MSc Leo Guo

PhD student
Electronic Components, Technology and Materials (ECTM), Department of Microelectronics

Expertise: Numerical methods/analysis for hyperbolic PDEs, regression methods, graph theory, functional analysis.

Themes: Micro/Nano System Integration and Reliability


I am a double bachelor degree holder (Applied Physics/Applied Mathematics) from TU Delft and post-graduate (Applied Mathematics) from the Swiss Federal Institute of Technology (ETH Zürich), with a specialization in numerical methods and analysis for partial differential equations. While most of my rich programming experience has been with MATLAB and Python, I also possess technical know-how of R, C# and SQL.
After graduating from ETH Zürich, I have been employed as a software developer at a medium-small sized Dutch health insurance organization (DSW Zorgverzekeraar). A collection of educational research items which I have worked on during my academic career can be found on my personal webpage.

I am a person who is passionate about mathematics in general, but my mathematical interest is greatest when problems that arise in physics are concerned. I often tell interested friends and family members about my findings in an enthusiastic manner. I am a caring person, who naturally likes to help other people, especially in my field of expertise. I frequently reach out to individuals on online platforms who are in need of help in mathematics and physics questions, basic or advanced.

Ever since I have started working at this current position, I have mainly been focusing on the following areas of research:
- Statistical regression and symbolic regression
- The application of data-driven methods in optimization strategies e.g. Bayesian optimization and constrained Bayesian optimization
- Finite Element analysis and modeling in context of DOE optimization and surrogate modeling

Projects history


European research project Power2Power for more efficient power semiconductors

  1. Centimeter-scale nanomechanical resonators with low dissipation
    Andrea Cupertino; Dongil Shin; Leo Guo; Peter G. Steeneken; Miguel A. Bessa; Richard A. Norte;
    2023. DOI: 10.48550/arXiv.2308.00611

  2. Bayesian optimization with Gaussian process regression: a multi-fidelity review
    Leo Guo;
    In 15th World Congress of Structural and Multidisciplinary Optimisation,

BibTeX support

Last updated: 3 Oct 2022