In this episode, our guest is Stephen Boyd. Stephen is the Samsung Professor in the School of Engineering at Stanford University.  Join as we dive deep into control, convex optimization, linear matrix inequalities, disciplined convex programming, teaching styles, and... rock & roll sound!

Outline
- 00:00 - Intro
- 07:48 - Early years at Berkeley 
- 10:25 - The role of theory in practice
- 16:19 - On traveling (intellectually)
- 19:40 - Convex optimization 
- 31:51 - On Linear Matrix Inequalities (LMIs)
- 39:57 - Convex Optimization Control Policies  (COCPs)
- 50:20 - CVX and Disciplined Convex Programming (DCP)
- 58:14 - About AI
- 1:03:58 - Teaching
- 1:11:07 - Open source and publishing
- 1:15:13 - Future of control and advice to future students
- 1:20:08 - Outro

Episode links
- Stephen’s website: https://tinyurl.com/yrmk6p2w
- CSM acceptance speech: https://tinyurl.com/43yhs583
- L. Chua: https://tinyurl.com/k4zx4vya
- C. Desoer: https://tinyurl.com/4euxvcxx
- S. Sastry: https://tinyurl.com/2p9hfrha
- G. Dantzig: https://tinyurl.com/2s4m3jvz
- Simplex algorithm: https://tinyurl.com/2r8bxwe5
- Interior point methods: https://tinyurl.com/4ev4z6zm
- Invariants and dissipated quantities: https://tinyurl.com/43zswmwt
- Linear matrix inequalities: https://tinyurl.com/4y57date
- COCP paper: https://tinyurl.com/468apvdx
- Keynote talk at L4DC: https://tinyurl.com/2y3z4v68
- Model Predictive Control (MPC): https://tinyurl.com/bdf8r2sx
- DCP: https://tinyurl.com/yc38kvae 
- YALMIP: https://tinyurl.com/mr3rk2r4
- Stephen's books: https://tinyurl.com/52v9fu83

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Podcast info
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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.