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    nglm/README.md

    Hi! I'm Natacha Galmiche, a researcher in Machine Learning!

    About me

    I am currently a machine learning researcher at the University of Bergen (Norway) in Michoel's group. Before that, I was a PhD candidate in the machine learning group of the University of Bergen. I studied mathematics and computer science in Toulouse (France), with 2 complementary master's programs in High-Performance Computing, Big Data and Machine Learning. My preferred programming language is python, but I also did some development for example in javascript for a web-based application.

    On my GitHub profile, you can see most of my research and teaching projects.

    Research

    As a researcher, I develop machine learning methods for biogenetics and my current project's objective is to identify new treatment targets (proteins) in neuropsychiatric disorders using causal machine learning. My main source of data is UK Biobank and UK Biobank Pharma Proteomics Project (UKB-PPP). In this context, I have developed a small package for efficient, robust and practical downloading of UKB-PPP data (UKB PPP Download).

    My main PhD project aims at clustering time-series data in the context of ensemble weather prediction. From there, I became more and more interested in research questions related to clustering in general, notably taking into account the uncertainty on the number of clusters (PersiGraph and GraphApp), cluster validity indices (PyCVI and ClusterExp) as well as the impact of different distance metrics on the training and evaluation of machine learning models.

    While doing an internship at the Nansen Center in Bergen (Norway) in 2019, I also carried out research on machine learning applied to ocean inverse problems, using Self-Organising Maps and Hidden Markov Models to infer subsurface data from surface data (SubMAPP).

    Teaching

    During my PhD, I have been lucky enough to be a teaching assistant in 2 machine learning courses, INF264: Introduction to Machine Learning (INF264 and Python Crash Course) and INF265: Deep Learning (INF265 and PyTorch Tutorials). There, I was responsible for the practical part of the courses and could then design many exercises (with solutions) and tutorials.

    In addition, I have been a lecturer / course coordinator in introductory courses in programming and python, DIGI611: Algoritmer og programmering (DIGI611, note: resources in Norwegian).

    Pinned Loading

    1. pycvipycviPublic

      Internal Cluster Validity Indices in python, compatible with time-series data

      Python 4 2

    2. ukbppp_dlukbppp_dlPublic

      Package for easy, robust and efficient download of significant pQTL data from UKB PPP

      Python 3

    3. pytorch-tutorialspytorch-tutorialsPublic

      Thorough and step by step PyTorch tutorials

      Jupyter Notebook

    4. digi611digi611Public

      A Practical Introduction to Algorithms and Programming in Python for complete beginners (in Norwegian)! 100% online course offered by the University of Bergen (Norway) in spring 2024

      Python

    5. inf265inf265Public

      Practical part of our deep learning course (targeting students with already one semester of machine learning). In python (PyTorch)

      Jupyter Notebook 1

    6. python-crash-coursepython-crash-coursePublic

      A Python crash course, designed for the machine learning course "INF264: Introduction to machine learning"

      Jupyter Notebook