I am a graduate student in the School of Computer Science at Tel-Aviv University, where I am advised by Lior Wolf. I hold an MS.c. in Physics from Tel-Aviv University, where I was supervised by Nissan Itzhaki. In addition, I work as an Applied Scientist at Amazon.


  1. M. Rotman, A. Dekel, R. Ber, L. Wolf and Y. Oz. Semi-supervised Learning of Partial Differential Operators and Dynamical Flows, Uncertainty in Artificial Intelligence (UAI), 2023. Preliminary arXiv version
  2. M. Rotman and L. Wolf. Energy Regularized RNNs for Solving Non-Stationary Bandit Problems. International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023. Preliminary arXiv version
  3. E. Sheffi, M. Rotman and L. Wolf. Gradient Adjusting Networks for Domain Inversion. Scandinavian Conference on Image Analysis (SCIA), 2023. Preliminary arXiv version
  4. M. Rotman, A. Dekel, S. Gur, Y. Oz, and L. Wolf. Unsupervised disentanglement with tensor product representations on the torus. International Conference on Learning Representations (ICLR), 2022.
  5. M. Rotman and L. Wolf. Natural statistics of network activations and implications for knowledge distillation. International Conference on Image Processing (ICIP), 2021. Preliminary arXiv version
  6. I. Reis, M. Rotman, D. Poznanski, J.X. Prochaska, and L. Wolf. Effectively using unsupervised machine learning in next generation astronomical surveys. Astronomy and Computing, 2021. Preliminary arXiv version
  7. M. Rotman and L. Wolf. Shuffling recurrent neural networks. AAAI Conference on Artificial Intelligence (AAAI), 2021.
  8. M. Rotman, R. Brada, I. Beniaminy, S. Ahn, C. J. Hardy, and L. Wolf. Correcting motion artifacts in MRI scans using a deep neural network with automatic motion timing detection. In Medical Imaging 2021: Physics of Medical Imaging (SPIE), 2021. Preliminary arXiv version
  9. M. Rotman and L. Wolf. Electric analog circuit design with hypernetworks and a differential simulator. International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020. Preliminary arXiv version
  10. M. Rotman, I. Reis, D. Poznanski, and L. Wolf. Detect the unexpected: Novelty detection in large astrophysical surveys using fisher vectors. International Conference on Knowledge Discovery and Information Retrieval (KDIR), 2019.
  11. R. Brada, M. Rotman, R. Wein, S. Ahn, I. Malkiel, and C. J. Hardy. Towards motion-robust MRI – autonomous motion timing and correction during mr scanning using multi-coil data and a deep-learning neural network. International Society for Magnetic Resonance in Medicine 27th Annual Meeting (ISMRM), 2019.
  12. O. Koshorek, A. Cohen, N. Mor, M. Rotman, and J. Berant. Text segmentation as a supervised learning task. North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), 2018.

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