NOTE:
In light of recent events, and in accordance with the rules passed down by Dean Matsuda and the rest of the on-campus leadership, we are canceling the remaining ML Lunch Seminar meetings until Fall 2020. We wish all of you the best in this tumultuous time and encourage you to reach out if you need any help.
The Machine Learning Lunch Seminar is a student-run weekly talk series covering all areas of machine learning theory, methods, and applications. Each week, over 90 students and faculty from across Rice gather for a catered lunch, ML-related conversation, and a 45-minute research presentation. If you’re a member of the Rice community and interested in machine learning, please join us! Unless otherwise announced, the ML lunch occurs every Wednesday of the academic year at 12:00pm in Duncan Hall 3092 (the large room at the top of the stairs on the third floor).
The student coordinators are Cannon Lewis, Daniel LeJeune, Tianyi Yao, Lorenzo Luzi, Andersen Chang, Mitch Rodenberry, and Minjie Wang. Michael Weylandt is our spiritual guide and organizer emeritus. The ML Lunch Seminar is sponsored by the Rice Data to Knowledge Lab (D2K).
Announcements about the ML Lunch Seminars and other ML events on campus are sent to the ml-l@rice.edu mailing list. Click here to join.
Would you like to speak at the ML Lunch Seminar? Email us at RiceMLSeminar@gmail.com.
Upcoming Talk
March 4th, 12:00 pm – 1:00 pm in DCH 3092
Speaker: Suguman Bansal (COMP)
Please indicate interest, especially if you want lunch, here.
Abstract:
Intelligent machines, such as IoT devices, are fundamentally reactive systems that interact with the outer physical environment to achieve a goal.
Although prevalent, the design of a ‘provably correct’ intelligent machines would require to account for all the complex ways in which an environment and the machines may interact. This is challenging and error-prone. In this talk, I will introduce the paradigm of “Reactive synthesis” wherein the machine is automatically constructed from a high-level description of its goal. This way, an intelligent machine can be designed in a declarative fashion as opposed to an imperative fashion. This talk will cover recent theoretical and algorithmic advances in the area of reactive synthesis under the assumption of asynchrony and richer goal descriptions. It will also cover how you, an ML enthusiast/researcher, can contribute to this emerging field of research.