Baris Coskunuzer

PhD - Math
Princeton University - 2004

Identification of Molecular Compounds Targeting Bacterial Propionate Metabolism with Topological Machine Learning 2024 - Other

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

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

Breast Cancer Detection with Topological Machine Learning 2023 - Conference Paper

Minimal surfaces in ℍ2 × ℝ: Non-fillable curves 2023 - Journal Article

3D MRI Brain Tumor Diagnosis with Topological Descriptors 2023 - Conference Paper

Chainlet Orbits: Topological Address Embedding for the Bitcoin Blockchain 2023 - Journal Article

Histopathological Cancer Detection with Topological Signatures 2023 - Conference Paper

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

Innovative geometric deep learning models for onboard detection of anomalous events

2022/08–2024/02 NASA AIST Grant (co-I) (PIs: Y. Gel, Kyo Lee)

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.