inControl

Outline
00:00 - Intro
02:25 - Bachelier and the Théorie de la Spéculation
03:05 - Stochastic processes, Brownian motion, and the heat equation
09:45 - Poincaré's verdict, obscurity, and rediscovery
13:50 - Robert C. Merton: from hot rods to MIT
19:25 - Dynamic programming and Itô calculus
24:35 - Merton's portfolio problem as stochastic optimal control
31:10 - Options, dynamic hedging, and the Black–Scholes–Merton equation
39:50 - LTCM: the dream team
46:30 - August 1998: the crash
49:00 - Fat tails and the ten-sigma defense
51:40 - The ghosts of 2008 and echoes in the AI boom
54:00 - Robustness embraced at last: Hansen and Sargent
57:45 - Outro

Links
Bachelier's thesis, "Théorie de la Spéculation" (1900): https://www.numdam.org/item/10.24033/asens.476.pdf
Courtault et al., "Louis Bachelier on the Centenary of Théorie de la Spéculation": https://doi.org/10.1111/1467-9965.00098
Merton's Nobel autobiography: https://www.nobelprize.org/prizes/economic-sciences/1997/merton/biographical/
Merton's MIT "Infinite History" interview: https://infinite.mit.edu/video/robert-c-merton-phd-%E2%80%9970/
Mandelbrot, "The Variation of Certain Speculative Prices": https://doi.org/10.1086/294632
Merton, "Optimum Consumption and Portfolio Rules in a Continuous-Time Model": https://doi.org/10.1016/0022-0531(71)90038-X
Moehle & Boyd, "A Certainty Equivalent Merton Problem": https://doi.org/10.1109/LCSYS.2021.3111534
Brigo & Mercurio, "Interest Rate Models: Theory and Practice": https://doi.org/10.1007/978-3-540-34604-3
Armstrong, Brigo & Hanzon, "Optimal Projection Filters with Information Geometry": https://doi.org/10.1007/s41884-023-00108-x
Hu & Zhou, "Constrained Stochastic LQ Control with Random Coefficients, and Application to Portfolio Selection": https://doi.org/10.1137/S0363012904441969
Black & Scholes, "The Pricing of Options and Corporate Liabilities": https://doi.org/10.1086/260062
Merton, "Theory of Rational Option Pricing": https://doi.org/10.2307/3003143
Merton, "Option Pricing When Underlying Stock Returns Are Discontinuous": https://doi.org/10.1016/0304-405X(76)90022-2
Scholes' Nobel lecture: https://www.nobelprize.org/prizes/economic-sciences/1997/scholes/lecture/
Merton's Nobel lecture: https://www.nobelprize.org/prizes/economic-sciences/1997/merton/lecture/
Markowitz, "Portfolio Selection": https://doi.org/10.2307/2975974
Michael Lewis, "Liar's Poker": https://en.wikipedia.org/wiki/Liar%27s_Poker
Edwards, "Hedge Funds and the Collapse of Long-Term Capital Management": https://doi.org/10.1257/jep.13.2.189
Lowenstein, "When Genius Failed": https://en.wikipedia.org/wiki/When_Genius_Failed
Taleb, "Statistical Consequences of Fat Tails": https://arxiv.org/abs/2001.10488
Taleb & West, "Working with Convex Responses: Antifragility from Finance to Oncology": https://doi.org/10.3390/e25020343
Taleb, "The Black Swan": https://en.wikipedia.org/wiki/The_Black_Swan:_The_Impact_of_the_Highly_Improbable
Taleb, "Fooled by Randomness": https://en.wikipedia.org/wiki/Fooled_by_Randomness
Man Group, "The AI Bubble: Hidden Risks and Opportunities": https://www.man.com/insights/the-ai-bubble
Sen. Warren's remarks at the Vanderbilt Policy Accelerator: https://www.banking.senate.gov/newsroom/minority/warren-remarks-at-vanderbilt-policy-accelerator-event-highlighting-economic-and-financial-risks-of-potential-ai-crash
Meng & Chen, "Artificial Intelligence and Systemic Risk": https://arxiv.org/abs/2604.03272
Doyle, "Guaranteed Margins for LQG Regulators": https://doi.org/10.1109/TAC.1978.1101791
Safonov & Athans, "Gain and Phase Margin for Multiloop LQG Regulators": https://doi.org/10.1109/TAC.1977.1101470
Hansen & Sargent, "Robust Control and Model Uncertainty": https://doi.org/10.1257/aer.91.2.60
Hansen & Sargent, "Wanting Robustness in Macroeconomics": http://www.tomsargent.com/research/wanting.pdf
Support the show
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.

Outline
00:00 - Intro
02:10 - London in the 1960s
12:40 - From Oxford to Imperial College: David Mayne and the discrete-time Riccati equation
18:05 - The "global tour": Montenegro roads, hitch-hiking to Istanbul, and the San Francisco waterfront
22:30 - Feedback and causality between stochastic processes
31:15 - The system identification years
40:50 - Model complexity, the bias–variance trade-off, and concentration inequalities
52:05 - Adaptive control: living through a golden era
1:00:30 - McGill, George Zames, and CIFAR's "institute without walls," and COCOLOG
1:09:45 - Mean field games: the China connection, the cell-phone problem, and Nash Certainty Equivalence
1:20:15 - The Lasry–Lions simultaneous discovery
1:24:40 - From graphons to graphexons: sparse networks, Laplexions, and geometry
1:31:00 - Linear Stochastic Systems, Popper, and falsifiability
1:35:20 - Advice to young researchers
1:38:00 - Outro
Links
Peter Caines' website: https://www.mcgill.ca/cim/caines
Linear Stochastic Systems: https://epubs.siam.org/doi/book/10.1137/1.9781611974713
  On the discrete-time matrix Riccati equation of optimal control: https://doi.org/10.1080/00207177008931892
Feedback between stationary stochastic processes: https://doi.org/10.1109/TAC.1975.1101008
Prediction-error identification methods for stationary stochastic processes: https://doi.org/10.1109/TAC.1976.1101304
Asymptotic normality of prediction-error estimators for approximate system models: https://doi.org/10.1109/CDC.1978.268066
Discrete-time multivariable adaptive control (Axelby Award): https://doi.org/10.1109/TAC.1980.1102363
Discrete-time stochastic adaptive control: https://doi.org/10.1137/0319052
25 seminal control papers of the 20th century: https://books.google.ca/books/about/Control_Theory.html?id=eVhGAAAAYAAJ
COCOLOG: A conditional observer and controller logic for finite machines: https://epubs.siam.org/doi/10.1137/S0363012992226636
Hierarchical hybrid control systems: https://doi.org/10.1109/9.664153
On the hybrid optimal control problem: https://ieeexplore.ieee.org/document/4303244
Bode Lecture: https://ieeecss.org/presentation/bode-lecture/mean-field-stochastic-control
The cell-phone problem - Large population stochastic wireless power control: https://doi.org/10.1109/CDC.2003.1272542
Large-population stochastic dynamic games - McKean-Vlasov and the Nash Certainty Equivalence principle: https://projecteuclid.org/journals/communications-in-information-and-systems/volume-6/issue-3/Large-population-stochastic-dynamic-games--closed-loop-McKean-Vlasov/cis/1183728987.full
Large-population cost-coupled LQG with nonuniform agents and decentralized ε-Nash equilibria: https://doi.org/10.1109/TAC.2007.904450
Social optima in mean field LQG control: https://doi.org/10.1109/TAC.2012.2183439
ε-Nash mean field games with major and minor agents: https://arxiv.org/abs/1209.5684
Graphon mean field games and their equations: https://doi.org/10.1137/20M136373X
Mean field games on large sparse network limits - Laplexion dynamics on graphexons: https://www.sciencedirect.com/science/article/pii/S240589632500388X
Murray Wonham oral history: https://www.youtube.com/watch?v=8IBZyRo0vDk
Support the show
Podcast info
Podcast website: https://www.incontrolpodcast.com/
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Youtube: https://tinyurl.com/bdbvhsj6
Facebook: https://tinyurl.com/3z24yr43
Twitter: https://twitter.com/IncontrolP
Instagram: https://tinyurl.com/35cu4kr4

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.

Outline
00:00 - Intro
01:30 - Origin story: Naples, electrical engineering, and the fascination with chaos
08:00 - What is chaos?
15:00 - DC-DC converters and discontinuity-induced bifurcations
22:00 - Piecewise-smooth dynamical systems
26:55 - Complex networks, synchronization, and pinning control
40:30- Synthetic biology: from gene regulatory networks to multicellular control
58:00 - COVID-19: a network epidemic model for Italy
1:02:00 - Multiscale control, statistical mechanics, and physics-informed control
1:19:10 - State of the field and the IEEE CSS
1:26:35 - Advice to young researchers
1:29:00 - Outro

Links
Mario's website: https://sites.google.com/site/dibernardogroup/home
Scuola Superiore Meridionale: https://www.ssm.unina.it/
Chaos by James Gleick: https://en.wikipedia.org/wiki/Chaos:_Making_a_New_Science
Control of chaos:https://en.wikipedia.org/wiki/Control_of_chaos
Erasmus programme: https://en.wikipedia.org/wiki/Erasmus_Programme
An Adaptive Approach to the Control and Synchronization of Continuous-time Chaotic Systems: https://doi.org/10.1142/S0218127496000254
Piecewise-smooth Dynamical Systems: Theory and Applications: https://doi.org/10.1007/978-1-84628-708-4 
Bifurcations in nonsmooth dynamical systems: https://doi.org/10.1137/050625060 Controllability of complex networks via pinning:
https://doi.org/10.1103/PhysRevE.75.046103 
Criteria for global pinning-controllability of complex networks: https://doi.org/10.1016/j.automatica.2008.07.007
Controllability of complex networks: https://doi.org/10.1038/nature10011
Controlling complex networks with complex nodes: https://doi.org/10.1038/s42254-023-00566-3
Analysis, design and implementation of a novel scheme for in-vivo control of synthetic gene regulatory networks: https://doi.org/10.1016/j.automatica.2011.01.073
In-vivo Real-time Control of Protein Expression from Endogenous and Synthetic Gene Networks: https://doi.org/10.1371/journal.pcbi.1003625
A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic: https://doi.org/10.1038/s41467-020-18827-5
A Continuification-Based Control Solution for Large-Scale Shepherding: 
https://arxiv.org/abs/2411.04791
Shepherding control and herdability in complex multiagent systems: https://doi.org/10.1103/PhysRevResearch.6.L032012
Nonreciprocal field theory for decision-making in multi-agent control systems: https://doi.org/10.1038/s41467-025-63071-4

Support the show
Podcast info
Podcast website: https://www.incontrolpodcast.com/
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Youtube: https://tinyurl.com/bdbvhsj6
Facebook: https://tinyurl.com/3z24yr43
Twitter: https://twitter.com/IncontrolP
Instagram: https://tinyurl.com/35cu4kr4

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.

Outline
00:00 - Intro
01:15 - Origin story: early path and the road to science 
04:20 - On graphical visualization and aphantasia 
08:08 - The interest in fluid dynamics 
12:00 - Caltech, Jerry Marsden, and the move to the Pacific time zone 
19:43 - Dynamic Mode Decomposition (DMD) and the Koopman operator 
27:15 - On teaching and the Eigensteve channel 
39:22 - SINDy: Sparse Identification of Nonlinear Dynamics 
45:45 - Automatic knowledge creation and Explainable AI 
54:31 - HydroGym: RL benchmarks for fluid flow control 
1:01:37 - Optimization boot camp 
1:05:31 - Collimator 
1:13:18 - Outro
Links
Steve's website: https://www.eigensteve.com/
Eigensteve channel: https://www.youtube.com/c/eigensteve
Jerrold E. Marsden: https://en.wikipedia.org/wiki/Jerrold_E._Marsden
Aphantasia: https://en.wikipedia.org/wiki/Aphantasia
J. Nathan Kutz: https://amath.washington.edu/people/j-nathan-kutz
Clarence W. Rowley: https://cwrowley.princeton.edu/
DMD: https://en.wikipedia.org/wiki/Dynamic_mode_decomposition
Koopman operator: https://en.wikipedia.org/wiki/Koopman_operator
Dynamic Mode Decomposition book: https://epubs.siam.org/doi/book/10.1137/1.9781611974508
On Dynamic Mode Decomposition paper: https://doi.org/10.3934/jcd.2014.1.391
DMD with control: https://arxiv.org/abs/1409.6358
Compressed sensing and DMD: https://doi.org/10.3934/jcd.2015002
Modern Koopman Theory for Dynamical Systems: https://arxiv.org/abs/2102.12086
Deep learning for universal linear embeddings of nonlinear dynamics: https://doi.org/10.1038/s41467-018-07210-0
Data-driven discovery of Koopman eigenfunctions for control: https://doi.org/10.1088/2632-2153/abf0f5
PyDMD: https://github.com/PyDMD
Discovering governing equations from data by sparse identification of nonlinear dynamical systems: https://doi.org/10.1073/pnas.1517384113
Data-driven discovery of partial differential equations:
https://doi.org/10.1126/sciadv.1602614
SINDy for model predictive control in the low-data limit:
https://doi.org/10.1098/rspa.2018.0335
PySINDy: https://github.com/dynamicslab/pysindy
SINDy with control: https://arxiv.org/abs/2108.13404
SINDy review: https://doi.org/10.1146/annurev-control-030123-015238
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control: http://www.databookuw.com
Explainable AI: Learning from the Learners: https://arxiv.org/abs/2601.05525
HydroGym: https://github.com/dynamicslab/hydrogym
Support the show
Podcast info
Podcast website: https://www.incontrolpodcast.com/
Apple Podcasts: https://tinyurl.com/5n84j85j
Spotify: https://tinyurl.com/4rwztj3c
RSS: https://tinyurl.com/yc2fcv4y
Youtube: https://tinyurl.com/bdbvhsj6
Facebook: https://tinyurl.com/3z24yr43
Twitter: https://twitter.com/IncontrolP
Instagram: https://tinyurl.com/35cu4kr4

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.

Outline
00:00 – Intro
04:43 – Life and background
08:45 – Bell Labs
13:42 – Inventing the negative feedback amplifier
18:15 – Nyquist's landmark contributions
20:43 – Regeneration theory
27:10 – Frequency response
32:03 – Cauchy’s argument principle
36:05 – The Nyquist criterion
41:37 – Why is it so hard?
45:27 – Robustness, margins, and practical aspects
56:41 – Beyond the Nyquist criterion
1:04:25 – Pitfalls and common misunderstandings
1:07:00 – Outro
Links
Brian Douglas's video: http://y2u.be/sof3meN96MA
The Idea Factory: https://en.wikipedia.org/wiki/The_Idea_Factory
Inventing the Negative Feedback Amplifier: https://doi.org/10.1109/MSPEC.1977.6501721
Johnson–Nyquist noise:  https://doi.org/10.1103/PhysRev.32.110
Nyquist sampling theorem: https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem
Regeneration theory: https://doi.org/10.1002/j.1538-7305.1932.tb02344.x
Gain and phase margins: https://en.wikipedia.org/wiki/Bode_plot#Gain_margin_and_phase_margin
Routh–Hurwitz criterion: https://en.wikipedia.org/wiki/Routh%E2%80%93Hurwitz_stability_criterion
Åström’s lecture: https://archive.control.lth.se/media/Staff/KarlJohanAstrom/Lectures/ASMENyquistLecture2005.pdf
Scale-Relative Graphs: https://doi.org/10.1109/TAC.2023.3234016
Support the show
Podcast info
Podcast website: https://www.incontrolpodcast.com/
Apple Podcasts: https://tinyurl.com/5n84j85j
Spotify: https://tinyurl.com/4rwztj3c
RSS: https://tinyurl.com/yc2fcv4y
Youtube: https://tinyurl.com/bdbvhsj6
Facebook: https://tinyurl.com/3z24yr43
Twitter: https://twitter.com/IncontrolP
Instagram: https://tinyurl.com/35cu4kr4

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.