CAIML #37 is going to happen on July 8, 2025, at TH Köln. Thanks to TH Köln, Prof. Gernot Heisenberg and KölnBusiness for their support!
We will have two talks with additional time for networking.
Talk 1: Natasha Randall (Scientific Research Assistant at TH Köln) - Generating Photorealistic Flood Predictions with Generative Adversarial Networks
The forecasting of floods is traditionally carried out using extensive, expensive numerical simulations, but the advent of AI offers new frontiers to flood forecasting, such as creating synthetic photorealistic images of floods. In this talk, we explore how effectively Generative Adversarial Networks (GANs) can be used to generate flood imagery that is not only realistic, but also provides accurate predictions of the floodwaters resulting from real-world natural disasters. We also describe methods for evaluating photorealistic images, and then use these to analyse the performance of different GAN architectures and data inputs, investigating the wheres, hows, and whys of their predictions. We thus explore AI model behaviour, taking a deeper look at what deep learning models can (and can't!) learn about physical processes, even when they have not been explicitly encoded into the model. Overall, this talk highlights just some of the many benefits of bringing AI into the dynamic fields of hydrology and geosciences.
Talk 2: Mohamed Amine Jebari (Lead Data Scientist at TD Reply) - Causal convergence: the iterative blending of Algorithms and expertise
Working in the marketing analytics field often feels like an endless pursuit of the truth. While humans intuitively converge on causal relationships—linking an illness to a sick colleague, or assuming the sunrise causes the rooster to crow—establishing causality in statistics and econometrics is far more complex.
To increase our confidence in causal claims, we apply a wide range of methods: randomized control trials, quasi-experimental designs, simple regressions, complex Bayesian structural models, etc.
These techniques, frameworks, and tools are undeniably valuable. Yet, causality ultimately rests on human judgment. We draw on accumulated domain knowledge—built over years of experience—that enables us to form hypotheses, interpret findings, and correct course when needed.
This is where experts become equal partners in the causal inference process. Domain knowledge is essential to designing interventions, defining counterfactuals, and analyzing model outputs. It helps us answer questions like:
- What exactly do we mean by “impact”?
- What mechanisms are plausible?
- Are we measuring what truly matters?
Through the iterative process between data science and human insight—testing, refining, challenging assumptions—we begin to converge towards the truth, closer to understanding the cause of phenomenons.
Nevertheless, causal convergence is about the journey — a continuous back-and-forth between action and reaction, data and judgment, models and experience.
We will share more details on the talks soon. See you in July 🤖
