# 11/30/24: 	LB4TL: A Smooth Semantics for Temporal Logic to Train Neural Feedback Controllers with Navid Hashemi

Page: https://stenobird.com/podcast/boston-computation-club-4031660/11-30-24-lb4tl-a-smooth-semantics-for-temporal-logic-to-train-neural-feedback-controllers-with-navid-hashemi
Text version: https://stenobird.com/podcast/boston-computation-club-4031660/11-30-24-lb4tl-a-smooth-semantics-for-temporal-logic-to-train-neural-feedback-controllers-with-navid-hashemi.md
Podcast: [Boston Computation Club](https://stenobird.com/podcast/boston-computation-club-4031660)
Published: 2024-12-01T01:35:49+00:00
Episode link: https://podcasters.spotify.com/pod/show/bostoncc/episodes/113024--LB4TL-A-Smooth-Semantics-for-Temporal-Logic-to-Train-Neural-Feedback-Controllers-with-Navid-Hashemi-e2rn7fs
Audio file: https://anchor.fm/s/5eee01ac/podcast/play/95181756/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2024-11-1%2F091424e3-5061-02d9-d38d-7a85b2e25b39.m4a
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Duration seconds: 2707

## Resource

Navid Hashemi recently defended his PhD at USC and is about to begin a post-doc at Vanderbilt. His research focuses on the intersection of Artificial Intelligence and Temporal Logics, with applications in Formal Verification of Learning Enabled Systems and Neurosymbolic Reinforcement Learning. Today Navid joined us for a really exciting presentation about his work on metrizable logics for reinforcement learning, and a technique for verification thereof based on the over-approximation of reachable sets using ReLU.

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