My Academic Life

My research focus is on the intersection of Automated Machine Learning (AutoML), Automated Artificial Intelligence (AutoAI), and Time Series Analysis.

Selected Publications

Time Series Representations Classroom (TSRC): A Teacher-Student-based Framework for Interpretability-enhanced Unsupervised Time Series Representation Learning

Wadie Skaf, Mitra Baratchi, and Holger Hoos
Machine Learning, 2025
DOI

Neural Architecture and Hyperparameter Selection through Meta-Learning on Time Series

Erfan Moeini, Christopher Vox, Marie Anastacio, Wadie Skaf, Mitra Baratchi, and Holger H. Hoos
Proceedings of the AAAI Conference on Artificial Intelligence (To appear), 2026
URL

Denoising Architecture for Unsupervised Anomaly Detection in Time-Series

Wadie Skaf and Tomáš Horváth
European Conference on Advances in Databases and Information Systems, 2022
DOI

Full list of publications can be found on Google Scholar

Academic Supervision

Current Students

Aryamaan Basu Roy

Master of Science (M.Sc.)
Computer Science
RWTH Aachen University
2025 - Ongoing

Thesis Title

Conformalised Augmentation Selection for Time Series

Past Students

Elliot Johnson

Bachelor of Science (B.Sc.)
Computer Science
RWTH Aachen University
2024 - 2025

Thesis Title

Studying the Relationship between Window Size and Other Hyperparameters in Deep Learning-based Time Series Forecasters

View Thesis

Erfan Moeini

Master of Science (M.Sc.)
Computer Science
RWTH Aachen University
2024 - 2025

Thesis Title

Minimizing the Gap between AutoML and Time Series Domain

Co-supervised with: Marie Anastacio

In collaboration with:
Volkswagen AG (Supervisor: Christopher Vox)

Related Publications

  • Moeini E, Vox C, Anastacio M, Skaf W, Baratchi M, Hoos HH. Neural Architecture and Hyperparameter Selection through Meta-Learning on Time Series. Proceedings of the AAAI Conference on Artificial Intelligence (To appear) 2026.

Jesse Kroll

Bachelor of Science (B.Sc.)
Data Science and Artificial Intelligence
Leiden University (Universiteit Leiden)
2024 - 2025

Thesis Title

Evaluating Adversarial Robustness in Time-Series Classification: A Comparative Study on Self-Supervised Learning Models

Co-supervised with: Mitra Baratchi

View Thesis

Simon Klemp

Master of Science (M.Sc.)
Computer Science
RWTH Aachen University
2024 - 2025

Thesis Title

Automated Machine Learning (AutoML) for Feedback-Driven Optimisation of Time Series Anomaly Detection in Recycling Plants

In collaboration with:
Eggersmann Gruppe GmbH & Co. KG (Supervisors: Lukas Kreisköther, Sebastian Felder)

Teaching

Academic Year 2025/2026

Winter Semester

Efficient Artificial Intelligence (AI) with Rust

EffAIRust • Lab • Graduate (Master)
RWTH Aachen University
Germany
Lead Instructor

This lab is designed to introduce students to the basics of Rust programming and its applications in artificial intelligence.

Main Topics

  • Introduction to Rust programming language
  • Implementing basic AI models and ML algorithms using Rust
  • Using Rust in combination with Python (PyO3) to accelerate ML workflows

Technologies

Rust Python PyO3

Instruction Languages

English

Summer Semester

Efficient Artificial Intelligence (AI) with Rust

EffAIRust • Lab • Graduate (Master)
RWTH Aachen University
Germany
Lead Instructor

This lab is designed to introduce students to the basics of Rust programming and its applications in artificial intelligence.

Main Topics

  • Introduction to Rust programming language
  • Implementing basic AI models and ML algorithms using Rust
  • Using Rust in combination with Python (PyO3) to accelerate ML workflows

Technologies

Rust Python PyO3

Instruction Languages

English

Academic Year 2024/2025

Summer Semester

Hyperparameter Optimisation for Machine Learning

HPOML • Lab • Graduate (Master)
RWTH Aachen University
Germany
Instructor

This lab is designed to introduce students to the basics of hyperparameter optimisation for machine learning.

Main Topics

  • Introduction to hyperparameter optimisation
  • Grid Search
  • Random Search
  • Successive Halving
  • Bayesian Optimisation

Technologies

Python Scikit-Learn Pandas NumPy SMAC

Instruction Languages

English

Academic Year 2022/2023

Summer Semester

Introduction to Data Science

ITDS • Lecture • Graduate (Master)
Eötvös Loránd University (ELTE)
Hungary
Teaching Assistant • Practical Section (Lab)

This course is designed to introduce students to the basics of data science.

Main Topics

  • Introduction to data science
  • Data cleaning and preprocessing
  • Classification
  • Regression
  • Clustering
  • Dimensionality Reduction
  • Recommender Systems
  • Natural Language Processing
  • Deep Learning

Technologies

Python Pandas Scikit-Learn PyTorch NumPy Matplotlib Seaborn Plotly SciPy

Instruction Languages

English