This is the first episode of the Flush to Data podcast. We start with a discussion on mechanistic modelling and machine learning and venture into models for emulation, uncertainty quantification, and data quality. Bonus material includes a discussion on aspects of current scientific practice, including the lack of hypothesis testing, the evaluation of novelty, and the challenges with a generalist approach.

Hosts: Jörg Rieckermann and Kris Villez
Guest: Juan Pablo Carbjal

Links:
* Juan Pablo's web page: https://sites.google.com/site/juanpicarbajal/
* Article relating Gaussian processes and Kalman filter: www.jstor.org/stable/2984861 
* BBC podcast on Gauss: https://www.bbc.co.uk/programmes/b09gbnfj
* Using Lake Zurich as a heat sink: Unfortunately, we could not back-track the original source, despite considerable effort. If anyone of the listeners happens to know how to access the original source we would be grateful for a notice. The best we could find was documentation of related projects by Eawag: https://thermdis.eawag.ch/ and [1]. These show that ecological consequences have indeed been assessed in detail. 
* Goodhart's law: https://en.wikipedia.org/wiki/Goodhart's_law
* An invitation to reproducible computational research: https://doi.org/10.1093/biostatistics/kxq028
* Science in the age of selfies: https://doi.org/10.1073/pnas.1609793113 

References:
[1] Wüest, A. (2012). Potential zur Wärmeenergienutzung aus dem Zürichsee. Machbarkeit. Wärmeentzug (Heizen) und Einleitung von Kühlwasser. Kastanienbaum: Eawag. DORA-Link 

Episode guide:
[0:00:00 ] Who is Juan Pablo Carbajal?
[0:03:10 ] Mechanistic modelling versus artificial intelligence
[0:07:08 ] Who is Juan Pablo Carbajal? (ctd.)
[0:09:26 ] Cross-fertilization between robotics and wastewater engineering
[0:15:05 ] Emulation: using models to approximate other models
[0:21:22 ] Incorporating common sense and prior knowledge into data-driven models
[0:31:31 ] Equivalence between Gaussian processes and Kalman filter
[0:33:50 ] Utility of emulation
[0:40:15 ] Utility of quantified uncertainty
[0:44:50 ] Intermezzo
[0:49:04 ] What can models say about data quality 
[1:02:15 ] How to communicate about data quality?
[1:10:10 ] Preparing engineers for the future
[1:15:23 ] Thank you and goodbye!

Bonus material:
[1:16:40 ] Interpretable machine learning models
[1:22:33 ] Hypothesis testing
[1:26:14 ] Critical assessment of novelty
[1:30:50 ] Barriers to the generalist approach 
[1:35:48 ] Thank you and goodbye!