Graduate level · Concordia University, Algorithms & Complexity Lab, Department of Computer Science and Software Engineering · Montreal, Québec, Canada ·
Researching Algorithms Design & Analysis, Graph Theory, and Social Network Analysis
Working in the Algorithms & Complexity Lab
Under the supervision of Professor Hovhannes Harutyunyan
Date: Aug 2024 – Now
My key role consisted of:
Designed Spider, a graph community detection algorithm combining geodesic expansion, modularity-guided refinement, and greedy merge matching.
Benchmarked Spider against Louvain, Leiden, Infomap, CNM, and Label Propagation on nine real-world networks (34–8,035 vertices) and LFR benchmarks, achieving top F1 and ARI on Karate Club and Political Blogs and NMI 0.88 on Primary School, where flow- and propagation-based baselines collapse to near-trivial partitions.
Introduced Weighted Average Geodesic Distance Modularity (wGDM), a size-consistent, label-free partition quality measure.
Conducted the first systematic study of metric backbone sparsification with Leiden, achieving 14–71% edge reduction across seven networks while preserving — and on dense contact networks improving — detection quality (High School F1: 0.834 → 0.970).
Built a fully reproducible experimental pipeline with fixed random seeds, baseline implementations, and automated evaluation scripts.
Graduate level · University of Twente, Faculty of EE, Math and CS - FMT group: Formal Methods and Tools · Enschede, The Netherlands ·
Working and collaborating with the “Electrical Engineering, Mathematics and Computer Science” department of “University of Twente”.
Field of Research: Software Refactoring
Research Group: FMT group - Formal Methods and Tools
Date: Aug 2023 – March 2024
Supervisor: Dr. Iman Hemati Moghadam
My key role consisted of:
Implemented the KotlinCode2Text parser and integrated it into the RefDetect framework for automated refactoring detection.
Constructed two refactoring datasets used for empirical evaluation in the SANER 2024 study.
Improved analysis reliability and runtime through targeted debugging and algorithmic refinements.
Investigated LLM-based prompt engineering for cross-language code translation in refactoring mining.
Contributed to the XlateRefactor pipeline, evaluated across 241 commits and 71 repositories with ~2,000 validated refactorings in Java, Kotlin, and C, with all artifacts released publicly.
We have published one paper in the IEEE SANER 2024 Conference[1]. Our second paper, “XlateRefactor: A Language-Agnostic Pipeline for Cross-Language Refactoring Detection Using LLM-Based Code Translation”, is currently under review at JSME (major revision).
Undergraduate level · Vali-e-Asr University of Rafsanjan, Department of Computer Engineering · Rafsanjān, Kerman, Iran ·
Field of Research: Community Detection (Graph Algorithms)
Supervisor: Dr. Fahimeh Dabaghi-Zarandi
Department: “Computer Engineering” department of “Vali-e-Asr University of Rafsanjan”.
Date: Aug 2021 – March 2024
My key role consisted of:
Conducted a comprehensive review of prior work in graph-based community detection.
Designed and implemented CRLG, a randomized community detection framework leveraging both local and global network information.
Developed weighted probabilistic seeding and similarity-driven community assignment with heuristic community merging.
Implemented and evaluated the framework in MATLAB and Python, including validation, testing, and performance tuning.
Evaluated on real-world networks and GN/LFR benchmarks, achieving up to 10% improvement over LCDR, MOACO, Node2Vec-SC, NE-N2V, CDASS, and TS using NMI, modularity, and density metrics.
We have published one paper in the JNCA journal[1] (Q1), which has been cited 25+ times.