Big Data and Edge - research, MegaSense project, Carat-project maintenance
I am a postdoctoral researcher at the Discovery Reseach Group, in the Department of Computer Science. I am interested in everything related to Natural Language Processing and more specifically in automatic analysis of large news collections.
Postdoctoral Researcher in Spatiotemporal Data Analysis research group in the department of Computer Science in University of Helsinki.
Doctoral Student in Computer Science. Researching Natural Language Generation for factual settings such as news and reports. History in research of programming education.
Research focuses on (1) computational creativity, i.e. the practice of studying creativity using computational means, (2) autonomous agents, e.g. self-awareness and self-adaptivity in creative systems, and (3) cooperation mechanisms for (creative) agents. Also interested in nearly anything.
My goal is to use theoretical and empirical insights to develop a principled understanding of machine learning (ML) models so that they are reliable when deployed in the real-world. More concretely, I am thinking about the following problems:
Graph embeddings distill high-dimensional feature and neighbourhood information onto low-dimensional spaces so that they can be used to perform ML tasks such as node classification and link prediction. I seek to formally explore the relationship between robustness and traditional notions of performance (e.g. accuracy), especially in the presence of random as well as structural perturbations to the input graph, and uncertainty in the form of noisy or unavailable data.
When we use graph embeddings for downstream tasks, it is imperative to ensure that they are systemically sound and safe to use. I am working on building models that are provably guaranteed to be robust and fair by design.