About me

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

Research Interest

• Artificial Intelligence
  • Quantum Machine Learning, Deep Learning, Graph Neural Networks, Federated Learning, Reinforcement Learning.
  • Natural Language Processing (LLMs), Computer Vision, Multimodal and Multisource Learning
  • Applications in Healthcare, Bioinformatics, and Intelligent Systems
  • Interpretable, Explainable, and Causal models.
• Quantum-based models
  • Quantum Classifiers, Quantum Kernels, Quantum Neural Networks, Quantum Clustering, Quantum Generative Models
  • Quantum-like structures for judgment and decision-making
  • Quantum Computing applications.
• Internet of Things

News and Media appearance

• [Feb 2024] Promoted to Associate Professor (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] Media appearance: Aalto University Details Findings in Computing (Eeg Based Emotion Recognition: a Tutorial and Review) by Computer News Today.
• [May 2023] Media appearance: Reports from Halmstad University Add New Data to Findings in Cancer Research (Explainable Quantum Clustering Method To Model Medical Data) by NewsRx Women’s Health Daily.
• [April 2023] Media appearance: Investigators at Aalto University Report Findings in COVID-19 (Iomt: a Covid-19 Healthcare System Driven By Federated Learning and Blockchain) by NewsRx COVID-19 Daily.
• [April 2023] Media appearance: Findings from Aalto University Broaden Understanding of Machine Learning (Federated-learning Based Privacy Preservation and Fraud-enabled Blockchain Iomt System for Healthcare) by Health Policy and Law Daily.
• [January 2023] Media appearance: New Data from Aalto University Illuminate Findings in Technology (Fully Homomorphic Enabled Secure Task Offloading and Scheduling System for Transport Applications) by Tech Daily News
• [December 2022] Media appearance: Findings from Aalto University Update Knowledge of Mathematics (C-loss Based Higher Order Fuzzy Inference Systems for Identifying Dna N4-methylcytosine Sites) by Math Daily News
• [November 2022] Elevation to the grade of IEEE Senior member.
• [November 2022] 2022 Best Paper Award: Cross-modality paired-images generation and augmentation for RGB-infrared person re-identification, published in Neural Networks, volume 128, pp. 294-304, August 2020.
• [October 2022] Top 2% of scientists around the world for the year (2022) based on Elsevier database.

Call for Papers

• [Feb 2024] Call for papers “Zero Trust Edge and Federated Learning for Consumer Internet of Things” is open for submission in IEEE Transactions on Consumer Electronics. (I.F.- 4.3)
• [May 2023] Call for papers “Transformer Models for Multi-source Visual Fusion and Understanding” is open for submission in Information Fusion, Elsevier. (I.F.- 17.564)
• [June 2023] Call for papers “Human-oriented 3D vision in the Metaverse” is open for submission in Image and Vision Computing, Elsevier. (I.F.- 3.86)
• [June 2023] Call for papers “Visual Processing Techniques in Harsh Environments” is open for submission in Journal of Visual Communication and Image Representation, Elsevier. (I.F.- 2.6)

Selected Articles

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

• Quantum
  • 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.
  • Moreira, C., Tiwari, P., Pandey, H. M., Bruza, P., & Wichert, A. (2020). Quantum-like influence diagrams for decision-making. Neural Networks.
  • Upretty S, Tiwari, P., Dehdashti S, Fell L, Song D, Bruza P, & Melucci M (2020). Quantum-like structure in multidimensional relevance judgements, European Conference on Information Retrieval (ECIR).
  • Tiwari, P., & Melucci, M. (2019). Towards a quantum-inspired binary classifier. IEEE Access.
• Multimodal Fusion
  • Zhang, Y., Wang, J., Liu, Y., Rong, L., Zheng, Q., Song, D., Tiwari, P., & Qin, J. (2023). A Multitask learning model for multimodal sarcasm, sentiment and emotion recognition in conversations. Information Fusion.
  • 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
  • Xie, Q., Tiwari, P., & Ananiadou, S. (2023). Knowledge-enhanced Graph Topic Transformer for Explainable Biomedical Text Summarization. IEEE Journal of Biomedical and Health Informatics.
  • Liang, G., Kintak, U., Ning, X., Tiwari, P., Nowaczyk, S., & Kumar, N. (2023). Semantics-aware Dynamic Graph Convolutional Network for Traffic Flow Forecasting. IEEE Transactions on Vehicular Technology. [Source Code]
• (Bio-)NLP
  • Dou, M., Tang, J., Tiwari, P., Ding, Y., & Guo, F. (2024). Drug-drug interaction relation extraction based on deep learning: A review. 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.
  • Tiwari, P., Zhu, H., & Pandey, H. M. (2021). DAPath: Distance-aware knowledge graph reasoning based on deep reinforcement learning. Neural Networks.
• 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, 256, 109884.

Useful Links

Google Scholar (Citations: ∼6600, h-index: 37)