Publications

(2024). Deep Temporal & Structural Embeddings for Unsupervised Anomaly Detection in Dynamic Graphs. IEEE Transactions on Neural Networks and Learning Systems (under-review).

(2024). TGPCNet--Achieving Simplicity in Doped Material Simulation via Multimodal Text-Guided Pure Compounds for Enhanced Solar Efficiency. In The 41st International Conference on Machine Learning (ICML), ML for Life and Material Science–From Theory to Industry Applications, Vienna, Austria, 2024 (under-review).

(2024). PlayoffsNet--Enhancing NBA Playoffs Prediction Through Engineered Features and Explainable Deep Learning. International Journal of Data Science and Analytics (under-review).

(2024). p-ClustVal--Enhancing Clustering and Valuation with a Novel p-adic Approach in High-Dimensional Genomics Data. International Journal of Data Science and Analytics (under-review).

(2024). What Data-Centric AI Can Do For k-means--a Faster, Robust k-means-d. In The 41st International Conference on Machine Learning (ICML), Data-centric Machine Learning Research (DMLR)–Datasets for Foundation Models, Vienna, Austria, 2024 (under-review).

(2024). Novel De Bruijn Graph Embeddings for Enhanced Time Series Forecasting. Machine Learning (under-review).

(2024). A Reinforcement Learning Approach to Effective Forecasting of Pediatric Hypoglycemia in Diabetes I Patients: an extended de Bruijn Graph. Scientific Reports (under-review).

(2024). ti-knn: Telescope Indexing for k-Nearest Neighbor Search Algorithms over High Dimensional Data & Large Data Sets. Data Mining and Knowledge Discovery (under-review).

(2024). An Extended de Bruijn Graph for Feature Engineering Over Biological Sequential Data. Machine Learning: Science and Technology (under-review).

(2024). A Noise-Adaptive Sequential Extreme Gradient Boosting Algorithm for Optimality Prediction of User Grouping in IM-OFDMA Systems. IEEE Transactions on Communications (under-review).

(2024). Leveraging DFTB and Computer Vision for Enhanced Electronic Structure Prediction of C-Doped TiO2 Nanoparticles--A Novel Machine Learning Approach. Computational Materials Science (under-review).

(2024). Geometric-k-means--An Unbounded, Accurate and Energy-Efficient k-means. Machine Learning (under-review).

(2024). QuantumShellNet--Ground-State Eigenvalue Prediction of Materials Using Electronic Shell Structures and Fermionic Properties via Convolutions. Computational Materials Science (under-review).

(2023). Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics. In IEEE International Conference on Big Data, Data-Centric AI, Sorrento, Italy.

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(2023). Are Sports Awards About Sports? Using AI to Find the Answer. In ECML/PKDD, 10th Workshop on Machine Learning and Data Mining for Sports Analytics, Turin, Italy.

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(2023). AReS: An AutoML Regression Service for Data Analytics and Novel Data-centric Visualizations. In KDD Undergraduate Consortium (KDD-UC), Long Beach, CA, USA.

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(2023). Are They What They Claim: A Comprehensive Study of Ordinary Linear Regression Among the Top Machine Learning Libraries in Python. In KDD Undergraduate Consortium (KDD-UC), Long Beach, CA, USA (accepted).

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(2023). State of Charge and Temperature-Dependent Impedance Spectra Regeneration of Lithium-ion Battery by Duplex Learning Modeling. Journal of Energy Storage.

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(2022). Data on Machine Learning regenerated Lithium-ion battery impedance. Data in Brief.

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(2022). Rapidly Predicting Kohn-Sham Total Energy Using Data-centric AI. Nature Scientific Reports.

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(2022). Regeneration of Lithium-ion Battery Impedance using a Novel Machine Learning Framework and Minimal Empirical Data. Journal of Energy Storage.

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(2021). Data Expressiveness and Its Use in Data-centric AI. In NeurIPS Data-Centric AI, DCAI'21.

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(2021). Rare-class Learning over Mg-Doped ZnO Nanoparticles. Chemical Physics.

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(2021). A DFT Study on Stability and Electronic Structure of AlN Nanotubes . Materials Today Communications.

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(2021). Predicting Atom Types of Anatase TiO2 Nanoparticles with Machine Learning. Key Engineering Materials.

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(2021). Measuring the Proximity of Medical Treatment Areas with Text Mining. European Journal of Science and Technology.

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(2020). Tailoring the structural properties and electronic structure of anatase, brookite and rutile phase TiO2 nanoparticles: DFTB calculations. Computational Materials Science.

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(2019). Study of Structural and Optoelectronic Properties of Hexagonal ZnO Nanoparticles. In Bilecik Seyh Edebali University Journal of Science.

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(2019). Controlling structural and electronic properties of ZnO NPs: Density-functional tight-binding method. Bilge International Journal of Science and Technology Research.

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(2019). Size Dependent Electronic Structure and Structural Properties of Cupric Oxide (CuO) NanoParticles. International Natural Science, Engineering and Material Technologies Conference.

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(2018). Using data analytics to optimize public transportation on a college campus. In IEEE international conference on data science and advanced analytics (DSAA).

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(2017). A novel approach to optimization of iterative machine learning algorithms: Over heap structure. In IEEE International Conference on Big Data (Big Data).

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(2017). Improving expectation maximization algorithm over stellar data. In IEEE International Conference on Big Data (Big Data).

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(2017). Case Study: Clustering Big Stellar Data with EM. In IEEE/ACM International Conference on Big Data Computing, Applications and Technologies.

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(2017). A NOVEL APPROACH TO OPTIMIZATION OF ITERATIVE MACHINE LEARNING ALGORITHMS: OVER HEAP STRUCTURE. Indiana University, ProQuest Dissertations Publishing.

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(2017). Using data to build a better EM: EM* for big data. International Journal of Data Science and Analytics.

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(2016). Em*: An em algorithm for big data. In The IEEE International Conference on Data Science and Advanced Analytics (DSAA).

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(2016). EMPLOYING SOFTWARE ENGINEERING PRINCIPLES TO ENHANCE MANAGEMENT OF CLIMATOLOGICAL DATASETS FOR CORAL REEF ANALYSIS. In The International Workshop on Climate Informatics (CI).

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(2015). Red-RF: Reduced random forest for big data using priority voting & dynamic data reduction. In IEEE International Congress on Big Data (IEEE BigData).

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(2014). A new set of Random Forests with varying dynamic data reduction and voting techniques. In The IEEE International Conference on Data Science and Advanced Analytics (DSAA).

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(2014). Studying the milky way galaxy using paraheap-k. IEEE Computer.

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