Baris Coskunuzer

PhD - Math
Princeton University - 2004

Topology-Aware Deep Models for Skin Lesion Classification 2026 - Book Chapter

Breast Cancer Detection with Topological Deep Learning 2025 - Other

Diagnosis of Blood Diseases and Disorders with Topological Deep Learning 2025 - Other

Chainlet Orbits: Topological Address Embedding for Blockchain 2025 - Conference Paper

MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs 2025 - Data Set

ToFi-ML: Retinal Image Screening with Topological Machine Learning 2024 - Book Chapter

Towards Neural Scaling Laws for Foundation Models on Temporal Graphs 2024 - Data Set

3D MRI Brain Tumor Diagnosis with Topological Descriptors 2024 - Book Chapter

Distribution Network Resilience Enhancement with Topological Neural Networks

2022/12–2025/12 NSF-DMS-AMPS Research Grant (co-PI Jie Zhang)

Minimal Surfaces in Hyperbolic 3-Manifolds

2022/08–2025/07 NSF-DMS Geometric Analysis Research Grant

ATD: Predictive Anomaly Detection for Spatio-Temporal Data with MultiPersistence

2023/08–2026/07 NSF-DMS ATD Grant

Minimal Surfaces in 3-manifolds

2018/08–2023/07 Simons Collaboration Grant

Math Approach May Make Drug Discovery More Effective, Efficient

Dr. Baris Coskunuzer, professor of mathematical sciences at UT Dallas, and his colleagues developed an approach based on topological data analysis to screen thousands of possible drug candidates virtually and narrow the compound candidates considerably to those that are most fit for laboratory and clinical testing.