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  • ep46 - The fall of LTCM: Bachelier, Merton, and Black–Scholes ... when stochastic control met Wall Street

    ep46 - The fall of LTCM: Bachelier, Merton, and Black–Scholes ... when stochastic control met Wall Street
    2026/07/15
    Outline00:00 - Intro02:25 - Bachelier and the Théorie de la Spéculation03:05 - Stochastic processes, Brownian motion, and the heat equation09:45 - Poincaré's verdict, obscurity, and rediscovery13:50 - Robert C. Merton: from hot rods to MIT19:25 - Dynamic programming and Itô calculus24:35 - Merton's portfolio problem as stochastic optimal control31:10 - Options, dynamic hedging, and the Black–Scholes–Merton equation39:50 - LTCM: the dream team46:30 - August 1998: the crash49:00 - Fat tails and the ten-sigma defense51:40 - The ghosts of 2008 and echoes in the AI boom54:00 - Robustness embraced at last: Hansen and Sargent57:45 - OutroLinksBachelier's thesis, "Théorie de la Spéculation" (1900): https://www.numdam.org/item/10.24033/asens.476.pdfCourtault et al., "Louis Bachelier on the Centenary of Théorie de la Spéculation": https://doi.org/10.1111/1467-9965.00098Merton'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/10.1086/294632Merton, "Optimum Consumption and Portfolio Rules in a Continuous-Time Model": https://doi.org/10.1016/0022-0531(71)90038-XMoehle & Boyd, "A Certainty Equivalent Merton Problem": https://doi.org/10.1109/LCSYS.2021.3111534Brigo & Mercurio, "Interest Rate Models: Theory and Practice": https://doi.org/10.1007/978-3-540-34604-3Armstrong, Brigo & Hanzon, "Optimal Projection Filters with Information Geometry": https://doi.org/10.1007/s41884-023-00108-xHu & Zhou, "Constrained Stochastic LQ Control with Random Coefficients, and Application to Portfolio Selection": https://doi.org/10.1137/S0363012904441969Black & Scholes, "The Pricing of Options and Corporate Liabilities": https://doi.org/10.1086/260062Merton, "Theory of Rational Option Pricing": https://doi.org/10.2307/3003143Merton, "Option Pricing When Underlying Stock Returns Are Discontinuous": https://doi.org/10.1016/0304-405X(76)90022-2Scholes' 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/10.2307/2975974Michael Lewis, "Liar's Poker": https://en.wikipedia.org/wiki/Liar%27s_PokerEdwards, "Hedge Funds and the Collapse of Long-Term Capital Management": https://doi.org/10.1257/jep.13.2.189Lowenstein, "When Genius Failed": https://en.wikipedia.org/wiki/When_Genius_FailedTaleb, "Statistical Consequences of Fat Tails": https://arxiv.org/abs/2001.10488Taleb & West, "Working with Convex Responses: Antifragility from Finance to Oncology": https://doi.org/10.3390/e25020343Taleb, "The Black Swan": https://en.wikipedia.org/wiki/The_Black_Swan:_The_Impact_of_the_Highly_ImprobableTaleb, "Fooled by Randomness": https://en.wikipedia.org/wiki/Fooled_by_RandomnessMan Group, "The AI Bubble: Hidden Risks and Opportunities": https://www.man.com/insights/the-ai-bubbleSen. 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-crashMeng & Chen, "Artificial Intelligence and Systemic Risk": https://arxiv.org/abs/2604.03272Doyle, "Guaranteed Margins for LQG Regulators": https://doi.org/10.1109/TAC.1978.1101791Safonov & Athans, "Gain and Phase Margin for Multiloop LQG Regulators": https://doi.org/10.1109/TAC.1977.1101470Hansen & Sargent, "Robust Control and Model Uncertainty": https://doi.org/10.1257/aer.91.2.60Hansen & Sargent, "Wanting Robustness in Macroeconomics": http://www.tomsargent.com/research/wanting.pdfSupport the showPodcast infoPodcast website: https://www.incontrolpodcast.com/Apple Podcasts: https://tinyurl.com/5n84j85jSpotify: https://tinyurl.com/4rwztj3cRSS: https://tinyurl.com/yc2fcv4yYoutube: https://tinyurl.com/bdbvhsj6Facebook: https://tinyurl.com/3z24yr43Twitter: https://twitter.com/IncontrolPInstagram: https://tinyurl.com/35cu4kr4Acknowledgments and sponsorsThis 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.
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    1 時間 1 分
  • ep45 - Peter Caines: from stochastic and adaptive control to mean field games, graphons, and beyond!

    ep45 - Peter Caines: from stochastic and adaptive control to mean field games, graphons, and beyond!
    2026/06/15
    Outline 00:00 - Intro 02:10 - London in the 1960s12:40 - 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 - OutroLinks 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/4303244Bode 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=8IBZyRo0vDkSupport the showPodcast infoPodcast website: https://www.incontrolpodcast.com/Apple Podcasts: https://tinyurl.com/5n84j85jSpotify: https://tinyurl.com/4rwztj3cRSS: https://tinyurl.com/yc2fcv4yYoutube: https://tinyurl.com/bdbvhsj6Facebook: https://tinyurl.com/3z24yr43Twitter: https://twitter.com/IncontrolPInstagram: https://tinyurl.com/35cu4kr4Acknowledgments and sponsorsThis 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.
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    1 時間 32 分
  • ep44 - Mario di Bernardo: From Circuits to Cells and Swarms — Control meets Complexity

    ep44 - Mario di Bernardo: From Circuits to Cells and Swarms — Control meets Complexity
    2026/05/15

    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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    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.
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    1 時間 30 分
  • ep43 - Steve Brunton: DMD, Koopman, SINDy, Eigensteve Channel, HydroGym, Optimization, and much more

    ep43 - Steve Brunton: DMD, Koopman, SINDy, Eigensteve Channel, HydroGym, Optimization, and much more
    2026/04/15

    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
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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.
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    1 時間 14 分
  • ep42 - inControl guide to ... the Nyquist criterion

    ep42 - inControl guide to ... the Nyquist criterion
    2026/03/16

    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/
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    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.
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    1 時間 10 分
  • ep41 - A minimal history of optimal control

    ep41 - A minimal history of optimal control
    2026/02/16

    Outline
    00:00 - Intro
    02:55 - Brachistochrone problem
    20:52 - Beginning of the calculus of variations
    32:00 - Principle of least action
    42:37 - Maximum principle
    1:02:35 - Dynamic programming
    1:11:12 - Linear quadratic control
    1:16:37 - Beyond optimal control: games, nonsmooth analysis, MPC, RL
    1:28:40 - Outro

    Links
    300 years of optimal control: https://tinyurl.com/2s3t8se4
    Brachistochrone: https://tinyurl.com/mwmv38ew
    Acta Eruditorum, 1696: https://tinyurl.com/55yf5v49
    Acta Eruditorum, 1697: https://tinyurl.com/2a7msaaj
    Bernoulli family: https://tinyurl.com/y2vx2xdn
    Leibniz–Newton calculus controversy: https://tinyurl.com/3974fdhd
    Calculus of variations: https://tinyurl.com/3vvz8tuf
    Beginning of the Calculus of Variations: https://tinyurl.com/mv6btxfn
    Lagrangian mechanics: https://tinyurl.com/ycx5fv46
    Euler–Lagrange equation: https://tinyurl.com/53yybvyx
    Hamiltonian mechanics: https://tinyurl.com/yfrd8zhz
    Hamilton–Jacobi equation: https://tinyurl.com/46m9cuvs
    Pontryagin: https://tinyurl.com/35ehxnex
    Pontryagin’s autobiography: https://ega-math.narod.ru/LSP/book.htm
    Discovery of the Maximum Principle: https://tinyurl.com/3s43nv4t
    Maximum Principle: https://tinyurl.com/4f7352t4
    Goddard problem: https://tinyurl.com/5n8swp2m
    Hamilton–Jacobi–Bellman equation: https://tinyurl.com/4uemn5y4
    Kalman filter: https://tinyurl.com/39zx5yry
    Clarke: https://tinyurl.com/yj2tzcjb
    MPC: https://tinyurl.com/4sf5pzvy
    RL: https://tinyurl.com/ee5ne7sz
    AlphaGo: https://tinyurl.com/ydrf8jsc

    Support the show

    Podcast info
    Podcast website: https://www.incontrolpodcast.com/
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    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.
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    1 時間 32 分
  • ep40 - Jeff Shamma: gain scheduling, nonlinear control, learning & dissipativity in games, jiu-jitsu

    ep40 - Jeff Shamma: gain scheduling, nonlinear control, learning & dissipativity in games, jiu-jitsu
    2026/01/15

    Outline
    00:00 - Intro
    03:18 - Early days: why control, M. Athans, and IDSS
    12:21 - What is gain scheduling?
    33:37 - Paradigm shifts & the ‘90s: Minnesota → Texas → LA
    42:19 - Robustness & fundamental limitations of nonlinear systems
    57:35 - Set-valued control & estimation
    01:04:52 - Game theory & multi-agent control
    01:28:18 - Learning & dissipativity in games & multi agent AI
    01:45:03 - KAUST: building something new
    01:53:33 - On human-algorithmic interaction
    01:59:07 - Advice to future students: control, jiu-jitsu, and chatbots in education
    2:10:02 - Outro

    Links
    Jeff’s website: https://tinyurl.com/52btmmz7
    CSM interview: https://tinyurl.com/49wh98x7
    Domain: feedbackcontrol.com
    M. Athans: https://tinyurl.com/nhbw66wa
    PhD thesis: https://tinyurl.com/5eyxkfm6
    IDSS: https://tinyurl.com/bdenwy6d
    Research on gain scheduling: https://tinyurl.com/55se8zcr
    Overview of LPV systems: https://tinyurl.com/3ksff58b
    Åström’s lecture: https://tinyurl.com/33mxkkfe
    Necessity of the small gain theorem: https://tinyurl.com/mjn9eeb4
    Sensitivity reduction for nonlinear plants: https://tinyurl.com/23tej5yp
    Respect the unstable: https://tinyurl.com/3yww5eds
    Differential inclusion: https://tinyurl.com/4yvc8vcc
    Lectures on game theory: https://tinyurl.com/4z8hh3rn
    Dynamic fictitious play: https://tinyurl.com/yc6wsxjj
    Cooperative control and potential games: https://tinyurl.com/4hbmrt72
    Dissipativity theory in game theory: https://tinyurl.com/3theyc7x
    Population games, stable games, and passivity: https://tinyurl.com/zxwtzv6w
    Game theory and control: https://tinyurl.com/yencrwm3
    Higher-order uncoupled learning: https://tinyurl.com/376r5r9x

    Support the show

    Podcast info
    Podcast website: https://www.incontrolpodcast.com/
    Apple Podcasts: https://tinyurl.com/5n84j85j
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    RSS: https://tinyurl.com/yc2fcv4y
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    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.
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    2 時間 11 分
  • ep39 - Female influencers in control

    ep39 - Female influencers in control
    2025/12/16

    Outline

    00:00 - Intro

    05:01 - Female Influencers in Control — The Backstory

    08:08 - Sofya Kovalevskaya

    15:21 - Irmgard Lotz

    26:16 - A new wave of control influencers

    34:26 - Some data

    43:38 - What can one do?

    1:00:10 - Exhibition + survey

    1:05:21 - Outro


    Links
    Female influencers in control project: https://tinyurl.com/mv879ahf

    Charlotta Johnsson: https://tinyurl.com/343esbeu

    Eva Westin: https://tinyurl.com/3p6fd5n8

    Margret Bauer: https://tinyurl.com/47d35xzb

    Sofya Kovalevskaya: https://tinyurl.com/4mmzruwc

    Remembering Sofya Kovalevskaya: https://tinyurl.com/4cpw7vff

    Irmgard Lotz: https://tinyurl.com/y2exmndm

    Flow Computation Pioneer Irmgard Flügge-Lotz (1903–1974): https://tinyurl.com/4cy3xsp3

    Discontinuous Automatic Control: https://tinyurl.com/yeys5dxx

    Historical Female Influencers in Automatic Control: https://tinyurl.com/yxw4bjxe

    Activity report: https://tinyurl.com/jwzn4z3c


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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.
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