Graduate Researcher & Teaching Assistant @ Concordia University
Algorithms & Complexity Lab · Montréal, QC, Canada
I'm a thesis-based Master's student in Computer Science at Concordia University, working in the Algorithms & Complexity Lab under Prof. Hovhannes Harutyunyan. My thesis, Geodesic-Compact Community Detection and Quality Assessment in Social Networks, is expected to be completed in 2027.
My work sits at the intersection of Theoretical Computer Science, Graph Theory, Combinatorial Optimization, and Computational Social Science. I design, benchmark, and publish algorithms that uncover meaningful structure in social, citation, and synthetic networks.
I've also supported 15+ courses and accumulated 1,650+ teaching hours across tutorials, labs, marking, Programmer on Duty support, and TA leadership.
"Design algorithms. Mine graphs. Understand networks."
- Design & Analysis of Algorithms — combinatorial, approximation, randomized, and online algorithms
- Graph Theory & Graph Mining — community detection, graph algorithms, and network structure
- Social Network Analysis — community quality, metric backbone sparsification, and complex networks
- Machine Learning on Graphs — GNNs, scalable training strategies, and network embeddings
- Designed Spider, a community detection algorithm combining geodesic expansion, modularity-guided refinement, and greedy merge matching
- Introduced Weighted Average Geodesic Distance Modularity (wGDM), a size-consistent, label-free partition-quality measure
- Achieved 14–71% edge reduction across seven networks while preserving or improving community detection quality
- Built a reproducible evaluation pipeline with fixed random seeds, baseline implementations, and automated analysis
| Year | Paper | Venue |
|---|---|---|
| 2026 | Spider Community Detection: Seeded Geodesic Expansion with Modularity-Guided Refinement and Greedy Merge Matching (with H. Harutyunyan) | Computers |
| 2025 | From Dense Graphs to Meaningful Communities: Assessing Community Quality Using Geodesic Distance Modularity on Metric Backbone-Sparsified Networks (with H. Harutyunyan) | IEEE SNAMS 2025 |
| 2024 | Extending Refactoring Detection to Kotlin: A Dataset and Comparative Study (with I. H. Moghadam et al.) | IEEE SANER 2024 |
| 2022 | Community Detection in Complex Networks Based on an Improved Random Algorithm Using Local and Global Network Information (with F. Dabaghi-Zarandi) | Journal of Network and Computer Applications |
Programming Languages
ML & Data Science
Tools & Databases
- 🏆 Dean’s Excellence Award for Excellence in Teaching by a Teaching Assistant — Concordia University (2026)
- 🏅 Concordia Conference and Exposition Allowance (2025)
- 🥇 DRW Graduate Scholarship in Computer Science — Concordia University & DRW (2024)
- 🎓 Concordia Merit Scholarship — Concordia University, School of Graduate Studies (2024)
- 🔬 Financial Research Support (FRS) — Gina Cody School of Engineering and Computer Science (2024)
- 🌟 Top Researcher Award — Kerman Province (2023)
- 🎖️ Distinguished Student Award — Vali-e-Asr University (2023)
- 🕸️ Scalable Community Detection with GNNs — GCN and GraphSAGE training strategies for large graphs
- 🌊 Minimum-Cost Flow Algorithms — Experimental comparison of four network-flow optimization algorithms
- 🎮 Uncertainty — Open-source Unity space-shooter built with the Null References team
- 📺 Koolest TV Launcher — A fast, ad-free Android TV and Google TV home screen with the Glass design, iOS style look
- 🧲 Flickbar — Snap, resize and move macOS windows with trackpad gestures on their title bars
