About me

Hi! I’m Prayag, working as an Associate Professor in Machine Learning at Halmstad University, Sweden.

Research Interest

Quantum Machine Learning, Deep Learning, Graph Neural Networks, Federated Learning, Reinforcement Learning, Unsupervised Feature Selection
Natural Language Processing, Large Language Models, Computer Vision, Multimodal and Multisource Fusion
Applications in Healthcare, Bioinformatics, and Intelligent Systems
Explainable, Interpretable, and Causal Models
Internet of Things

[Oct 2024] 1 paper accepted by TMLR
[Sept 2024] 1 paper accepted by Nature Biomedical Innovations.
[Sept 2024] Hiring PhD Candidate in Multimodal Large Language Models (LLM) for Healthcare at Halmstad University, Sweden.
[Sept 2024] Promoted to Associate Professor (Docent) at Halmstad University, Sweden
[Jul 2024] 1 paper accepted by CIKM 2024
[Jul 2024] 2 papers accepted by TMLR (paper 1, paper 2)
[May 2024] 2 papers accepted by IEEE TNNLS & IEEE TFS
[Feb 2024] Promoted to Senior Lecturer at Halmstad University, Sweden
[Jan 2024] Editorial Board: Associate Editor of Neural Networks, Elsevier
[Nov 2023] Editorial Board: Associate Editor of Alexandria Engineering Journal, Elsevier
[May 2023] 1 paper accepted by ACL 2023

Topic-wise publication can be found in the Publication section, and full publication at Google Scholar.

Tiwari, P., Dehdashti, S., Safty, KH., Bruza, P., & Notzel, J. (2024). Enhancing Quantum Machine Learning: The Power of Non-Linear Optical Reproducing Kernels.
Tiwari, P., Dehdashti, S., Obeid, A. K., Marttinen, P., & Bruza, P. (2022). Kernel Method based on Non-Linear Coherent States in Quantum Feature Space. Journal of Physics A: Mathematical and Theoretical.
Tiwari, P., Zhang, L., Qu, Z., & Muhammad, G. (2024). Quantum Fuzzy Neural Network for Multimodal Sentiment and Sarcasm Detection. Information Fusion.
Qu, Z., Zhang, L., & Tiwari, P. (2024). Quantum Fuzzy Federated Learning for Privacy Protection in Intelligent Information Processing. IEEE Transactions on Fuzzy Systems.
Moreira, C., Tiwari, P., Pandey, H. M., Bruza, P., & Wichert, A. (2020). Quantum-like Influence Diagrams for Decision-Making. Neural Networks.

Qian, Y., Zheng, Z., Tiwari, P., Ding, Y., & Zou, Q. (2024). Multiple Kronecker RLS Fusion-Based Link Propagation for Drug-Side Effect Prediction. Transactions on Machine Learning Research (TMLR).
Wu, H., Liu, J., Jiang, T., Zou, Q., Qi, S., Cui, Z., Tiwari, P., & Ding, Y. (2024). AttentionMGT-DTA: A Multi-Modal Drug-Target Affinity Prediction Using Graph Transformer and Attention Mechanism. Neural Networks.

Liang, G., Tiwari, P., Nowaczyk, S., & Byttner, S. (2024). Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputation. CIKM 2024.
Liang, G., Tiwari, P., Nowaczyk, S., Byttner, S., & Fernandez, F. A. (2024). Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-Temporal Forecasting. IEEE Transactions on Neural Networks and Learning Systems.

Zhan, S., Mo, F., Tiwari, P., Wang, B., Li, Q., Nie, JY,. Simonsen, JG. (2024). Mixture of Latent Experts Using Tensor Products. Transactions on Machine Learning Research (TMLR).
Yu, F., Zhang, H., Tiwari, P., & Wang, B. (2024). Natural Language Reasoning: A Survey. ACM Computing Survey.
Wang, B., Xie, Q., Pei, J., Tiwari, P., Li, Z., & Fu, J. (2023). Pre-trained Language Models in Biomedical Domain: A Systematic Survey. ACM Computing Survey.
Gao, Y., Ji, S., Zhang, T., Tiwari, P., & Marttinen, P. (2022). Contextualized Graph Embeddings for Adverse Drug Event Detection. In Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD).
Ji, S., Zhang, T., Ansari, L., Fu, J., Tiwari, P., & Cambria, E. (2021). MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare. LREC 2022.

Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., & Marttinen, P. (2022). EEG Based Emotion Recognition: A Tutorial and Review. ACM Computing Surveys.
He, L., Niu, M., Tiwari, P., Marttinen, P., Su, R., Jiang, J., Guo, C., Wang, H., Ding, S., Wang, Pan, X., & Dang, W. (2022). Deep Learning for Depression Recognition with Audiovisual Cues: A Review. Information Fusion.
Saberi-Movahed, F., Rostami, M., Berahmand, K., Karami, S., Tiwari, P., Oussalah, M., & Band, S. S. (2022). Dual Regularized Unsupervised Feature Selection Based on Matrix Factorization and Minimum Redundancy with Application in Gene Selection. Knowledge-Based Systems.