Machine Learning

Supervisors: Faizan Ahmed(UT), Pieter Zeilstra (Saxion University of Applied Sciences)

Be part of an international research group working on the Horizon 2020 SUSTENANCE project, where the goal is to help local communities use renewable energy like solar and wind more efficiently and become less dependent on the main power grid.

Your assignment will be about extending an existing platform called IECON. This platform runs on small computers like Raspberry Pi and collects real-time energy data from households. Right now, this data stays mostly within the home. Your challenge is to make it possible for households to share their data securely and with full control, so they can decide who sees their data and how it is used.

To do this, you will explore the concept of data spaces, which are secure digital environments where data can be shared based on clear rules and privacy protections. You will research existing tools such as IDS connectors, talk to stakeholders to understand what they need, and then design, build, and test your own solution that fits on edge devices like Raspberry Pi.

If you are interested in sustainability, data privacy, and working with real-world technologies that have a global impact, this project offers a chance to make a real difference while building valuable skills for your future.

Supervisors: Faizan Ahmed

Black-box machine learning models are frowned upon by regulators. Therefore, it is essential to develop methods and tools to peek inside these black boxes. Explainable AI (XAI) is a set of methods and tools for explaining or reasoning about the outcomes or internal workings of these black-box models.

Explainable AI has many different facets, which lead to several interesting research directions. This topic is therefore not narrowed down to one specific research question; instead, it is presented as a set of research directions that I find interesting. A few concrete directions are as follows:

1. Explainable AI for time series or sequence models: The focus could be on developing new XAI techniques or adapting existing ones to the time series data modality. A very concrete project in this direction is a comparative study of the TCAV and C-SHAP frameworks.
2. Explainable AI for point clouds: Although point cloud analysis is formally a computer vision task, the data modality requires specialist tools and techniques, since its data structure differs substantially from that of images.
3. Explanation multiplicity: If two models differ only in their hyperparameters, what can be said about the relationship between the explanations they produce? This is a new and emerging area within explainable AI.
4. Explanation by or for LLMs: LLMs offer a way to present explanations in a human-understandable form. On the other hand, LLMs are themselves black-box models that require explanation. In this regard, mechanistic interpretability is an interesting direction to explore.
5. Applied explainable AI: Applying existing explainability techniques in domains such as healthcare and finance comes with its own challenges, so this could be a standalone research topic in itself.

If you find any of the above directions interesting, or have a project of your own in explainable AI, feel free to contact me.