THE USM DATA SCIENCE ENSEMBLE
The Dubyak Center for Digital Science and Innovation is proud to present The USM Data Science Ensemble: a seminar series focused on AI and Data Science. This monthly series will feature talks by our invited, keynote speakers Reza Zadeh, Luis Carvalho, Ryan Taylor, Robert Swain, Allan Hanbury, Vikas Singh, Ahmad P. Tafti, Mehdi Maadooliat, and Hilal Maradit Kremers. All of our speakers are highly distinguished members of their respective fields, representing cutting-edge research interests. Each talk will provide a professional forum for data scientists, artificial intelligence (AI) practitioners, and applied machine learning researchers to present their latest research findings and innovations.
Each seminar will consist of a combination of talks, panel discussions, and paper/poster presentations. We reserve significant time for open discussions on sharing best practices and future directions in the field.
Our focus is on exploring the intersection of data science and real-world applications. We hope this series will bring together scientists and interested audiences to explore the open problems, applications, and future directions in AI and data science. Click the links below to learn more about our past events as well as our upcoming events, and how to attend them via Zoom.
Dubyak Center for Digital Science and Innovation Presents- Deep Learning for Medical Image Analysis
Join us on Mayl 5th, 4 - 5pm EDT, for Dr. Cigdem Gunduz Demirs' presentation on Deep Learning for Medical Image Analysis.
Dubyak Center for Digital Science and Innovation Presents: Natural Language Processing for Clinical Excellence- The State of Practices, Opportunities, and Challenges
Join us on April 7th, 4 - 5pm EDT, for Yanshan Wangs' presentation on the Natural Language Processing for Clinical Excellence: The State of Practices, Opportunities, and Challenges.
Effective and Efficient Neural Re-Ranking in Information Retrieval
Join us on May 6th, 4 - 5pm EDT, for Allan Hanbury's presentation on new approaches to overcoming disadvantages related to neural re-ranking in information retrieval.