M. Kuschel, T. Hasija, and T. Marrinan, “Rademacher Complexity Regularization for Correlation-Based Multiview Representation Learning,”10.1109/icassp48485.2024.10446173__2024, doi:10.1109/icassp48485.2024.10446173 .

S. Vieluf, T. Hasija, M. Kuschel, C. Reinsberger, and T. Loddenkemper, “Developing a deep canonical correlation-based technique for seizure prediction,”10.1016/j.eswa.2023.120986__Expert Systems with Applications, vol. 234, Art. no. 120986, 2023, doi:10.1016/j.eswa.2023.120986 .

M. Kuschel, T. Marrinan, and T. Hasija, “Geodesic-Based Relaxation For Deep Canonical Correlation Analysis,”10.1109/mlsp55844.2023.10285937__2023, doi:10.1109/mlsp55844.2023.10285937 .

T. Hasija and T. Marrinan, “A GLRT for estimating the number of correlated components in sample-poor mCCA,” in Proc. 30th European Signal Processing Conference (EUSIPCO), 2022, pp. 2091–2095.

I. Lehmann et al., “Multi-Task fMRI Data Fusion Using IVA and PARAFA,”10.1109/ICASSP43922.2022.9747662__in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 1466–1470, doi:10.1109/ICASSP43922.2022.9747662 .

I. Lehmann et al., “Multi-Task fMRI Data Fusion Using IVA and PARAFA,”10.1109/icassp43922.2022.9747662__2022, doi:10.1109/icassp43922.2022.9747662 .

T. Hasija, T. Marrinan, C. Lameiro, and P. J. Schreier, “Determining the dimension and structure of the subspace correlated across multiple data sets,”10.1016/j.sigpro.2020.107613__Signal Processing, vol. 176, 2020, doi:10.1016/j.sigpro.2020.107613 .

S. Vieluf, V. Scheer, T. Hasija, P. Schreier, and C. Reinsberger, “Multimodal approach towards understanding the changes in the autonomic nervous system induced by an ultramarathon.,” Res Sports Med, vol. 28, no. 2, pp. 231–240, 2020.

T. Hasija, M. Gölz, M. Muma, P. J. Schreier, and A. M. Zoubir, “Source Enumeration and Robust Voice Activity Detection in Wireless Acoustic Sensor Networks,” 2019.

C. Lameiro, T. Hasija, T. Marrinan, and P. J. Schreier, “Estimating the Number of Correlated Components Based on Random Projections,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 5152–5156.

S. Vieluf, T. Hasija, R. Jakobsmeyer, P. Schreier, and C. Reinsberger, “Exercise-Induced Changes of Multimodal Interactions Within the Autonomic Nervous Network.,” Front Physiol, vol. 10, p. 240, 2019.

T. Marrinan, T. Hasija, C. Lameiro, and P. J. Schreier, “Complete Model Selection in Multiset Canonical Correlation Analysis,” in Proc. European Signal Process. Conf. (EUSIPCO), 2018, pp. 1082–1086.

T. Hasija, C. Lameiro, and P. J. Schreier, “Determining the dimension of the improper signal subspace in complex-valued data,” IEEE Signal Process. Lett., vol. 24, pp. 1606–1610, 2017.

Y. Song, P. J. Schreier, D. Ramírez, and T. Hasija, “Canonical correlation analysis of high-dimensional data with very small sample support,” Signal Process., vol. 128, pp. 449–458, 2016.

T. Hasija, Y. Song, P. J. Schreier, and D. Ramírez, “Detecting the dimension of the subspace correlated across multiple data sets in the sample poor regime,” 2016.

T. Hasija, Y. Song, P. J. Schreier, and D. Ramírez, “Bootstrap-based detection of the number of signals correlated across multiple data sets,” 2016.

Y. Song, T. Hasija, P. J. Schreier, and D. Ramírez, “Determining the number of signals correlated across multiple data sets for small sample support,” 2016.