Dr. Kurban holds the position of Assistant Professor of Electrical and Computer Engineering at Texas A&M University at Qatar and serves as an Adjunct Associate Professor of Computer Science and Data Science at Indiana University Bloomington, where he earned his Ph.D. in Computer Science with a minor in Statistics in September 2017. Dr. Kurban’s research encompasses a wide range of topics including data-centric AI, deep learning, graph theory, and large-scale data analytics. His pioneering work has been applied across diverse fields such as computational materials science, computational biology, sports analytics, public transportation, and astronomy. Noteworthy recent projects include pioneering AI and data-driven methodologies in materials science and engineering. Dr. Kurban is dedicated to transforming traditional science through AI innovations, leveraging technology to tackle complex problems.
IEEE DSAA'24 Conference: We are excited to announce a special research session entitled “Advancing Materials Science through Data Science: Innovations, Applications, and Challenges,” which will be held at the the 11th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2024) this October in San Diego. We encourage researchers and practitioners to submit their novel work and contribute to this cutting-edge discussion. For more details and to participate, please visit our session’s dedicated webpage: Advancing Materials Science through Data Science: Innovations, Applications, and Challenges. Join us in shaping the future of materials science with the power of data science!
Ph.D., Computer Science, Sep 2017
Indiana University Bloomington, IN, USA
FEATURED PUBLICATIONS
Ground-State Eigenvalue Prediction of Materials Using Electronic Shell Structures and Fermionic Properties via Convolutions
A Reinforcement Learning Approach to Effective Forecasting of Pediatric Hypoglycemia in Diabetes I Patients–an extended de Bruijn Graph
Enhancing Photocatalytic Efficiency of TiO2 Nanoparticles through Carbon Doping–An Integrated DFTB and Computer Vision Approach
Text-To-Energy–Accelerating Quantum Chemistry Calculations through Enhanced Text-to-Vector Encoding and Orbital-Aware Multilayer Perceptron
CRISP–Comprehensive Regression for Impedance Spectroscopy Prediction over ELF Regions using AI
An Extended de Bruijn Graph for Feature Engineering Over Unstructured Data
Making Fantasy Leagues More Real by Adding Team Chemistry
Geometric-k-means–A Novel, Exact, Unbounded Distance Calculation Reducing k-means
Are Sports Awards About Sports? Using AI to Find the Answer
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
Data Expressiveness and Its Use in Data-centric AI
CH3NH3PbI3 Perovskite Nanoparticles
Rare-class Learning over Mg-Doped ZnO Nanoparticles
Predicting Atom Types in Different Temperatures
DFTB calculations
Data Clustering with EM (DCEM) for Big Data, an R package
Using Data Analytics to Optimize Public Transportation on a College Campus
A Novel Approach to Optimization of Iterative Machine Learning Algorithms
An Expectation Maximization Algorithm for Big Data
Reduced random forest for big data using priority voting & dynamic data reduction
Studying the milky way galaxy using paraheap-k