Department of Microelectronics
Faculty of Electrical Engineering, Mathematics, and Computer Science

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: Autonomous sensor systems

Biography

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

Power2Power

European research project Power2Power for more efficient power semiconductors

  1. Efficient and Adaptive Bayesian Data-Driven Design of Reliable Solder Joints for Micro-electronic Devices
    Leo Guo; Adwait Inamdar Willem D. van Driel; Guoqi Zhang;
    Applied Mathematical Modelling,
    Volume 154, pp. 116645, 2026. DOI: 10.1016/j.apm.2025.116645

  2. Multi-objective Bayesian optimisation of spinodoid cellular structures for crush energy absorption
    Hirak Kansara; Siamak F. Khosroshahi; Leo Guo; Miguel A. Bessa; Wei Tan;
    Computer Methods in Applied Mechanics and Engineering,
    Volume 440, pp. 117890, 2025. DOI: 10.1016/j.cma.2025.117890

  3. Adaptive Bayesian Data-Driven Design of Reliable Solder Joints for Micro-electronic Devices
    Leo Guo; Adwait Inamdar; Willem D. van Driel; GuoQi Zhang;
    arXiv:2507.19663,
    2025. DOI: 10.48550/arXiv.2507.19663

  4. Multi-fidelity Bayesian Data-Driven Design of Energy Absorbing Spinodoid Cellular Structures
    Leo Guo; Hirak Kansara; Siamak F. Khosroshahi; GuoQi Zhang; Wei Tan;
    arXiv:2507.22079,
    2025. DOI: 10.48550/arXiv.2507.22079

  5. Centimeter-scale nanomechanical resonators with low dissipation
    Andrea Cupertino; Dongil Shin; Leo Guo; Peter G. Steeneken; Miguel A. Bessa; Richard A. Norte;
    Nature Communications,
    Volume 15, pp. 4255, 2024. DOI: 10.1038/s41467-024-48183-7

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

  7. Bayesian optimization with Gaussian process regression: a multi-fidelity review
    Leo Guo;
    In 15th World Congress of Structural and Multidisciplinary Optimisation,
    2023.

BibTeX support

Last updated: 3 Oct 2022

Leo Guo