Software and instructions for setting up and running a self-driving lab (autonomous experimentation) demo using dimmable RGB LEDs, an 8-channel spectrophotometer, a microcontroller, and an adaptive design algorithm, as well as extensions to liquid- and solid-based color matching demos.

Scalable Non-myopic Bayesian Optimization in Dynamic Cost Settings

Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.

Bayesian Optimization and Design of Experiments

A package for the optimisation of numerical responses

Project repository for the development of a Question-Answering (QA) information retrieval system fine-tuned on customer queries.

This project leverages spotify's api and provided user playlists to create and tune a neural network model that generates song recommendations based off of song data in provided playlists.

An evaluation framework for machine learning models simulating high-throughput materials discovery.

Discrete Bayesian optimization with LLMs, PEFT finetuning methods, and the Laplace approximation.

Pre-trained Gaussian processes for Bayesian optimization

Experimental design and (multi-objective) bayesian optimization.

Official implementation of TEVC'2024 paper "Hypervolume-Guided Decomposition for Parallel Expensive Multiobjective Optimization"

This repository contains resources about my optional master semester research project performed with the Sycamore lab at EPFL. We studied mechanism design and preferential bayesian optimization for the maximization of unknown welfare functions.

Bayesian optimization using Gaussian Process regression (Python)

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

Change-point detection, rate-monitoring and pattern analysis for time-tagged event data using Bayesian Blocks (Scargle, 2013) and Non-Negative Matrix Factorization (NMF)

PHYSBO -- optimization tools for PHYsics based on Bayesian Optimization

Official implementation of Black-box Universal Adversarial Attacks with Bayesian Optimization.

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.