Saikrishna, Vidya (Dr) - Research

Publications

  1. Feng Xia, Ciyuan Peng, Jing Ren, Falih Gozi Febrinanto, Renqiang Luo, Vidya Saikrishna, Shuo Yu and Xiangjie Kong (2025), "Graph Learning", Foundations and Trends® in Signal Processing: Vol. 19: No. 4, pp 371-551. http://dx.doi.org/10.1561/2000000137
  2. Saikrishna V., Dasari N., Lim S., “Reimagining Assessment for AI-Integrated Learning: A Transnational Case of Chinese Students in Australian Higher Education”. Paper presented at the International Conference on Emerging Technologies and Education (ICETE 2026), Melbourne, Australia. (Proceedings forthcoming.)
  3. Dasari N., Saikrishna V., Lim S., “Enhancing Learning Outcomes Through Student-Created Scenarios and Responsible GenAI Integration in Project-Based Learning”. Paper presented at the International Conference on Emerging Technologies and Education (ICETE 2026), Melbourne, Australia. (Proceedings forthcoming.)
  4. Lim S., Saikrishna V., Dasari N., “Automated Marking Using Local Large Language Models and Open Source Tools”. Paper presented at the International Conference on Emerging Technologies and Education (ICETE 2026), Melbourne, Australia. (Proceedings forthcoming.)
  5. Liu, H., Zhang, Y., Saikrishna, V., Tian, Q., & Zheng, K. (2024). Prompt Learning for Multi-Label Code Smell Detection: A Promising Approach. arXiv preprint arXiv:2402.10398.
  6. Febrinanto, F., Kristen, M., Chandra, T., Ma, J., Saikrishna, V., Xia, F. (2024). Spatio-temporal Vision Graph Non-Contrastive Learning for Audio Deepfake Detection. Submitted to International Conference on Data Mining (ICDM 2024).  
  7. Tang, T., Hou, M., Yu, S., Cai, Z., Han, Z., Oatley, G., & Saikrishna, V. (2024). Fedgst: An efficient federated graph neural network for spatio-temporal poi recommendation. ACM Transactions on Sensor Networks.
  8. Kaur, A., Hoshyar, A. N., Saikrishna, V., Firmin, S., Xia, F. (2024). Deepfake Video Detection: Challenges and Opportunities. Artificial Intelligence Review 159(57).
  9. Hou, M., Xia, F., Chen, X., Saikrishna, V., & Chen, H. (2023). Adaptive Spatio-Temporal Graph Learning for Bus Station Profiling. ACM Transactions on Spatial Algorithms and Systems.
  10. Liu, Z., Zhou, H., Xia, F., Shen, G., Saikrishna, V., He, X., ... & Kong, X. (2023, October). Subgraph Federated Learning with Global Graph Reconstruction. In Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (pp. 158-173). Singapore: Springer Nature Singapore.
  11. Tu, H., Yu, S., Saikrishna, V., Xia, F., & Verspoor, K. (2023, December). Deep Outdated Fact Detection in Knowledge Graphs. In 2023 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 1443-1452). IEEE.
  12. Febrinanto, F. G., Moore, K., Thapa, C., Liu, M., Saikrishna, V., Ma, J., & Xia, F. (2023). Entropy Causal Graphs for Multivariate Time Series Anomaly Detection. arXiv preprint arXiv:2312.09478.
  13. Sun, K., Xia, F., Liu, J., Xu, B., Saikrishna, V., & Aggarwal, C. C. (2022). Attributed Graph Force Learning. IEEE Transactions on Neural Networks and Learning Systems.
  14. Peng, C., Xia, F., Saikrishna, V., & Liu, H. (2022, November). Physics-Informed Graph Learning. In 2022 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 732-739).
  15. Sun, K., Li, W., Saikrishna, V., Chadhar, M., & Xia, F. (2022). COVID-19 datasets: A brief overview. Computer Science and Information Systems, 19(3), 1115-1132.
  16. Godly, C. J., Balasubramanian, V., Stranieri, A., Saikrishna, V., Mohammed, R. S., & Chackappan, G. (2022, November). Deep learning model to empower student engagement in online synchronous learning environment. In 2022 IEEE 19th India Council International Conference (INDICON) (pp. 1-6). IEEE.
  17. Zhang, D., Zhang, M., Guo, T., Peng, C., Saikrishna, V., & Xia, F. (2021, July). In Your Face: Sentiment Analysis of Metaphor with Facial Expressive Features. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
  18. Saikrishna, V., Dowe, D. L., & Ray, S. (2022). MML learning and inference of hierarchical Probabilistic Finite State Machines. In Applied Data Analytics-Principles and Applications (pp. 291-325). River Publishers.
  19. Saikrishna, V., & Ray, S. (2019, May). MML Inference of Hierarchical Probabilistic Finite State Machine. In 2019 Cybersecurity and Cyberforensics Conference (CCC) (pp. 78-84). IEEE.
  20. Saikrishna, V., Dowe, D. L., & Ray, S. (2016, December). Statistical compression-based models for text classification. In 2016 Fifth International Conference on Eco-friendly Computing and Communication Systems (ICECCS) (pp. 1-6). IEEE.
  21. Saikrishna, V., Dowe, D. L., & Ray, S. (2015, January). MML inference of Finite State Automata for probabilistic spam detection. In 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR) (pp. 1-6). IEEE.
  22. Saikrishna, V., & Ray, S. (2013). Improved Approximate Multiple-Pattern String Matching using Consecutive N-Grams. International Journal of Computer Applications, 81(2).
  23. Saikrishna, V., Rasool, A., & Khare, N. (2013). Spam Filtering through Multiple Pattern Bit Parallel String Matching Combining Shift AND & OR. International Journal of Computer Applications, 61(5), 40-45.
  24. Saikrishna, V., Rasool, A., & Khare, N. (2012). String matching and its applications in diversified fields. International Journal of Computer Science Issues (IJCSI), 9(1), 219.
  25. Saikrishna, V., Rasool, A., & Khare, N. (2012). Time Efficient String Matching Solution for Single and Multiple Pattern using Bit Parallelism. International Journal of Computer Applications, 46(6), 15-20.
  26. A reduced Candidate set Generation Algorithm for frequent item set with occurrence of items 100% in transaction database, in proceedings of National Conference in Bansal Institute of Science and Technology, Bhopal, India.

Grants / awards

  • Received Best Poster Award for the work titled “Explainable Graph Learning for Psychiatric Disorder Detection”. Work presented at 37th Australasian Joint Conference on Artificial Intelligence (AJCAI) 2024
  • Best paper award at IEEE Cybersecurity and Cyber-forensics Conference in 2019 held at Melbourne Institute of Technology for the paper titled “MML Inference of Hierarchical Probabilistic Finite State Machine”.
  • Received Postgraduate Publications Award (PPA) from Monash University after successful submission of PhD
  • Federation University “Living Values Award” for Excellence, Australia
  • Commendation of Excellence from Federation University Information Engineering Institute, China
  • Letter of Commendation for Student Evaluation Learning and Teaching (SELT) for ITECH3108(Dynamic Web Development) 2024
  • Student nomination for excellence in teaching at Monash University for FIT1031 (Computer and Networks)
  • Student voice commendation 2019 award for “on-campus educators of the year 2019” at Central Queensland University, Melbourne Campus, Australia

Supervised projects

  • Supervised Masters students for Industry projects at Federation University
  • Supervised Masters students at Melbourne Institute of Technology as a group for Capstone projects negotiating with the industry partners

Research higher degree completions

  • Falih Febrinanto, PhD – Completed in 2025 (Federation University, Australia) – Thesis Titled “Reliable Graph Neural Networks for Anomaly Detection”
  • Mujie Liu – PhD (under supervision)
  • Cinthia Godly – PhD (under supervision)
  • Dinh Thang Nguyen – PhD (under supervision)