Rademacher Complexity Regularization for Correlation-Based Multiview Representation Learning
M. Kuschel, T. Hasija, T. Marrinan, in: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2024.

Developing a deep canonical correlation-based technique for seizure prediction
S. Vieluf, T. Hasija, M. Kuschel, C. Reinsberger, T. Loddenkemper, Expert Systems with Applications 234 (2023).

Geodesic-Based Relaxation For Deep Canonical Correlation Analysis
M. Kuschel, T. Marrinan, T. Hasija, in: 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), IEEE, 2023.

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

Multi-Task fMRI Data Fusion Using IVA and PARAFA
I. Lehmann, E. Acar, T. Hasija, M.A.B.S. Akhonda, V.D. Calhoun, P.J. Schreier, T. Adali, in: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 1466–1470.

Multi-Task fMRI Data Fusion Using IVA and PARAFA
I. Lehmann, E. Acar, T. Hasija, M.A.B.S. Akhonda, V.D. Calhoun, P. Schreier, T. Adali, in: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2022.

Determining the dimension and structure of the subspace correlated across multiple data sets
T. Hasija, T. Marrinan, C. Lameiro, P.J. Schreier, Signal Processing 176 (2020).

Multimodal approach towards understanding the changes in the autonomic nervous system induced by an ultramarathon.
S. Vieluf, V. Scheer, T. Hasija, P. Schreier, C. Reinsberger, Res Sports Med 28 (2020) 231–240.

Source Enumeration and Robust Voice Activity Detection in Wireless Acoustic Sensor Networks
T. Hasija, M. Gölz, M. Muma, P.J. Schreier, A.M. Zoubir, in: Proc. Asilomar Conf. on Signals, Systems, Computers, 2019.

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

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

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

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

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

Detecting the dimension of the subspace correlated across multiple data sets in the sample poor regime
T. Hasija, Y. Song, P.J. Schreier, D. Ramírez, in: Proc. IEEE Work. Stat. Signal Process., Palma de Mallorca, Spain, 2016.

Bootstrap-based detection of the number of signals correlated across multiple data sets
T. Hasija, Y. Song, P.J. Schreier, D. Ramírez, in: Proc. Asilomar Conf. Signals Syst. Computers, Pacific Grove, CA, USA, 2016.

Determining the number of signals correlated across multiple data sets for small sample support
Y. Song, T. Hasija, P.J. Schreier, D. Ramírez, in: Proc. Eur. Signal Process. Conf., Budapest, Hungary, 2016.