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Hi there, Iā€™m @LucaNyckees.

I am currently working on my Master's thesis (supervised by Prof. Kathryn Hess and Nicolas Berkouk) in the field of topological data analysis - we use topological optimization machine learning to tackle problems encountered in multi-persistent homology. I am interested in applications of topology to machine learning, data analysis and computational neurosciences. Along with my studies, I've been working on projects and internships within the Laboratory of Topology and Neuroscience at EPFL.

More generally speaking, I really enjoy addressing real-world problems through various types of data analysis - with an emphasis on both data visualization (e.g. interactive visualization with web applications) and on the adapted geometric framework(s) (what mathematical tools are best fit to the task?). I am interested in creating strong and task-specific coding pipelines that combine the fundamentals of machine learning and data visualization.

  • key-interests : statistical and topological data analysis, optimization machine learning, computational geometry
  • Python tools : pytorch, tensorflow, streamlit, networkx, pyvis, pandas, pyspark, numpy, plotly, matplotlib, NLTK

Luca Nyckees's Projects

graph-label-propagation icon graph-label-propagation

The goal of this project is to implement a graph label propagation method to study and classify image data. The method has the particularity that it combines well with other classifiers to achieve enhanced performance.

machine-learning-basics icon machine-learning-basics

In this project, we try the challenge of the Higgs Boson search by implementing from srcatch the standard fundamentals of statistical machine learning.

parametric-morse-theory icon parametric-morse-theory

Exploring he framework of parametric Morse theory, and providing a pipeline that computes the parametric persistence diagrams of a graph (of its clique complex).

topology-metric icon topology-metric

We implement the computation of linear integral sheaf metrics through topological optimization machine learning. We present applications to image analysis.

zigzag-homology icon zigzag-homology

Zigzag persistence studies the topological behavior of point-cloud data by generalizing the setting of persistent homology. We provide a way to compute by using extended persistence.

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