Welcome!

Hi! I’m Prayag, an Associate Professor in Machine Learning at Halmstad University, Sweden. Visit Google Scholar (Citations: ∼11400, H-index: 50) for my research publications.

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

News

• [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

Selected Articles

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

Quantum ML

• 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.

Multimodal/Information Fusion

• 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.

Graph 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.

(Bio-)NLP

• 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.

Healthcare

• 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.