Dr. Chris Callison-Burch is the Aravind K Joshi term assistant professor in the Computer and Information Science Department at the University of Pennsylvania. Before joining Penn, he was a research faculty member at the Center for Language and Speech Processing at Johns Hopkins University for 6 years. He was the Chair of the Executive Board of the North American chapter of the Association for Computational Linguistics (NAACL) from 2011-2013, and he has served on the editorial boards of the journals Transactions of the ACL (TACL) and Computational Linguistics. He is a Sloan Research Fellow, and he has received faculty research awards from Google, Microsoft and Facebook in addition to funding from DARPA and the NSF. Chris teaches a semester-long course on Crowdsourcing at Penn.

Dr. Lyle Ungar is a Professor of Computer and Information Science at the University of Pennsylvania. He also holds appointments in several other departments in the Engineering, Medicine, and Business Schools. Dr. Ungar received a B.S. from Stanford University and a Ph.D. from M.I.T. He has published over 200 articles and is co-inventor on eight patents. His current research includes machine learning, data mining, and text mining, and uses social media to better understand the drivers of physical and mental well-being. Lyle’s research group collects MTurk crowdsourced labels on natural language data such Facebook posts and tweets, which they use for a variety of NLP and psychology studies. Lyle (with collaborators) has given highly successful tutorials on information extraction, sentiment analysis, and spectral methods for NLP at conferences including NAACL, KDD, SIGIR, ICWSM, CIKM, and AAAI. He and his student gave a tutorial on crowdsourcing last year at the Joint Statistical Meetings (JSM).

Ellie Pavlick is a Ph.D. student at the University of Pennsylvania. Ellie received her B.A. in economics from the Johns Hopkins University, where she began working with Dr. Chris Callison-Burch on using crowdsourcing to create low-cost training data for statistical machine translation by hiring non-professional translators and post-editors. Her current research interests include entailment and paraphrase recognition, for which she has looked at using MTurk to provide more difficult linguistic annotations such as discriminating between fine-grained lexical entailment relations and identifying missing lexical triggers in FrameNet. Ellie TAed and helped design the curriculum for the Crowdsourcing and Human Computation course at Penn.