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Jones Seminars on Science, Technology, and Society

Jones Seminars invites engineering, science, and technology leaders from across the nation to present and facilitate conversation on cutting edge research or scientific issues of our day. 

This endowed speaker series was launched in the 1970s through the generosity of Charles C. Jones '18 Th'19, who sought to facilitate conversation and learning for the curious-minded. All Dartmouth faculty, staff, students, alumni, and the general public are welcome to attend the seminars, typically held on Friday afternoons. PhD students are required to attend weekly, as well as meet before or after each event to discuss its topic in more detail.

If you would like to receive email reminders about the weekly Jones Seminars, please email jones.seminar@dartmouth.edu with your email address.

Upcoming Events

Sep

18

Special Seminar: Perspectives on AI Risk or "How I learned to stop worrying and love AI"

William Regli, Professor of Computer Science, U Maryland at College Park

Thursday
12:00pm - 1:15pm ET

Spanos Auditorium/Online

A summary of personal views developed during a decade away from academia in various forms of public service.

Sep

26

Jones Seminar: How Do Neural Networks Learn Features from Data?

Adit Radhakrishnan, Assistant Professor of Applied Mathematics, MIT

Friday
3:30pm - 4:30pm ET

Spanos Auditorium/Online

A unifying mechanism that characterizes feature learning across neural network architectures.

Oct

03

Jones Seminar: Quantum Computing with Sound

Andrew Cleland, Professor of Molecular Engineering, U Chicago

Friday
3:30pm - 4:30pm ET

Spanos Auditorium/Online

Advances pointing to the possibility of a phonon-based quantum computer, in which phonons allow inexpensive scaling to a large quantum computer.

Oct

10

Jones Seminar: Modern Developments in Error Correction

Ken Duffy, Professor of Electrical & Computer Engineering, Northeastern U.

Friday
3:30pm - 4:30pm ET

Spanos Auditorium/Online

An introduction to Guessing Random Additive Noise Decoding (GRAND), a class of error correction decoders suitable for any moderate redundancy code.

Past Events