# Summary

Today on the show we will be talking about agent based modelling that is used to simulate macro economical systems, like the British labour market. For this I am talking to Maria del Rio Chanona currently doing her PhD at the Complexity Science Hub Vienna.

Maria will not only tell us about ways to use agent based modelling to simulate the effect of policy changes or external events like the pandamic on complex systems like the labour market, but during our deep dive, she will talk about her most recent work that focuses on using NLP to tab new sources of fine grain data on an individual level, like social media that can be used feed the next generation of agent based models.

# References

[https://www.csh.ac.at/researcher/maria-del_rio-chanona/](https://www.csh.ac.at/researcher/maria-del_rio-chanona/)

[https://mariadelriochanona.info/](https://mariadelriochanona.info/)

Forecasting the propagation of pandemic shocks with a dynamic input-output model [https://www.sciencedirect.com/science/article/pii/S0165188922002317](https://www.sciencedirect.com/science/article/pii/S0165188922002317)

Occupational mobility and automation: a data-driven network model [https://royalsocietypublishing.org/doi/full/10.1098/rsif.2020.0898](https://royalsocietypublishing.org/doi/full/10.1098/rsif.2020.0898)

Mental health concerns prelude the Great Resignation: Evidence from Social Media [https://arxiv.org/abs/2208.07926](https://arxiv.org/abs/2208.07926)

# Episode

Most AI practitioner's, including myself, think of long running algorithms as something that is caused by big data or poor implementation and that can be solved best by more compute, but today on the show we will be discussing hard problems and their runtime complexity with Kirill Simonov from the algorithm and complexity group at the technical university Vienna.

Kirill is talking about this research in algorithm complexity and gives us a taste of how to solve hard problems with for example, approximation algorithms, that exchanging the accuracy or correctness of results for lower runtimes, or parameterized complexity algorithms that reduce runtime by limiting the solution space.

# References

Kirill Simonov: https://www.ac.tuwien.ac.at/people/ksimonov/

Thesis: https://bora.uib.no/bora-xmlui/bitstream/handle/11250/2735169/archive.pdf?sequence=1&isAllowed=y

Lecture on Fixed-Parameter Algorithms: https://www.youtube.com/watch?v=4q-jmGrmxKs

I am sure most of you are listening or looking at some weather forecast during the day and more often than we like to see, we read news about climate change causing new temperature records or glaciers melting at accelerating rates. Today we are not going to talk about climate change or weather forecast directly, but its underlying principle, Hydrology (which is study of water movement and distribution in a physical system). We will talk about strategies to build Hydrological models and more concretely we are looking at the intersection of Machine Learning and Hydrology. For this I am talking to Moritz Feigl. Co-founder and Chief Data Scientist at Baseflow.ai

During his PhD, Moritz investigated how Hydrology can benefit from Machine Learning, and in the interview we are going to contrast and compare two main approaches in Hydrological modeling. On one side we look at process-based models that are build on a systematic understanding of the physical world and principles and on the other side, at data-driven models; like modern Deep Learning systems that are learning a input-output relationship based on observations alone.

Moritz explains different ways how to combine those traditionally opposing approaches to get the best of both worlds, increasing the accuracy of predictions and enhancing our understanding of the underlying physical systems.

References

https://www.linkedin.com/in/moritz-feigl/

https://www.linkedin.com/company/baseflow-ai-solutions/

https://baseflow.ai/

https://abstracts.boku.ac.at/search_abstract.php?paID=3&paSID=19947&paSF=&paLIST=0&language_id=DE

# Summary

Have you every had troubles paying your bills and got some nasty calls or letters about it? Debt collection is surely one area where I would not have thought to find AI, but today on the show, I am talking to Stephan Stricker Founder and CEO of Pair Finance and Maxime Kaniewicz, Data Science Team lead.

On how they combine the insights and methods from targeted marketing with reinforcement learning, to nudge customers towards paying their bills.

I think this episode is of great value to anyone who is thinking of building reinforcement learning systems for real business cases.

We speak about many of the main challenges in reinforcement learning, like how to collect intermediate rewards that match the business objects without running into the alignment problem. Or how to evaluate and compare different agents and policies without loosing revenue and cause damage to the business. We discuss the necessity of historical training data and the continuous flow of new training data in order to improve and optimize the system. We hear about ways to overcome the cold start problem by helping the agent to expand into new environments by providing new actions in combination with new priors and experiences.

I hope you will like this episode, and I can ensure you that there is a lot to learn.

# References

https://www.pairfinance.com/

https://www.linkedin.com/in/stephanstricker/- Stephan Stricker - Founder and CEO of Pair Finance

https://www.linkedin.com/in/maxime-kaniewicz/- Maxime Kaniewicz - Data Science Team Lead

When we talk about data on this podcast, its mostly about training data and its properties that are relevant for the training machine learning models. But today we look at the bigger picture and the use of data in future data economies. How should the future use of data on a bigger scale look like? How can we make sure to build trustworthy and ethical data economy that follow our European Values?

Today on the show I am talking to Nina Popanton from the Data Intelligence Initiative (DIO) about its role as an enable of data collaborations. We talk about the challenges and the opportunities they see for companies, academia and the public sector when sharing data for specific use cases.

We discuss the motivation for stockholders to come together and share their data, and under which circumstances they are willing to do so.

In addition we are taking a step back and have a look the greater picture and the socioeconomic responsibility of a data sharing economy, driven by the "European Strategy for Data" and European projects like Gaia-X.

I know this episode diverges from our usual focus on AI and its methods, but I hope it will be an inspiration to you. Let's get started …

# References

Nina Popanton - https://www.linkedin.com/in/nina-popanton-4b1541179/

DIO - https://www.dataintelligence.at/

Gaia-X - https://www.gaia-x.eu/

GreenData Hub - https://www.greendatahub.at/

Today on the show I am talking to Adrian Schiegl, the head of data science at XUND; an Austrian AI Startup that develops systems to predict medical diagnoses based on patients self reported symptoms.

During the interview Adrian is going to share some of the findings, challenges and solutions XUND has experienced and developed since its inception in 2018. For example, Xund's decision to move away from developing an mobile phone based self diagnosis system towards an Medical API that enables other vendors to integrate their automatic diagnoses system into their own products. In addition Adrian is telling us about recent research projects and future goals of the company, to move into hospitals and clinics in order to support the digitalization of a patients medical journey and ensure the most effective treatment possible.

#References

Adrian Schiegl : https://www.linkedin.com/in/adrian-schiegl/

Xund: https://xund.ai/

Bayesian Neural Networks : https://proceedings.neurips.cc/paper/2020/hash/322f62469c5e3c7dc3e58f5a4d1ea399-Abstract.html

This is the second part of my interview with Hamid Eghbal-zadeh, post-doc at the Johannes Kepler University at the Institute of Machine Learning.

In the interview, we are talking about his research on a series of different aspects of representation learning with deep neural networks in order to make them more robust and improve their out-of-distribution behavior.

In this second part, we are talking about disentangled representations and the benefit they bring to agents trained in contextualized reinforcement tasks, in order to operate in unseen contexts and environments.

References:

This is the first part of my interview with Hamid Eghbal-zadeh, post-doc at the Johannes Kepler University at the Institute of Machine Learning.

In the interview, we are talking about his research on a series of different aspects of representation learning with deep neural networks in order to make them more robust and improve their out-of-distribution behavior.

In this first part, we are talking about the origin of representation learning and data augmentation. Hamid explains his research on the effects of representation learning on model training and highlights some of the important caveats that data augmentation can have on the robustness of your models.

References:

# Show

Today on the show I have the pleasure to talk to Moritz Kampelmühler; Computer Vision and Machine Learning Research Engineer at Reactive Reality.

Reactive Reality is a spin-off from the Technical University of Graz that started out with developing a virtual dressing room that can be used by shoppers to create a virtual avatar of themselves and try out all kinds of different garments.

On the show, Moritz explains how they provide a rich shopper experience that scales making use of methods like photo-realistic rendering and state-of-the-art image processing in 2D and beyond.

In the second part of the Interview, we discuss the current research efforts at Reactive Reality that focus on 3D and how to create 3D reconstructions of people and garments from only a few or even a single picture.

# References

* Pictofit Shopping App: https://apps.apple.com/at/app/pictofit-shopping/id1527419372

* Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., & Li, H. (2019). Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2304-2314).

# Intro

Today on the show I have the pleasure to talk to Shiva Ghahremani, Co-founder and CEO of Asigmo Data Science.

Asigmo is a teaching company that has developed a data science Bootcamp to jump-start your career in Data Science.

In the interview, we talk about the structure of the Bootcamp and the content of the syllabus. We further discuss the role of boot camps in the wide area of online education and how at Asigmo they strive to find the perfect balance between the time students invest in the course and the skills they learn in return.

I think this episode is most interesting for people that are curious to find effective ways to pivot their career towards Data Science and alike. People with a background in software development that want to specialize or people in academia that are searching for a smooth transition into industry.

More than anything else, I hope you find this episode interesting and entertaining. 

# References

https://www.linkedin.com/in/shiva-ghahremani/

https://www.asigmo.com/