I am a Machine Learning Researcher, interested in generalisation and robustness in deep learning and reinforcement learning. Since 2019, I am a Postdoctoral Research Scientist at the LIT AI LAB, and in 2021 I was part of the cohort 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.
- 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.