I work as a postdoctoral researcher in the Computer Vision group supervised by Prof. Dr. Michael Moeller at the University of Siegen since April 2023. Previously, I was a Ph.D. candidate supervised by Prof Dr.-Ing. Margret Keuper in the Computer Vision and Machine Learning group at the Max-Planck-Institute for Informatics and in the focus group of Computer Vision in the Data and Web Science Group at the University of Mannheim.
Together with Aaron Klein, Arber Zela, Giovanni Zappella and Rhea Sukthanker I also organize the AutoML Seminars as part of the ELLIS units Berlin and Freiburg. I was part of the organization team of the second workshop on neural architecture search @ ICLR 2021.
My research mainly focuses on efficient, unsupervised graph representations and embeddings for supervised surrogate models for neural architecture search.
News
- 11/2023 I presented my work about Multi-Objective Performance Prediction for Neural Architecture Search at the Doctoral Consortium at BMVC 2023!
- 10/2023 We have one workshop paper (Implicit Representations for Image Segmentation) accepted to Unireps@NeurIPS
- 07/2023 Our paper An Evaluation of Zero-Cost Proxies - from Neural Architecture Performance to Model Robustness was accepted at GCPR 2023
- 07/2023 I successfully defended my Ph.D. thesis with the title Topology Learning for Prediction, Generation, and Robustness in Neural Architecture Search
- 04/2023 I started as a PostDoc at the University of Siegen in the Group of Prof. Dr. Michael Moeller
- 01/2023 Our paper Neural Architecture Design and Robustness: A Dataset was accepted at ICLR 2023
- 09/2022 Our paper Learning Where to Look - Generative NAS is Surprisingly Efficient was accepted at ECCV 2022
Publications
Implicit Representations for Image Segmentation
J. P. Schneider, M. Fatima, J. Lukasik, A. Kolb, M. Keuper, M. Moeller
accepted at Unireps@NeurIPS 2023
An Evaluation of Zero-Cost Proxies - from Neural Architecture Performance to Model Robustness
J. Lukasik, M. Moeller, M. Keuper
GCPR 2023 (oral)
PDF
Differentiable Architecture Search: a One-Shot Method?
J. Lukasik*, J. Geiping*, M. Moeller, M. Keuper
AutoML Conference 2023 Workshops
Neural Architecture Design and Robustness: A Dataset
S. Jung*, J. Lukasik*, M. Keuper
ICLR 2023
Website | PDF | Code
Learning Where to Look - Generative NAS is Surprisingly Efficient
J. Lukasik* , S. Jung*, M. Keuper
ECCV 2022
PDF | Code | Poster
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks
A. Zela*, JN. Siems*, L. Zimmer*, J. Lukasik, M. Keuper, F. Hutter
ICLR 2022
PDF
DARTS for Inverse Problems: a Study on Sensitivity
J. Geiping*, J. Lukasik*, M. Keuper, M. Moeller
NeurIPS workshop on Inverse Problems
PDF | Poster
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search
J. Lukasik, D. Friede, A. Zela, F. Hutter, M. Keuper
IJCNN 2021
PDF | Code
Neural Architecture Performance Prediction Using Graph Neural Networks
J. Lukasik, D. Friede, H. Stuckenschmidt, M. Keuper
GCPR 2020
PDF