Statistics for Data Science Essentials

Chris Callison-Burch is an associate professor of Computer and Information Science at the University of Pennsylvania, where his work has positioned him as a thought leader in natural language processing (NLP) and crowdsourcing. Before joining Penn, he was a research faculty member at the Center for Language and Speech Processing at Johns Hopkins University, contributing significantly to advancements in the field during his six-year tenure.Chris has held prominent roles in major conferences and organizations, including serving as the General Chair of the ACL 2017 conference, Program Co-Chair for EMNLP 2015, Chair of the Executive Board of NAACL (2011–2013), and Secretary-Treasurer for SIGDAT (2015–2017). His editorial contributions span leading journals such as Transactions of the ACL (TACL) and Computational Linguistics.With over 100 publications that have been cited more than 10,000 times, Chris's research has had a profound impact on computational linguistics. Recognized as a Sloan Research Fellow, he has received prestigious faculty research awards from Google, Microsoft, Amazon, and Facebook. His work has also been supported by DARPA and the National Science Foundation (NSF).Chris's research interests focus on the intersections of natural language processing, machine translation, and the innovative use of crowdsourcing to tackle complex computational challenges. His contributions continue to shape the future of AI-driven language technologies and their practical applications.

Hamed Hassani is an Associate Professor in the Department of Electrical and Systems Engineering, as well as in the Departments of Computer and Information Sciences and Statistics at the University of Pennsylvania. He earned his PhD in Computer and Communication Sciences from EPFL, Lausanne. Prior to his current role, he was a research fellow at the Simons Institute for the Theory of Computing at UC Berkeley and a post-doctoral researcher at ETH Zurich. His research interests encompass machine learning, coding theory, and information theory, with a focus on developing robust algorithms for data science applications.On Coursera, Hamed teaches the course Statistics for Data Science Essentials, which is designed to provide learners with foundational statistical concepts essential for data science. His expertise and innovative teaching methods aim to equip students with the necessary skills to analyze data effectively and make informed decisions based on statistical analysis. Hamed's contributions to the field have been recognized through various awards, including the IEEE Information Theory Society's Thomas M. Cover Dissertation Award and the NSF CAREER Award.