Dr Falih Febrinanto
ARC Grant-Funded Researcher A
School of Biological Sciences
College of Science
I am a postdoctoral researcher with the Wildlife Crime Research Hub (WCRH) at Adelaide University. I received a PhD in Information Technology from Federation University Australia, with a thesis focused on developing reliable graph neural networks for various anomaly detection tasks. In addition, my research focuses on developing machine learning architectures for real-world applications, including classification and prediction. During my time at the Hub, my work has involved evaluating AI tools for monitoring wildlife and environmental crime in natural habitats, such as detecting malicious events through sound, and in cyberspace, such as conducting surveillance of the online wildlife trade. A key focus of this research is to perform active and real-time detection. I also contribute to several interdisciplinary projects that use different data modalities, including audio, image, and graph data, to support automated analysis. My work has been published at several conferences and in journals, including Interspeech, WSDM, IEEE Transactions on Computational Social Systems, IEEE Computational Intelligence Magazine, and ACM Transactions on Intelligent Systems and Technology.
| Date | Position | Institution name |
|---|---|---|
| 2026 - ongoing | ARC Grant-Funded Researcher (A) | Adelaide University |
| 2025 - 2025 | Scholarly Teaching Fellow (A), Information Technology | Federation University Australia |
| 2023 - 2023 | Sessional Academic | RMIT University |
| 2021 - 2026 | Sessional Academic | Federation University Australia |
| Date | Type | Title | Institution Name | Country | Amount |
|---|---|---|---|---|---|
| 2022 | Scholarship | CSIRO's Data61 PhD Scholarship | CSIRO's Data61 | Australia | - |
| 2021 | Scholarship | PhD Tuition Fee Waiver Scholarship | Federation University Australia | Australia | - |
| Language | Competency |
|---|---|
| English | Can read, write, speak, understand spoken and peer review |
| Indonesian | Can read, write, speak, understand spoken and peer review |
| Date | Institution name | Country | Title |
|---|---|---|---|
| 2025 | Federation University Australia | Australia | PhD |
| 2021 | Federation University Australia | Australia | Master |
| 2018 | Universitas Brawijaya | Indonesia | Bachelor |
| Year | Citation |
|---|---|
| 2026 | Febrinanto, F. G., Moore, K., Thapa, C., Ma, J., & Saikrishna, V. (2026). SIGNL: A label-efficient audio deepfake detection system via spectral-temporal graph non-contrastive learning. Expert Systems with Applications, 307, 131056. |
| 2025 | Febrinanto, F. G., Simango, A., Xu, C., Zhou, J., Ma, J., Tyagi, S., & Xia, F. (2025). Refined causal graph structure learning via curvature for brain disease classification. Artificial Intelligence Review, 58(8), 21 pages. Scopus2 WoS2 |
| 2025 | Rabooki, S. F., Li, B., Febrinanto, F. G., Peng, C., Naghizade, E., Han, F., & Xia, F. (2025). GraphDART: Graph Distillation for Efficient Advanced Persistent Threat Detection. IEEE Transactions on Computational Social Systems, 1-13. Scopus1 |
| 2025 | Febrinanto, F. G., Moore, K., Thapa, C., Liu, M., Saikrishna, V., Ma, J., & Xia, F. (2025). Entropy Causal Graphs for Multivariate Time Series Anomaly Detection. ACM Transactions on Intelligent Systems and Technology, 16(6), 1-25. Scopus5 WoS4 |
| 2025 | Xia, F., Peng, C., Ren, J., Febrinanto, F. G., Luo, R., Saikrishna, V., . . . Kong, X. (2025). Graph Learning. FOUNDATIONS AND TRENDS IN SIGNAL PROCESSING, 19(4), 184 pages. WoS2 |
| 2024 | Yu, S., Xia, F., Wang, Y., Li, S., Febrinanto, F. G., & Chetty, M. (2024). PANDORA: Deep Graph Learning Based COVID-19 Infection Risk Level Forecasting. IEEE Transactions on Computational Social Systems, 11(1), 717-730. Scopus4 WoS3 |
| 2024 | Dafik., Mursyidah, I. L., Agustin, I. H., Baihaki, R. I., Febrinanto, F. G., Husain, S. K. B. S., & Sunder, R. (2024). On Rainbow Vertex Antimagic Coloring and Its Application on STGNN Time Series Forecasting on Subsidized Diesel Consumption. Iaeng International Journal of Applied Mathematics, 54(5), 984-996. Scopus7 |
| 2023 | Febrinanto, F. G., Xia, F., Moore, K., Thapa, C., & Aggarwal, C. (2023). Graph Lifelong Learning: A Survey. IEEE Computational Intelligence Magazine, 18(1), 32-51. Scopus66 WoS52 |
| 2022 | Wang, L., Yu, S., Febrinanto, F. G., Alqahtani, F., & El-Tobely, T. E. (2022). Fairness-Aware Predictive Graph Learning in Social Networks. Mathematics, 10(15), 2696. Scopus3 WoS1 |
| 2021 | Febrinanto, F. G., Dafik., & Nisviasari, R. (2021). The implementation of Blockchain framework in MOOCs to support a freedom of learning in Indonesia. Journal of Physics Conference Series, 1836(1), 012043. Scopus2 |
| 2019 | Febrinanto, F. G., Dewi, C., & Triwiratno, A. (2019). The Implementation of K-Means Algorithm as Image Segmenting Method in Identifying the Citrus Leaves Disease. Iop Conference Series Earth and Environmental Science, 243(1), 11 pages. Scopus21 WoS13 |
| Year | Citation |
|---|---|
| 2025 | Liu, M., Dong, Q., Wang, C., Cheng, X., Febrinanto, F. G., Hoshyar, A. N., & Xia, F. (2025). Motif-Induced Subgraph Generative Learning for Explainable Neurological Disorder Detection. In M. Gong, Y. Song, Y. S. Koh, W. Xiang, & D. Wang (Eds.), Lecture Notes in Computer Science (Vol. 15443 LNAI, pp. 376-389). SPRINGER-VERLAG SINGAPORE PTE LTD. DOI Scopus3 WoS1 |
| 2023 | Febrinanto, F. G., Liu, M., & Xia, F. (2023). Balanced Graph Structure Information for Brain Disease Detection. In S. Wu, W. Yang, M. B. Amin, B. H. Kang, & G. Xu (Eds.), Lecture Notes in Computer Science (Vol. 14317 LNAI, pp. 134-143). SPRINGER-VERLAG SINGAPORE PTE LTD. DOI Scopus9 WoS5 |
| Year | Citation |
|---|---|
| 2025 | Febrinanto, F. G., Moore, K., Thapa, C., Ma, J., Saikrishna, V., & Xia, F. (2025). Rehearsal with Auxiliary-Informed Sampling for Audio Deepfake Detection. In Proceedings of the Annual Conference of the International Speech Communication Association Interspeech (pp. 5358-5362). ISCA. DOI Scopus1 |
| 2023 | Febrinanto, F. G. (2023). Efficient Graph Learning for Anomaly Detection Systems. In Wsdm 2023 Proceedings of the 16th ACM International Conference on Web Search and Data Mining (pp. 1222-1223). SINGAPORE: ASSOC COMPUTING MACHINERY. DOI Scopus3 WoS2 |
| 2022 | Zhang, C., Febrinanto, F., Liu, M., Kong, X., Zhang, D., & Islam, S. M. N. (2022). Attractiveness based conference ranking. In Proceedings of the ACM Symposium on Applied Computing (pp. 803-806). ELECTR NETWORK: ASSOC COMPUTING MACHINERY. DOI WoS1 |
| 2021 | Hou, M., Ren, J., Febrinanto, F., Shehzad, A., & Xia, F. (2021). Cross Network Representation Matching with Outliers. In B. Xue, Y. S. Koh, & M. Pechenizkiy (Eds.), IEEE International Conference on Data Mining Workshops Icdmw Vol. 2021-December (pp. 951-958). ELECTR NETWORK: IEEE. DOI Scopus3 WoS2 |
| 2021 | Feng, Z., Hou, M., Liu, H., Liu, M., Kaur, A., Febrinanto, F. G., & Zhao, W. (2021). SmartColor: Automatic Web Color Scheme Generation Based on Deep Learning. In M. Alsmirat, A. Almaaitah, Y. Jararweh, & J. Lloret (Eds.), 2021 12th International Conference on Information and Communication Systems Icics 2021 (pp. 285-290). ELECTR NETWORK: IEEE. DOI Scopus3 WoS2 |
| Year | Citation |
|---|---|
| 2026 | Febrinanto, F., McGowan, S., Maher, J., & Cassey, P. (2026). Deep Learning for Combating Wildlife and Environmental Crime: A Survey. DOI |
| 2024 | Liu, M., Dong, Q., Wang, C., Cheng, X., Febrinanto, F. G., Hoshyar, A. N., & Xia, F. (2024). Motif-induced Subgraph Generative Learning for Explainable Neurological Disorder Detection. DOI |
- System Modelling (ITECH2002)
- Agile Coding (ITECH2306)
- Mobile Development Fundamentals (GPSIT2000)
- Foundation of Programming (ITECH1400)
- Data Visualization (ITECH3102)
- Network Architecture and Design (ITECH2301)
- Business Analytics and Decision Support (ITECH3101)
- Dynamic Web Development (ITECH3108), Mobile Device Programming (ITECH3107)
- Practical Data Science with Python (COSC2670/COSC2738)
Connect With Me
External Profiles
