Data-driven Engineering Science Seminars (DESS) is an interdisciplinary seminar series in the College of Engineering and Applied Science (CEAS) dedicated to the most recent advances in data-driven modeling, characterization and design applied to emerging problems in engineering science. The focus of the talks will be on deep learning solutions but we also welcome rigorous approaches to inverse problems as well as novel computational and experimental techniques. These include but are not limited to machine-learning-based data analytics, data-driven surrogate models, inverse design optimization, probabilistic methods, and novel sensing solutions. Applications will be diverse. The emphasis, however, will be on fundamental principles and rational frameworks that advance data-driven solutions for grand challenge problems. DESS is co-organized by six faculty members across CEAS:

• David Bortz (Applied Mathematics)
• Jed Brown (Computer Science)
• Alireza Doostan (Aerospace Engineering Science)
• Todd Murray (Mechanical Engineering)
• Rafael Piestun (Electrical, Computer & Energy Engineering)
• Fatemeh Pourahmadian (Civil, Environmental & Architectural Engineering)

If you would like to invite a speaker as part of DESS, please contact one of the organizers in a related department.

If you would like to receive DESS announcements, please self-subscribe to our mailing list via: https://lists.colorado.edu/sympa/info/dess

Stay tuned for our Fall 2022 schedule