Astrit Tola
Postdoctoral Scholar · Department of Mathematics · Florida State University
I work at the intersection of Topological Data Analysis, Machine Learning, and AI, with applications spanning drug discovery, biological networks, and cybersecurity. I lead TopoML&AI @ FSU — a research group designing topology-aware AI methods that make modern ML more interpretable, robust, and grounded in mathematical structure.
Previously, I completed my Ph.D. in Mathematics at the University of Texas at Dallas (2020–2025), advised by Prof. Baris Coskunuzer. My dissertation, "Graph Representation Learning with Topological Data Analysis," developed new topological methods for learning expressive, interpretable representations of graphs. I currently work with Asst. Prof. Malbor Asllani at FSU.
Education
Ph.D. in Mathematics · University of Texas at Dallas
Dissertation: Graph Representation Learning with Topological Data Analysis
Advisor: Prof. Baris Coskunuzer
B.S. in Mathematics · Middle East Technical University (METU)
Ankara, Turkey.
News
New Grant ReliaQuest Innovation Challenge Award
Excited to share that our proposal, "TopoCorr-KG: Topology-Aware Retrieval and Correlation Across Disparate Cybersecurity Knowledge Graphs", has been selected for the ReliaQuest Innovation Challenge at Florida State University. Looking forward to collaborating with Co-PI Malbor Asllani, with Ece Karacam (GTA) and Sebastian Powers (UTA) joining the team.
TopoFormer accepted at ICLR 2026
Our paper "Topology Meets Attention for Graph Learning" was accepted at ICLR 2026.
TopER presented at NeurIPS 2025
Poster on topological embeddings for graphs at NeurIPS 2025, San Diego.
Research Highlights
Topological Data Analysis
Persistent homology, Euler characteristic curves, and topological summaries as inductive biases for modern ML.
Graph Classification & Embeddings
Topology-aware Transformers and embeddings for graph-level tasks (TopoFormer, TopER).
Node Classification
Spatial and contextual node representations for node-level prediction tasks (SCNode).
Link Prediction
Strong, interpretable baselines and dual-perspective methods for link prediction in graphs (PROXI, DuoLink).
Drug Discovery & Biology
Topological machine learning for identifying molecular compounds, e.g., compounds targeting bacterial propionate metabolism.
AI for Cybersecurity
Retrieval and correlation across cybersecurity knowledge graphs (TopoCorr-KG, ReliaQuest Innovation Challenge).