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Dr. Hasan Kurban

Dr. Hasan Kurban

Research Assistant Professor

Office Number: 246S
Office Phone: +974.4423.0416

Before joining 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 (USA) 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. He also builds new data-centric algorithms to discover and predict patterns particularly in time series data. For each work he releases code. For example, Dr. Kurban has improved expectation-maximization (EM) clustering called EM* receiving several awards. His CRAN R package DCEM for EM* has >25K downloads (6/24/2023).

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. His most recent work, “Rapidly predicting Kohn–Sham total energy using data-centric AI,” Kurban et al. in Nature Scientific Reports is among the first papers demonstrating how data-centric AI (DCAI) can be used to effectively improve materials science. Another recent work, “State of charge and temperature-dependent impedance spectra regeneration of lithium-ion battery by duplex learning modeling” in Journal of Energy Storage shows how data-centric AI can effectively build an experimental space. More information about Dr. Kurban can be found at his website:

Research Interests:

  • Data Science
  • Machine Learning
  • Big Data
  • Applied Artificial Intelligence in Materials Science
  • Data Mining
  • Software Engineering


Ph.D. in Computer Science and minor in Statistics, Indiana University (USA), September 2017

M.Sc. in Computer Science, Indiana University (USA), May 2012 

Intensive Certificate Program, University of Connecticut (USA), July 2010 

Intensive Certificate Program, Marmara University (Turkey), June 2009

B.Sc. in Mathematics, Inonu University (Turkey), June 2008


  • Honorable Mention Paper Award, IEEE International Conference on Data Science and Advanced Analytics (DSAA’16), Montreal, Canada, 2016.
  • Best Poster Award, IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (UCC/BDCAT), Austin, Texas, USA, 2017.
  • Turkish National Ministry of Education Scholarship (all tuitions, fees, and a stipend) Scholarship awarded to high-achieving Turkish university graduates enabling them to pursue graduate study and research in top-ranked universities abroad, 2009 - 2017.
  • Computer Science Graduate Fellowship, Indiana University, Bloomington, August 2010 - May 2012.


  • Temiz, S., Erol, S., Kurban, H., & Dalkilic, M. M. (2023). State of charge and temperature-dependent impedance spectra regeneration of lithium-ion battery by duplex learning modeling.Journal of Energy Storage64, 107085.
  • Kurban, H., Kurban, M., & Dalkilic, M. M. (2022). Rapidly predicting Kohn–Sham total energy using data-centric AI.Scientific Reports12(1), 14403.
  • Temiz, S., Kurban, H., Erol, S., & Dalkilic, M. M. (2022). Data on Machine Learning regenerated Lithium-ion battery impedance.Data in Brief45, 108698.
  • Malec, M., Kurban, H., & Dalkilic, M. (2022). CcImpute: An accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data.BMC bioinformatics23(1), 291.
  • Temiz, S., Kurban, H., Erol, S., & Dalkilic, M. M. (2022). Regeneration of Lithium-ion battery impedance using a novel machine learning framework and minimal empirical data.Journal of Energy Storage52, 105022.
  • Sharma, P., Kurban, H., & Dalkilic, M. (2022). DCEM: An R package for clustering big data via data-centric modification of Expectation Maximization.SoftwareX17, 100944.
  • Kurban, H., Sharma, P., & Dalkilic, M. (2021, December). Data Expressiveness and Its Use in Data-centric AI. In of 35th Conf. on Neural Information Processing Systems (NeurIPS 2021).
  • Kurban, H., & Kurban, M. (2021). Building Machine Learning systems for multi-atoms structures: CH3NH3PbI3 perovskite nanoparticles.Computational Materials Science195, 110490.
  • Kurban, H., Kurban, M., Sharma, P., & Dalkilic, M. M. (2021). Predicting Atom Types of Anatase TiO2 Nanoparticles with Machine Learning. InKey Engineering Materials (Vol. 880, pp. 89-94). Trans Tech Publications Ltd.
  • Kurban, H., & Kurban, M. (2021). Rare-class learning over Mg-doped ZnO nanoparticles.Chemical Physics546, 111159.
  • Muz, İ., Kurban, H., & Kurban, M. (2021). A DFT study on stability and electronic structure of AlN nanotubes.Materials Today Communications26, 102118.
  • Kurban, H., Alaei, S., & Kurban, M. (2021). Effect of Mg content on electronic structure, optical and structural properties of amorphous ZnO nanoparticles: A DFTB study.Journal of Non-Crystalline Solids560, 120726.
  • Kurban, H. (2021). Atom classification with machine learning and correlations among physical properties of ZnO nanoparticle.Chemical Physics545, 111143.
  • Kurban, H. (2021). Metin Madenciliği ile Tıbbi Tedavi Alanlarının Yakınlıklarının Ölçülmesi.Avrupa Bilim ve Teknoloji Dergisi, (21), 518-526.
  • Kurban, H. (2020). Practical Data Science: Examining the Correlations between Structural and Electronic Properties of Different Phases of TiO2 Nanoparticles. Selçuk-Teknik Dergisi,19(4), 1-9.
  • Kurban, H., Dalkilic, M., Temiz, S., & Kurban, M. (2020). Tailoring the structural properties and electronic structure of anatase, brookite and rutile phase TiO2 nanoparticles: DFTB calculations.Computational Materials Science183, 109843.
  • Kurban, H., & Kurban, M. (2019). Study of Structural and Optoelectronic Properties of Hexagonal ZnO Nanoparticles.Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi6(2), 243-250.
  • Kurban, M., Kurban, H., & Dalkilic, M. (2019). Controlling structural and electronic properties of ZnO NPs: Density-functional tight-binding method.Bilge International Journal of Science and Technology Research3, 35-39.
  • Kurban, H., Kurban, M., & Dalkılıç, M. (2019). Density-functional tight-binding approach for the structural analysis and electronic structure of copper hydride metallic nanoparticles.Materials Today Communications21, 100648.
  • Zimmer, K., Kurban, H., Jenne, M., Keating, L., Maull, P., & Dalkilic, M. (2018, October). Using data analytics to optimize public transportation on a college campus. In2018 IEEE 5th international conference on data science and advanced analytics (DSAA) (pp. 460-469).
  • Kurban, H., Kockan, C., Jenne, M., & Dalkilic, M. M. (2017, December). Case Study: Clustering Big Stellar Data with EM. InProceedings of the fourth IEEE/ACM international conference on big data computing, applications and technologies (pp. 271-272).
  • Kurban, H., & Dalkilic, M. M. (2017, December). A novel approach to optimization of iterative machine learning algorithms: over heap structure. In2017 IEEE International Conference on Big Data (Big Data) (pp. 102-109).
  • Kurban, H., Jenne, M., & Dalkilic, M. M. (2017). Using data to build a better EM: EM* for big data.International Journal of Data Science and Analytics4, 83-97.
  • Kurban, H., Kockan, C., Jenne, M., & Dalkilic, M. M. (2017). Improving expectation maximization algorithm over stellar data. In2017 IEEE International Conference on Big Data (Big Data) (pp. 2559-2568).
  • Kurban, H., Jenne, M., & Dalkilic, M. M. (2016, October). EM: An EM Algorithm for Big Data. In2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)(pp. 312-320).
  • Jenne, M., Zimmerman, A., Kurban, H., Johnson, C., & Dalkilic, M. M (2016). Employing Software Engineering Principles to Enhance Management of Climatological Datasets for Coral Reef Analysis. The 6th International Workshop on Climate Informatics (CI).
  • Mohsen, H., Kurban, H., Zimmer, K., Jenne, M., & Dalkilic, M. M. (2015, June). Red-rf: Reduced random forest for big data using priority voting & dynamic data reduction. In2015 IEEE International Congress on Big Data (pp. 118-125).
  • Mohsen, H., Kurban, H., Jenne, M., & Dalkilic, M. (2014, October). A new set of Random Forests with varying dynamic data reduction and voting techniques. In2014 International Conference on Data Science and Advanced Analytics (DSAA)(pp. 399-405).
  • Jenne, M., Boberg, O., Kurban, H., & Dalkilic, M. (2014). Studying the milky way galaxy using paraheap-k.Computer47(9), 26-33.