In this episode I got to chat with the incredibly wonderful Dr Keyan Miao about the fascinating work she’s done on neural ODEs. We discuss applying control theoretic tools to optimise neural ODEs, as well as their use for both controller and observer design. And in case that wasn’t already enough we also touch on dissipativity, Koopman operators, and reinforcement learning!

You can find the transcript for this episode at: https://docs.google.com/document/d/1iI0swQSnNEqgGQgf7og8udRV3IBCXbbfEkLFlolGxEU/edit?usp=sharing

Follow the podcast on LinkedIn: https://www.linkedin.com/company/coffee-and-control-podcast/

Or connect with me directly: https://www.linkedin.com/in/lucy-hodgins-733a30175/

Join the live recording on 20th October 2026!

https://www.youtube.com/watch?v=2S9X66_Wfzc

( 9:30 PDT, 12:30 ET, 16:30 UTC, 17:30 BST, 18:30 CEST)

References

Keyan Miao: https://kymiao.github.io/

Richard Vinter: https://profiles.imperial.ac.uk/r.vinter

 QianXiao Li from NUS and Wei-Nan Er from Princeton: https://www.jmlr.org/papers/volume18/17-653/17-653.pdf

https://link.springer.com/article/10.1007/s40687-018-0172-y

https://ems.press/journals/jems/articles/5404458

Book on control theory/ philosophy: https://apac.ee.kntu.ac.ir/publications/books/control-systems-a-historical-and-philosophical-perspective/

Control theory/ philosophy article: https://www.numberanalytics.com/blog/control-theory-philosophical-lens-language

NCCR automation fellowship: https://nccr-automation.ch/research-fellowship

Andreas Krause: https://las.inf.ethz.ch/krausea

Steve Brunton ResNet video: https://www.youtube.com/watch?v=w1UsKanMatM&t=911s

Original neural ODE paper: https://proceedings.neurips.cc/paper_files/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf

Speeding up Neural ODE training and inference: https://proceedings.mlr.press/v235/miao24a.html

Neural ODE review: https://www.sciencedirect.com/science/article/pii/S2950550X25000263

Review of Neural ODEs in biomedical engineering: https://www.annualreviews.org/content/journals/10.1146/annurev-bioeng-110824-124907

Neural ODEs for observer design paper: https://arxiv.org/abs/2212.00866

Non-linear observer review: https://www.sciencedirect.com/science/article/pii/S1367578821000948

Neural ODEs for control design papers: https://arxiv.org/abs/2504.17139

Survey on CBFs: https://coogan.ece.gatech.edu/papers/pdf/amesecc19.pdf

Koopman operator paper: https://arxiv.org/pdf/2509.07294

Combining neural ODEs with RL: https://www.sciencedirect.com/science/article/pii/S0925231225007131

Neural ODE dissipativity paper: https://www.sciencedirect.com/science/article/pii/S0005109826003043

Papers on Neural Network Verification: HuanZhang (UIUC)

https://proceedings.neurips.cc/paper_files/paper/2018/file/d04863f100d59b3eb688a11f95b0ae60-Paper.pdf

https://proceedings.neurips.cc/paper_files/paper/2021/file/fac7fead96dafceaf80c1daffeae82a4-Paper.pdf

https://arxiv.org/pdf/2605.26577

Papers on Direct NN parameterization: Ian Manchester (USyd)and Patricia Pauli (Eindhoven)

https://ieeexplore.ieee.org/abstract/document/9319198

https://arxiv.org/abs/2410.22258

https://proceedings.mlr.press/v202/wang23v.html

https://ieeexplore.ieee.org/abstract/document/9867842

Ricky Chen: https://rtqichen.github.io/

Flow matching paper: https://arxiv.org/abs/2210.02747

Antonis Papachristoudolou: https://eng.ox.ac.uk/people/antonis-papachristodoulou

Carmen Amo Alonso episode: https://open.spotify.com/episode/393QBgVgsQwgh4wKj1bcLy