I build fast, scalable algorithms and systems for graphs. My flagship work, Highway, detects overlapping communities in networks up to 1.13 million nodes using sparse backbones — and is now integrated into cdlib, a mainstream community-detection library.
I'm currently finishing my MEng at the University of Toronto and applying to CS/EECS PhD programs for Fall 2027. My research interests lie in scalable graph algorithms, interpretable structural representations, and methods for making model decisions more traceable — as well as in the connection between graph theory and real-world problems.
I would also like to express my sincere gratitude to Prof. Samin Aref for his mentorship and guidance throughout my graduate studies. His support, encouragement, and trust have played a central role in my development as a researcher and in shaping the direction of my work.
News
- Aug 2026 Highway talk at Toronto Metropolitan University.
- Aug 2026 Journal extension in preparation for SNAM.
- Aug 2026 Open-sourcing HELM.
- Now
- Jul 2026 Highway to appear at ASONAM 2026 (Springer proceedings).accepted
- Jul 2026 Highway integrated into cdlib.
- Jun 2026 Presented at WAW 2026 — talk recording.
- May 2026 Poster at the MIE Graduate Research Symposium, Hart House Great Hall.
- Mar 2026 Featured in a University of Toronto MIE article on the Data Analytics & ML MEng.
Publications
Overlapping Network Community Detection Using Sparse Backbones
ASONAM 2026 (Springer proceedings) · Accepted
A four-step sparse-backbone method. Evaluated on ~3,000 synthetic graphs and three real SNAP networks (up to 1.13M nodes / 2.99M edges); the only method to finish all three within 300s (7.34× faster on the largest instance).
Triad: Suppressing Structural Degeneracy in Overlapping Community Detection
WAW 2026 · Presentation
A QCP formulation with node/edge/community constraints that explicitly suppresses structural degeneracy — the predecessor method that led to Highway.
Selected Research
One arc — structural reliability → scalability → interpretability.
Triad
Structurally reliable overlapping assignments through node/edge/community constraints — precise but hard to scale.
Read the case study → FlagshipHighway
Scalable overlapping community detection through sparse structural backbones — to 1.13M nodes.
Read the case study → FutureDSEG
Carrying overlapping-community ideas toward interpretable, graph-structured semantic control — exploratory.
Read the vision →Selected System
A full-stack AI wealth operating system
A six-layer data platform with a governed data spine and multiple clients, grown out of a finance-ML course project. Evidence of engineering maturity rather than a research thrust.
Talks & Presentations
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Highway & TRIAD — WAW 2026Conference talk, Workshop on Algorithms and Models for the Web Graph · in person, Jun 18 2026 · Recording
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Highway — Toronto Metropolitan UniversityTalk · Aug 12 2026
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Highway — MIE Graduate Research SymposiumPoster · Hart House Great Hall, May 4 2026 · selected for the MIE hallway display · Poster
Formal Training
Degrees, selected relevant coursework, and grades.
M.Eng., Mechanical & Industrial Engineering — Data Analytics & Machine Learning · 2025–2026
Research advised by Prof. Samin Aref.
- Foundations of Data Analytics and Machine LearningA+
- Data Science Methods and Statistical LearningA+
- Introduction to Machine LearningA+
- Cloud-Based Data AnalyticsA+
- Positive Psychology for EngineersA+
- Artificial Intelligence in FinanceIn progress
- Introduction to Reinforcement LearningIn progress
- MEng Research ProjectA+
B.C.S. — Computer Science Major · Artificial Intelligence Specialization · Computational Mathematics Minor · 2020–2025