Outline

00:00 - Intro

00:42 - “Research should be fun”

02:02 - Early steps in research

09:00 - Book writing and meeting C. Desoer

18:33 - Control synthesis via the factorization approach

25:46 - The graph metric

29:27 - Robotics and CAIR

36:00 - Randomized algorithms

40:41 - On learning

44:05 - Neural networks

48:40 - Tata, hidden Markov models, and large deviations theory

55:48 - Picking problems and role of luck

58:07 - Compressed sensing and non-convex optimization

01:02:17 - Interplay between control and machine learning

01:09:10 - Advice to future students

01:13:29 - Future of control

Links

Sagar’s website: https://tinyurl.com/4hwruajs

Control Systems Synthesis — A Factorization Approach: https://tinyurl.com/59wdc4sv

A Brief History of the Graph Topology: https://tinyurl.com/49uftzdk

Randomized algorithms for robust controller synthesis using statistical learning theory: https://tinyurl.com/wanpyeuc

Learning and Generalisation: https://tinyurl.com/2s3mzh8h

Are Analog Neural Networks Better Than Binary Neural Networks? https://tinyurl.com/3fnk27xc

An Introduction to Compressed Sensing: https://tinyurl.com/fc6a8eer

Podcast info
Podcast website: https://www.incontrolpodcast.com/
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Acknowledgments and sponsors
This episode was supported by the National Centre of Competence in Research on «Dependable, ubiquitous automation» and the IFAC Activity fund. The podcast benefits from the help of an incredibly talented and passionate team. Special thanks to L. Seward, E. Cahard, F. Banis, F. Dörfler, J. Lygeros, ETH studio and mirrorlake . Music was composed by A New Element.

ep33 - Mathukumalli Vidyasagar: control synthesis, robotics, randomized algorithms, learning, compressed sensing, non-convex optimization