Welcome!

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

Research Interests

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: Healthcare, Bioinformatics, and Intelligent Systems
Model Design: Explainable, Interpretable, and Causal Models
Internet of Things

[Feb 2025] 1 paper accepted by CVPR 2025
[Jan 2025] 1 paper accepted by NAACL 2025
[Jan 2025] 2 papers accepted by WWW 2025 (preprint 1, preprint 2)
[Jan 2025] Workshop: Quantum Machine Learning for Communication Networks, ICCCN 2025
[Jan 2025] Joined the Editorial Board as Associate Editor of IEEE Transactions on Fuzzy Systems
[Dec 2024] Tutorial accepted on Quantum Machine Intelligence and Fuzzy Learning, FUZZ-IEEE 2025
[Dec 2024] 1 paper accepted by ICASSP 2025
[Dec 2024] 1 paper accepted by AAAI 2025
[Dec 2024] Special Session: Quantum Machine Learning Algorithms and Applications, IJCNN 2025
[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] Joined the Editorial Board as Associate Editor of Neural Networks, Elsevier
[Nov 2023] Joined the Editorial Board as Associate Editor of Alexandria Engineering Journal, Elsevier
[May 2023] 1 paper accepted by ACL 2023

Explore topic-wise publications in the Publication Section.
For a full list, visit Google Scholar.

Tiwari, P., Dehdashti, S., Safty, KH., Bruza, P., & Notzel, J. (2025). Enhancing Quantum Machine Learning: The Power of Non-Linear Optical Reproducing Kernels.
Yao, B., Tiwari, P., Li, Q. (2025). Self-supervised pre-trained neural network for quantum natural language processing. Neural Networks.
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.