I am a Postdoctoral Fellow at Mila, Québec AI Institute & School of Computer Science (SoCS) at McGill University. I received my PhD from the Department of Electrical and Computer Engineering (ECE) at McGill University in 2022, under the supervison of Prof. Zeljko Zilic and Prof. Reihaneh Rabbany.
Currently, I am working on dynamic graph representation learning, graph neural networks, and generally graph machine learning. My background also includes anomaly detection, network analysis, and data mining applied in real-world networks including cryptocurrency transaction networks. In a previous life, I was doing research on the operating systems and compilers for real-time embedded systems.
- Aug. 2023: The pre-print of our work Temporal Graph Benchmark for Machine Learning on Temporal Graphs is now available! Check out the , , , , and . Thanks to all amazing collaborators from Mila, McGill, Kumo.AI, Imperial College London, University of Montreal, Stanford, and Oxford.
- Feb. 2023 - Present: If you are interested to learn more about dynamic graph representation learning, consider joining our reading group!
- Dec. 2022: Temporal Graph Learning (TGL) Workshop was hosted at NeurIPS 2022.
Poursafaei, Farimah*, Shenyang Huang*, Kellin Pelrine, and Reihaneh Rabbany. "Towards Better Evaluation for Dynamic Link Prediction." In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, Dec. 2022.
Poursafaei, Farimah, Zeljko Zilic, and Reihaneh Rabbany. "A Strong Node Classification Baseline for Temporal Graphs." In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM), pp. 648-656. Society for Industrial and Applied Mathematics, 2022.
Poursafaei, Farimah, Reihaneh Rabbany, and Zeljko Zilic. "SigTran: Signature Vectors for Detecting Illicit Activities in Blockchain Transaction Networks." In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 27-39. Springer, Cham, 2021.
Poursafaei, Farimah, Ghaith Bany Hamad, and Zeljko Zilic. "Detecting Malicious Ethereum Entities via Application of Machine Learning Classification." In 2nd Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS), pp. 120-127. IEEE, 2020.
Poursafaei, Farimah, Sepideh Safari, Mohsen Ansari, Amir Yeganeh-Khaksar, Mohammad Salehi, and Alireza Ejlali. "Energy-and Reliability-Aware Task Replication in Safety-Critical Embedded Systems." In Proceedings of the 4th International of CSI Symposium on Real-Time and Embedded Systems and Technologies (RTEST), 2022.
Poursafaei, Farimah, Mostafa Bazzaz, Morteza Mohajjel Kafshdooz, and Alireza Ejlali. "Slack Clustering for Scheduling Frame-Based Tasks on Multicore Embedded Systems." Microelectronics Journal 81 (2018): 144-153.
Lévy, Sacha, Farimah Poursafaei, Kellin Pelrine, and Reihaneh Rabbany. "Active Keyword Selection to Track Evolving Topics on Twitter." Accepted in Utility-Driven Mining and Learning (UDML) at ICDM 2022. arXiv preprint arXiv:2209.11135.
Bazzaz, Mostafa, Ali Hoseinghorban, Farimah Poursafaei, and Alireza Ejlali. "High-Performance Predictable NVM-Based Instruction Memory for Real-Time Embedded Systems." IEEE Transactions on Emerging Topics in Computing, 2018: 441-455.
Ansari, Mohsen, Sepideh Safari, Farimah R. Poursafaei, Mohammad Salehi, and Alireza Ejlali. "AdDQ: Low-Energy Hardware Replication for Real-Time Systems through Adaptive Dual Queue Scheduling." The CSI Journal on Computer Science and Engineering (JCSE), 2017: 31-38.
Poursafaei, Farimah, Sepideh Safari, Mohsen Ansari, Mohammad Salehi, and Alireza Ejlali. "Offline Replication and Online Energy Management for Hard Real-Time Multicore Systems." In 2015 CSI Symposium on Real-Time and Embedded Systems and Technologies (RTEST), IEEE, 2015.