Dr. Hasan Kurban

Dr. Hasan Kurban

Computer & Data Scientist

About me

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.

Interests

  • Data Science, Data Mining, Machine Learning, Big Data, AI in Materials Science, Software Engineering

Education

  • Ph.D., Computer Science, Sep 2017

    Indiana University Bloomington, IN, USA

Projects

FEATURED PUBLICATIONS

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All Machine Learning Big Data Data Science Artificial Intelligence Data Mining Software Engineering Data Analytics
Impedance Spectroscopy

Impedance Spectroscopy

CRISP–Comprehensive Regression for Impedance Spectroscopy Prediction over ELF Regions using AI

Feature Engineering Over Unstructured Data

Feature Engineering Over Unstructured Data

Efficient Feature Engineering Over Unstructured Data for Use with Traditional AI Models

More Real Fantasy Leagues

More Real Fantasy Leagues

Making Fantasy Leagues More Real by Adding Team Chemistry

Geometric-k-means

Geometric-k-means

Geometric-k-means–A Novel, Exact, Unbounded Distance Calculation Reducing k-means

Sports Awards

Sports Awards

Are Sports Awards About Sports? Using AI to Find the Answer

Telescope Indexing for k-Nearest Neighbor Search Algorithms

Telescope Indexing for k-Nearest Neighbor Search Algorithms

tik-nn–Telescope Indexing for k-Nearest Neighbor Search Algorithms over High Dimensional Data & Large Data Sets

AutoML Regression Service for Data Analytics and Novel Data-centric Visualizations

AutoML Regression Service for Data Analytics and Novel Data-centric Visualizations

AReS–An AutoML Regression Service for Data Analytics and Novel Data-centric Visualizations

A Comprehensive Study of Ordinary Linear Regression in Python

A Comprehensive Study of Ordinary Linear Regression in Python

Are They What They Claim–A Comprehensive Study of Ordinary Linear Regression Among the Top Machine Learning Libraries in Python

Cooperative Model Framework with Minimal Viable Theoretical Data

Cooperative Model Framework with Minimal Viable Theoretical Data

An Efficient and Novel Approach for Predicting Kohn-Sham Total Energy–Bootstrapping a Cooperative Model Framework with Minimal Viable Theoretical Data

ccImpute algorithm to impute dropout events in the single-cell RNA-seq data

ccImpute algorithm to impute dropout events in the single-cell RNA-seq 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

Regeneration of Lithium-ion Battery Impedance

Regeneration of Lithium-ion Battery Impedance using a Novel Machine Learning Framework and Minimal Empirical Data

Data-centric AI

Data-centric AI

Data Expressiveness and Its Use in Data-centric AI

Machine Learning Systems for Multi-Atoms Structures

Machine Learning Systems for Multi-Atoms Structures

CH3NH3PbI3 Perovskite Nanoparticles

Rare-class Learning

Rare-class Learning

Rare-class Learning over Mg-Doped ZnO Nanoparticles

Atom Type Prediction

Atom Type Prediction

Predicting Atom Types in Different Temperatures

Deep Analysis of TiO2 Nanoparticles

Deep Analysis of TiO2 Nanoparticles

DFTB calculations

R Package

R Package

Data Clustering with EM (DCEM) for Big Data, an R package

Public Transportation Optimization

Public Transportation Optimization

Using Data Analytics to Optimize Public Transportation on a College Campus

Iterative Machine Learning

Iterative Machine Learning

A Novel Approach to Optimization of Iterative Machine Learning Algorithms

Clustering Big Data

Clustering Big Data

An Expectation Maximization Algorithm for Big Data

Random Forest

Random Forest

Reduced random forest for big data using priority voting & dynamic data reduction

Studying the Milky Way

Studying the Milky Way

Studying the milky way galaxy using paraheap-k

Popular Topics

Artificial intelligence AutoML B+ Trees Ball-k-means Big Data Big Data Mining Binary Search Tree Classification Clustering Core-Set Selection Cost-sensitive learning Data Analysis Data Analytics Data Mining Data Science Data-centric AI De Bruijn Graph DFTB Downstream Analysis Dropout Event Electrochemical Impedance Spectroscopy Enery Prediction Expectation Maximization Extreme Gradient Boosting feature-engineering Heap High Dimensional Data Imputation k-means Clustering Kernel methods Kohn-Sham Machine Learning Material Science Materials Science Model visualization Nanoparticles nba-fantasy-league Nearest Neighbors Next Generation Sequencing OLS Ordinary Least Squares Paired Indexing Prototype models Public Transportation Python python-flask R programming random forest Random Forests Regression scaling-statistics scRNA Single-cell Single-cell RNASeq Support vector machines Unstructured Data Unsupervised Learning Weighted Voting XGBoost

Contact

  • Texas A&M Engineering Building | Education City, PO Box 23874 | Doha, Qatar