Hasan Kurban
Computer & Data Scientist
About me
My research and teaching interests include machine learning (ML), data mining, big data, data science and artificial intelligence (AI). I theoretically work on data-centric ML/AL and also build ML systems over various domains such as material science , chemistry, bioinformatics, to help accelerate scientific discoveries. For example, we use ML to develop long standing traditional quantum modeling techniques, particularly Density-functional theory (DFT), a computational method developed in the 1970s, for modelling quantum mechanics that is among the most popular tools of the material scientists.
Interests
- Machine Learning
- Data Mining
- Data Science
- Data Analytics
- Artificial Intelligence
- Computer Science
- Big Data
- Computational Science
- Teaching and Learning
Education
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Ph.D., Computer Science, Sep 2017
Indiana University Bloomington, IN, USA
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M.Sc., Computer Science, May 2012
Indiana University Bloomington, IN, USA
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EAP, July 2010
University of Connecticut, CT, USA
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EAP, June 2009
Marmara University, Istanbul, Turkey
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B.Sc., Mathematics, June 2008
Inonu University, Malatya, Turkey
Projects
FEATURED PUBLICATIONS
Applying Data-Centric AI to Improve a Single-cell RNA-seq Pipeline
Applying Data-Centric AI to Improve a Single-cell RNA-seq Pipeline
A paired index structure for k-Nearest Neighbor Search Algorithms
A paired index structure for k-Nearest Neighbor Search Algorithms
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–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 using a Novel Machine Learning Framework and Minimal Empirical Data
Data-centric AI
Data Expressiveness and Its Use in Data-centric AI
Machine Learning Systems for Multi-Atoms Structures
CH3NH3PbI3 Perovskite Nanoparticles
Rare-class Learning
Rare-class Learning over Mg-Doped ZnO Nanoparticles
Atom Type Prediction
Predicting Atom Types in Different Temperatures
Artificial Intelligence Based Hospital Appointment System
Hospital Appointment System
Practical Data Science over Nanoparticles
Data Science
Deep Analysis of TiO2 Nanoparticles
DFTB calculations
R Package
Data Clustering with EM (DCEM) for Big Data, an R package
Structural Analysis
Density-functional tight-binding approach for the structural analysis and electronic structure of copper hydride metallic nanoparticles
Public Transportation Optimization
Using Data Analytics to Optimize Public Transportation on a College Campus
Iterative Machine Learning
A Novel Approach to Optimization of Iterative Machine Learning Algorithms
Clustering Big Data
An Expectation Maximization Algorithm for Big Data
Coral Reef Analysis
EMPLOYING SOFTWARE ENGINEERING PRINCIPLES TO CLIMATOLOGICAL DATASETS
Random Forest
Reduced random forest for big data using priority voting & dynamic data reduction
Studying the Milky Way
Studying the milky way galaxy using paraheap-k
Contact
- 700 N Woodlawn Ave, Bloomington, IN 47408