Permanent researchers
Joaquin Vanschoren is an associate professor and head of the Automated Machine Learning lab at TU Eindhoven. He aims to scientifically understand (human-like) intelligence and build AI systems with advanced capabilities for the benefit of all humanity. He authored the first book on AutoML, gave tutorials at NeurIPS and AAAI and won several awards, including the Dutch Data Prize and Amazon Research Award. He founded OpenML, a useful open science platform for machine learning, and co-founded the Croissant standard for sharing AI resources. He was the inaugural chair of the NeurIPS Datasets and Benchmarks track, editor-in-chief of the DMLR journal, and co-chair of the MLCommons AI Risk & Reliability working group. He is a founding member of the European AI societies ELLIS and CAIRNE.
Research engineers
Pieter Gijsbers is working on making (automated) machine learning research simple through developing open source software. He is a long-term contributor to openml-python, started the AutoML Benchmark and GAMA. Pieter is currently working on improving the AI-on-Demand platform.
Taniya Das is a Research Engineer solving various engineering and machine learning tasks for openml.org and AI-on-Demand platform (an EU project), to make ML research better. She has contributed to openml-tensorflow and openml-pytorch extensions for OpenML, making it possible to use solve deep learning tasks using openml-python API. She is currently working on using LLMs to make both the platforms more intelligent.
Subhaditya Mukherjee is a Research Engineer working on various engineering and machine learning tasks for openml.org. He is currently working on making the OpenML experience better and more user-friendly.
Lecturers
Prabhant Singh I am currently working as a lecturer and researcher. I have experience in Python, data and systems as well as infrastructure engineering.
Postdocs
Alexis Cvetkov-Iliev is a postdoc in the group, applying machine-learning techniques to study and optimize building renovation. His research interests include active learning, Bayesian optimization, and transfer learning.
PhD students
Andrei Simion-Constantinescu is a PhD Researcher in the AutoML group focused on self-supervised learning for computer vision tasks. He holds an MSc degree from TU Delft with the graduation topic on contrastive learning for unlabeled videos. His work involves adapting state-of-the-art self-supervised techniques to solve practical problems in greenhouse crop prediction and vertical farming as part of NWO Sky High Project.
Israel Campero Jurado is a PhD student in computer science working on the European ITEA project called INNO4HEALTH. His interests focus on applying automated machine learning (AutoML) to healthcare solutions and democratising AutoML.
Jiarong Pan is a PhD student in the group, working on improving sample efficiency in machine learning algorithms. His research interests include meta-learning, Bayesian optimization and multi-objective optimization.
Fangqin Zhou is a PhD student working on the TWINERGY project, which aims to optimize the growth of cherry tomatos in a vertical farm. Her interests focus on the Hyperspectral Imaging using deep learning and transformer models, as well as Reinforcement Learning.
Elif Ceren Gok Yildirim is a PhD student working on continual learning. Her research centers on exploring the capabilities of machine learning algorithms and advancing them to adapt and evolve over time. She likes to explore novel approaches to enable machines to accumulate knowledge from past experiences and apply it to new tasks without forgetting and contribute to the CL field.
Murat Onur Yildirim is a PhD Candidate in the AutoML group. He focuses on automatically and continually learning sparse experts for computer vision tasks. His research is dedicated to creating efficient continual learners by leveraging sparsity in networks, data, and labels.
Bilge Celik is a PhD student focusing on Automated Machine Learning for online data streams. She develops machine learning systems automatically adjusting to changing dynamics of real-time data streams.
Alumni and visitors
Anna Vettoruzzo received her B.Sc. degree in Information Engineering at the University of Padova, Italy, in 2019, and her M.Sc. degree in ICT for Internet and Multimedia at the University of Padova in 2021, with a focus on Machine Learning for Healthcare. Since 2021, she has been pursuing a Ph.D. degree in the field of machine learning at the School of Information Technology, Halmstad University, Sweden, under the supervision of Assoc. Prof. Mohamed-Rafik Bouguelia and Prof. Thorsteinn Rögnvaldsson. Her research interests include meta-learning, few-shot learning, continual learning, and, more broadly, enhancing the generalization capabilities of machine learning models.
Branislav Pecher is a former visiting PhD student from Kempelen Institute of Intelligent Technologies. His research mainly focuses on learning with limited labelled data and a better understanding of its sensitivity to different factors that influence how well these approaches work. During the visit, he worked on few-shot learning, exploring how different subsets of samples of different characteristics affect the success of transfer in these approaches, and proposing a new method for selecting a subset of high-quality samples for few-shot learning.
Jos van der Velde is a senior engineer focusing on software architecture and infrastructure. I previously worked on the Croissant standard, and the AI-on-Demand platform. I'm still actively contributing to the OpenML infrastructure.
Mert Kilickaya is a former postdoctoral fellow researching autonomous visual learners that can continuously improve by extracting their own supervision from dynamic visual data streams. He advanced techniques in self-supervised continual learning, enabling AI systems to autonomously adapt and improve without relying on external annotations.
Rafael Gomes Mantovani is a former PhD student, and currently a professor at the Federal Technology University - Paraná (UTFPR), campus of Apucarana, Brazil. He researches Machine Learning, Data Mining, Meta-learning, and Automated Machine Learning/Data Science.