Before joining the Electrical and Computer Engineering at Texas A&M at Qatar, Dr. Hasan Kurban was a Visiting Associate Professor in the Computer Science Department and Data Science Program at Indiana University, Bloomington, where he received his Ph.D. in Computer Science with a minor in Statistics (2017) and where he is currently an Adjunct Professor. Dr. Kurban works in AI both foundational and applied. In foundations, he improves traditional model-centric algorithms by employing data-centric techniques resulting in vastly improved run times (while preserving accuracy) so that applications in big data are feasible. In applications, he has improved public transport, clustered the Milky Way, and now is focusing on materials science–efficiently predicting properties of nanoparticles–and energy storage–efficiently predicting impedance for battery state of health and state of charge.
Prospective PhD Students: I am currently seeking ambitious PhD students to join my research lab, focusing on generative AI. If you have strong academic credentials and demonstrate a keen interest in discovery and innovative research, you could be an excellent fit. To explore this opportunity further, please forward your CV to hasan.kurban[at]tamu[dot]edu.
Ph.D., Computer Science, Sep 2017
Indiana University Bloomington, IN, USA
CRISP–Comprehensive Regression for Impedance Spectroscopy Prediction over ELF Regions using AI
Efficient Feature Engineering Over Unstructured Data for Use with Traditional AI Models
tik-nn–Telescope Indexing for k-Nearest Neighbor Search Algorithms over High Dimensional Data & Large Data Sets
AReS–An AutoML Regression Service for Data Analytics and Novel Data-centric Visualizations
Are They What They Claim–A Comprehensive Study of Ordinary Linear Regression Among the Top Machine Learning Libraries in Python
An Efficient and Novel Approach for Predicting Kohn-Sham Total Energy–Bootstrapping a Cooperative Model Framework with Minimal Viable Theoretical Data
ccImpute–an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data
Regeneration of Lithium-ion Battery Impedance using a Novel Machine Learning Framework and Minimal Empirical Data
Using Data Analytics to Optimize Public Transportation on a College Campus
A Novel Approach to Optimization of Iterative Machine Learning Algorithms