Information Security seminars

Srini Devadas examines how hardware and cryptography can strengthen each other. Hardware can protect keys, monitor attacks in real time and reduce the trusted computing base, while hardware vulnerabilities can undermine software security. The talk presents research on accelerating cryptographic computation and using those capabilities to design more efficient protocols. It also asks how cryptography and new security definitions can guide the construction of secure hardware. Devadas is MIT's Webster Professor of Electrical Engineering and Computer Science. Hybrid: MIT Stata Center, Room 32-G449 (Kiva), 32 Vassar Street, Cambridge, MA 02139; online via Zoom. Thursday 24 September 2026, talk at 19:00 EDT (UTC−4); in-person refreshments begin at 18:30. This public event is organized by the Boston Chapter of the IEEE Computer Society and GBC/ACM. Register through the organizer-linked Zoom form for either mode and indicate online or in-person attendance. For physical access, use the Stata Center entrance nearest Main Street, then the fourth-floor elevators.

This set of short webinars will provide neuroscience researchers working in a neuroimaging setting with practical tips on strengthening credibility at different stages of the research project. Each webinar will be hosted by Cassandra Gould Van Praag from the Wellcome Centre for Integrative Neuroimaging.

The possibilities of machine learning and neural networks in particular are ever expanding. With increased opportunities to do good, however there are just as many opportunities to do harm and even in the case that good intentions are at the helm, evidence suggests that opportunities for good may eventually prove to be the opposite. The greatest threat to what machine learning is able to achieve and to us as humans, is machine learning that does not reflect the diversity of the users it is meant to serve. It is important that we are not so pre-occupied with advancing technology into the future that we have not taken the time to invest the energy into engineering the security measures this future requires. It is important to investigate now, as thoroughly as we investigate differing deep neural network architectures, the complex questions regarding the fact that humans and the society in which they operate is inherently biased and loaded with prejudice and that these traits find themselves in the machines we create (and increasingly allow to run our lives).