OPTIMEO is a web application doubled by a package that helps you optimize your experimental process by generating a Design of Experiment, generating new experiments using Bayesian Optimization (BO), and analyzing the results of your experiments using Machine Learning models.

Demonstrating the use of Prefect to orchestrate the creation of machine learning surrogate models as applied to mechanistic crop models.

Using Bayesian optimization via Ax platform + SAASBO model to simultaneously optimize 23 hyperparameters in 100 iterations (set a new Matbench benchmark).

Bayesian optimization using Gaussian Process regression (Python)

Predicting Next Booking Destinations for Airbnb Users. Feel free to access the Streamlit App in the link below.

Testing submission of AWS batch jobs using BoTorch (via Ax) and TorchX for neural architecture search

Exploring Gaussian Processes with Lithuanian weather forecast data

Bayesian optimisation of prophet temperature model parameters with daily and yearly seasonalities plus extra regressors

This project presents an end-to-end machine learning solution to problem of predicting salary from job offers posted on linkedin.

An analysis of travel insurance policies

NLP research project on contemporary romantic fiction, combining BERTopic, OCTIS, and Goodreads ratings to model which themes drive higher book scores.

Developing delivery time estimation model using Neural Networks

Optimizing image processing in retinal prosthetics using human-in-the-loop optimization.

Contains all code and course work for a module on reinforcement learning.

Mithilfe von Machine Learning und Open Data zu Unfällen in Berlin (2018-2021) beantworten wir folgende Frage: Was sind die wichtigen Faktoren/Einflüsse auf Unfallgefahr? Und wie gut lässt sich damit die Unfallschwere überhaupt vorhersagen?