Parsa Kamalipour

RA & TA & Master of Computer Science at Concordia University @ Algorithms & Complexity Lab.

Research Experience

Research Interests:
  • Design & Analysis of Algorithms
  • Graph Theory & its applications
  • Combinatorial Algorithms
  • Social Networks Analysis
  • Computational Social Science
  • Complex Networks
  • Data Structures & Databases
  • Graph Mining
  • Applied Machine Learning

Graduate Research Assistant

Graduate level, Concordia University, Algorithms & Complexity Lab, Department of Computer Science and Software Engineering, Montreal, Québec, Canada, [August, 17, 2024]

  • 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 on 14 real-world networks (up to 8,035 nodes / 183,663 edges) against Leiden, Louvain, and Infomap, achieving 8–15% improvements in NMI, modularity, and F1-score.
    • Applied metric backbone sparsification, achieving an average 65% edge reduction, and introduced Weighted Average Geodesic Distance Modularity (wGDM) to normalize and balance GDM for local community quality evaluation.
    • Built a fully reproducible experimental pipeline with fixed random seeds, baseline implementations, and automated evaluation scripts.
  • We have published two papers so far: the IEEE SNAMS 2025 Conference[1] and the Computers Journal[2]

Remote Research Assistant

Graduate level, University of Twente, Faculty of EE, Math and CS - FMT group: Formal Methods and Tools, Enschede, The Netherlands, [August, 01, 2023]

  • 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.
  • We have published one paper in the IEEE SANER 2024 Conference[1], and we have submitted our 2nd paper in the ??? [2].

Undergraduate Research Assistant

Undergraduate level, Vali-e-Asr University of Rafsanjan, Department of Computer Engineering, Rafsanjān, Kerman, Iran, [August, 01, 2021]

  • 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].