Dr. Hasan Kurban

Dr. Hasan Kurban

Computer & Data Scientist

About me

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!

Interests

  • Artificial Intelligence, Software Engineering, Computational Science

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
Time Series Forecasting

Time Series Forecasting

Novel De Bruijn Graph Embeddings for Enhanced Time Series Forecasting

QuantumShellNet

QuantumShellNet

Ground-State Eigenvalue Prediction of Materials Using Electronic Shell Structures and Fermionic Properties via Convolutions

Pediatric Hypoglycemia Forecasting

Pediatric Hypoglycemia Forecasting

A Reinforcement Learning Approach to Effective Forecasting of Pediatric Hypoglycemia in Diabetes I Patients–an extended de Bruijn Graph

Vision Based DFTB

Vision Based DFTB

Enhancing Photocatalytic Efficiency of TiO2 Nanoparticles through Carbon Doping–An Integrated DFTB and Computer Vision Approach

T2E

T2E

Text-To-Energy–Accelerating Quantum Chemistry Calculations through Enhanced Text-to-Vector Encoding and Orbital-Aware Multilayer Perceptron

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

An Extended de Bruijn Graph for Feature Engineering Over Unstructured Data

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 Carbon Doped Ti02 Classification Clustering Computational Science Computer Vision Core-Set Selection Cost-sensitive learning Data Analysis Data Analytics Data Mining Data Science Data-centric AI de Bruijn Graph DFT DFTB Diabetes Downstream Analysis Dropout Event Electrochemical Impedance Spectroscopy Enery Prediction Expectation Maximization Extreme Gradient Boosting feature-engineering Graph embeddings Heap High Dimensional Data Hypoglycemia 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 Physics Informed Machine Learning 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 Time series analysis TimesNet TSlib Unstructured Data Unsupervised Learning Weighted Voting XGBoost

Contact

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