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AwesomeLemon/README.md

👋 Hi! I’m a PhD student in the Evolutionary Intelligence group at the Dutch Institute for Maths & Computer Science (CWI).

My primary research interests are Hyperparameter Optimization and Neural Architecture Search (NAS). I believe leveraging Evolutionary Algorithms (EAs) for non-differentiable Machine Learning problems such as these is a potentially high-impact research direction. In addition to not requiring gradients, most EAs are trivial to parallelize & scale, so they are fully compatible with Sutton’s Bitter Lesson.

Research-wise I’m also interested in Explainable AI (my Master thesis was partly about using NAS for explainability) and image generation (the applied part of my PhD project is about privacy-preserving medical image data sharing with GANs).

Life-wise I’m into fantasy, hiking, and discussing weird ideas.

Always happy to chat about these topics!

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  1. ENCAS ENCAS Public

    NAS + Cascades | Best Paper @ GECCO 2022

    Python 15 4

  2. PBT-NAS PBT-NAS Public

    Population Based Training for Neural Architecture Search

    Python 3

  3. MO-PBT MO-PBT Public

    Forked from ArkadiyD/MO-PBT

    An algorithm for multi-objective hyperparameter optimization: Multi-Objective Population Based Training (MO-PBT) | ICML 2023

    Python

  4. HyFree-S3 HyFree-S3 Public

    Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific Segmentation

    Python 2