PhD in Scientific Computing (IT department, Uppsala University) Sept 2024 -

This page documents the progress of my PhD in Scientific Computing. The program is expected to take approximately five years and includes:

• At least 70 credits (hp) of coursework
• One year of departmental duties, including teaching and supervision

Currently, I am in my first year and primarily focused on coursework, familiarizing myself with ongoing research projects, taking courses and supervising Master’s students.

Supervising and reviewing student projects

• 2025 VT
  • Co-Supervising master thesis projects with Prashant Singh:
    • Viktor Bergstedt:
      • Thesis Simulating Complex Particle Dynamics with Graph Neural Networks
      • Other work: BiTrain an experimental regression package based on bitwise classification
    • Alessia Rossi:
      • Research project: Adaptive Covariance Estimation in Convex Multi-Objective Optimization (ongoing)
  • Reviewer for Master’s thesis:
    • Linjing Shen: Oversampling Time-Series Compositional Data for Classification of Microbiome Samples

Teaching duties

• UPCOMING (2025 HT)
  • Teaching assistant for 1TD354 Scientific Computing for Partial Differential Equations (5.0hp)

Courses / Credits taken so far (36.5 / 70.0)

• 7.5hp Advanced probabilistic machine learning - FTN0204 (2024 HT)
• 5.0hp Statistical Machine Learning - FTN0061 (2024 HT)
• 4.0hp Numerical Linear Algebra - FTN0577 (2024 HT)
• 3.5hp Numerical Optimization - FTN0578 (2024 HT)
• 2.0hp Ethics of Technology and Science, part I - FTN0001 (2025 VT)
• 2.0hp Summer school on Generative modelling, GEMSS and Statlearn (2025 VT)
• 7.5hp Probability Theory and Statistics by the maths department (2025 VT)
• 5.0hp Techniques and Technologies forScientific Software Engineering - FTN0439 (2025 VT)

Future planed courses

• 10.0hp Statistical Learning for Data Science (2025 HT)
• 7.5hp Academic Teacher Training Course (2025 HT)
• 5.0hp Mathematical foundations of scientific computing (2025 HT)
• 5.0hp Simulation technologies (2026 VT)

Summer Schools

• Generative Modeling Summer School / Statlearn Mar 31st - Apr 4th 2025
  • The summer school covered a broad range of topics in generative modeling, from foundational concepts such as Gaussian Mixture Models (GMMs), Probabilistic Circuits, and Probabilistic PCA, to more advanced models including Normalizing Flows, Score-Based Diffusion Models, and Subtractive Sum-of-Squares Models.
  • The program featured excellent lectures by leading researchers, including Benjamin Billot, Gilles Louppe, Antonio Vergari, and Yingzhen Li.
  • It was also a great opportunity to meet and exchange ideas with fellow PhD students from across Europe who are working on similar research topics.