Saikrishna, Vidya (Dr) - Research

Publications

  1. 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.
  2. 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).  
  3. 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.
  4. Kaur, A., Hoshyar, A. N., Saikrishna, V., Firmin, S., Xia, F. (2024). Deepfake Video Detection: Challenges and Opportunities. Artificial Intelligence Review 159(57).
  5. 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.
  6. 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.
  7. 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 Deep Outdated Fact Detection in Knowledge Graphs.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.
  8. 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.
  9. 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.
  10. 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).
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. Saikrishna, V., & Ray, S. (2013). Improved Approximate Multiple-Pattern String Matching using Consecutive N-Grams. International Journal of Computer Applications, 81(2).
  19. 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.
  20. 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.
  21. 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.
  22. 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)