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. I'm drawn to graph research that is both scalable and interpretable, and to the connection between graph theory and real-world problems.
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.
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 →DSEG
Carrying overlapping-community ideas toward interpretable, graph-structured semantic control — exploratory.
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.
Background
University of Toronto — MEng, Mechanical & Industrial Engineering (Data Analytics & ML), 2025–2026. Research advised by Prof. Samin Aref.
University of Waterloo — B.C.S. Honours with Distinction; CS major, AI specialization, Computational Math minor, 2020–2025.