Kuschel, M., Hasija, T., &; Marrinan, T. (2024). Rademacher Complexity Regularization for Correlation-Based Multiview Representation Learning. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).https://doi.org/10.1109/icassp48485.2024.10446173 

Vieluf, S., Hasija, T., Kuschel, M., Reinsberger, C., &; Loddenkemper, T. (2023). Developing a deep canonical correlation-based technique for seizure prediction. Expert Systems with Applications, 234, Article 120986.https://doi.org/10.1016/j.eswa.2023.120986 

Kuschel, M., Marrinan, T., &; Hasija, T. (2023). Geodesic-Based Relaxation For Deep Canonical Correlation Analysis. 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP).https://doi.org/10.1109/mlsp55844.2023.10285937 

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

Lehmann, I., Acar, E., Hasija, T., Akhonda, M. A. B. S., Calhoun, V. D., Schreier, P. J., &; Adali, T. (2022). Multi-Task fMRI Data Fusion Using IVA and PARAFA. Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1466–1470.https://doi.org/10.1109/ICASSP43922.2022.9747662 

Lehmann, I., Acar, E., Hasija, T., Akhonda, M. A. B. S., Calhoun, V. D., Schreier, P., &; Adali, T. (2022). Multi-Task fMRI Data Fusion Using IVA and PARAFA. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).https://doi.org/10.1109/icassp43922.2022.9747662 

Hasija, T., Marrinan, T., Lameiro, C., &; Schreier, P. J. (2020). Determining the dimension and structure of the subspace correlated across multiple data sets. Signal Processing, 176.https://doi.org/10.1016/j.sigpro.2020.107613 

Vieluf, S., Scheer, V., Hasija, T., Schreier, P., &; Reinsberger, C. (2020). Multimodal approach towards understanding the changes in the autonomic nervous system induced by an ultramarathon. Res Sports Med, 28(2), 231–240.

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

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

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

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

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

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

Hasija, T., Song, Y., Schreier, P. J., &; Ramírez, D. (2016). Detecting the dimension of the subspace correlated across multiple data sets in the sample poor regime. Proc. IEEE Work. Stat. Signal Process.

Hasija, T., Song, Y., Schreier, P. J., &; Ramírez, D. (2016). Bootstrap-based detection of the number of signals correlated across multiple data sets. Proc. Asilomar Conf. Signals Syst. Computers.

Song, Y., Hasija, T., Schreier, P. J., &; Ramírez, D. (2016). Determining the number of signals correlated across multiple data sets for small sample support. Proc. Eur. Signal Process. Conf.