saba-sadiya [at] em.uni-frankfurt.de
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About
I am a Humboldt Researcher Associate at the Frankfurt Institute of Advanced Studies (FIAS), working at Dr Gemma Roig's Computational Vision & AI lab. I am also affiliated with Dr Radoslaw Cichy's Neural Dynamics of Visual Cognition lab.
Previously I completed a dual Ph.D in Computer Science under Dr Mohammad Ghassemi, and Cognitive Neuroscience under Dr Taosheng Liu. I earned Bachelors in both Computer Science and Mathematics from the "Technion - Institute of Technology" and subsequently worked as a VLSI engineer at Apple Inc. before becoming a Fulbright grantee in MSU.
My main areas of interest are neural representations, bioinformatics, and ML interpretability. My PhD thesis focused on utilizing electroencephalography in conjunction with state-of-the-art machine learning techniques to investigate how attention modulates sensory representations.
I spend the little free time I have learning to play various instruments to varying degrees of success.
Project Highlights
Efficient Shortcut and Bias Mitigation
Even SOTA models like CLIP are riddled with shortcuts (In ImageNet, a watermark in Hanzi correlates with the 'cardboard' class). I develop methods to mitigate such biases, focusing on interpretability, label efficiency, and resources accessibility.
Papers:Efficient unsupervised shortcut learning detection and mitigation in transformers In Proceedings of the 2025 International Conference on Computer Vision (ICCV), 2025 L. Kuhn*, S. Saba-Sadiya*, J. Schlotterer, F. Buettner, C. Seifert, R. Gemma
[pdf] [bib] [code]Weakly Supervised Shortcut Mitigation Using Sparse Projections Online Preprint, 2025 M. Ahsan*, D. Towadras*, S. Saba-Sadiya*, J. Schlotterer, F. Buettner, C. Seifert, R. Gemma
[pdf] [bib] [code]
Feature Imitating Networks
Novel framework for integrating expert knowledge into deep learning networks by initalizing weights to approximate known statistical measures.
Papers:Feature Imitating Networks In Proceedings of the 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 S. Saba-Sadiya and T. Alhanai and T. Liu and M. Ghassemi
[pdf] [bib] [code]MambaNet: A Hybrid Neural Network for Predicting the NBA Playoffs In SN Computer Science 5 (5) , 2022 R. Khanmohammadi, S. Saba-Sadiya and T. Alhanai and T. Liu and M. Ghassemi
[pdf] [bib] [code]
Artifact Detection and Correction in EEG Data
Applying state-of-the-art self-supervised machine learning techniques to artifact detection and correction in EEG data.
Papers:Different Algorithms (Might) Uncover Different Patterns: A Brain-Age Prediction Case Study In Proceedings of the 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2023 T. Ettling*, S. Saba-Sadiya*, and G. Roig
[pdf] [bib] [code]Unsupervised EEG Artifact Detection and Correction In Frontiers in Digital Health, Special issue on Machine Learning in Clinical Decision-Making. Volume 2, 2020 S. Saba-Sadiya and E. Chantland and T. Alhanai and T. Liu and M. Ghassemi
[pdf] [bib] [code]EEG Channel Interpolation Using Deep Encoder-Decoder Networks In Proceedings of the 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020 S. Saba-Sadiya and T. Alhanai and T. Liu and M. Ghassemi
[pdf] [bib] [code]
