013, CIKM 2013, EMNLP 2015, and FLAIRS 2011, a best reviewer award at NAACL 2015, the Richard King Mellon Presidential Fellowship in 2011, and he is a Facebook Fellowship finalist for 2014-2015 and 2015-2016. He is an alumnus of Columbia University, and a former research scientist intern of 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.