I am a Postdoctoral Research Scientist at the Linz Institute of Technology (LIT) AI LAB, and a member of Google Cloud Research Innovators. I received my PhD degree in 2019 on Representation Learning and Inference from Signals and Sequences. In 2014, I joined the Institute of Computational Perecption at the Johannes Kepler University of Linz, where I pursued my PhD.
Causal Contextual Reinforcement Learning
Building agents that can reason, and discover the causal factors of the world.
Understanding Deep Learning
Understanding how deep learning works, and how the underlying building blocks that make DL successful function.
Robustness and Generalisation in DL
Adversarial Machine Learning, Learning under Distribution mismatch, Robustness in DL
Learning robust representations from genomic data, molecules, audio sequences and images.
- W. Zellinger, N. Shepeleva, M.C. Dinu, H. Eghbal-zadeh, H.D. Nguyen, B. Nessler, S. Pereverzyev, B.A. Moser, The balancing principle for parameter choice in distance-regularized domain adaptation, In Proceedings of Advances in Neural Information Processing Systems, 2021.
- K. Koutini, H. Eghbal-zadeh, G. Widmer, Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks, in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 1987-2000, 2021, doi: 10.1109/TASLP.2021.3082307. [paper][code]
- H. Eghbal-zadeh, F. Henkel, G. Widmer, Context-Adaptive Reinforcement Learning using Unsupervised Learning of Context Variables, In Proceedings of Machine Learning Research, NeurIPS 2020 Workshop on Pre-registration in Machine Learning, PMLR 148:236-254, 2021. [paper][blog post]
- H. Eghbal-zadeh, F. Henkel, G. Widmer, Learning to Infer Unseen Contexts in Causal Contextual Reinforcement Learning, SSL-RL Workshop, ICLR, 2021. [paper][environment]
- H. Eghbal-zadeh, F. Henkel, G. Widmer, Context-Adaptive Reinforcement Learning using Unsupervised Learning of Context Variables, Pre-registration Workshop, NeurIPS, 2020. [blog post]
- H. Eghbal-zadeh, K.Koutini, V. Haunschmid, P. Primus, M. Lewandowski, W. Zellinger, G. Widmer, Adversarial Robustness in Data Augmentation, Towards Trustworthy ML: Rethinking Security and Privacy for ML, ICLR Workshop, 2020. [talk]
- H. Eghbal-zadeh, Representation Learning and Inference from Signals and Sequences, PhD Thesis, 2019.
- H. Eghbal-zadeh, W. Zellinger, G. Widmer, Mixture Density Generative Adversarial Networks, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. [paper] [code]
- H. Eghbal-zadeh, M. Dorfer, G. Widmer, Deep Within-Class Covariance Analysis for Robust Audio Representation Learning, Advances in Neural Information Processing Systems, Interpretability and Robustness in Audio, Speech, and Language Workshop, 2018. [paper] [slides]
- H. Eghbal-zadeh, L. Fischer, N. Popitsch, F. Kromp, S. Taschner-Mandl, T. Gerber, E. Bozsaky, P. F. Ambros, I. M. Ambros, G. Widmer, B. A. Moser, DeepSNP: An End-to-End Deep Neural Network with Attention-Based Localization for Breakpoint Detection in Single-Nucleotide Polymorphism Array Genomic Data, in Journal of Computational Biology, 2018. [paper] [code]
If you are from an under represented group, and need help with ML research or similar topics, you can book a mentoring session with me.