Hasan Kurban

Orcid: 0000-0003-3142-2866

According to our database1, Hasan Kurban authored at least 30 papers between 2014 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
Stress-Testing Multimodal Foundation Models for Crystallographic Reasoning.
CoRR, June, 2025

Theorem-of-Thought: A Multi-Agent Framework for Abductive, Deductive, and Inductive Reasoning in Language Models.
CoRR, June, 2025

Beyond Atomic Geometry Representations in Materials Science: A Human-in-the-Loop Multimodal Framework.
CoRR, June, 2025

PhysicsNeRF: Physics-Guided 3D Reconstruction from Sparse Views.
CoRR, May, 2025

Multivariate de Bruijn Graphs: A Symbolic Graph Framework for Time Series Forecasting.
CoRR, May, 2025

xChemAgents: Agentic AI for Explainable Quantum Chemistry.
CoRR, May, 2025

Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding.
CoRR, May, 2025

Exploring Various Sequential Learning Methods for Deformation History Modeling.
CoRR, April, 2025

A Noise-Adaptive Machine Learning Framework for Optimizing User Grouping in Dynamic IM-OFDMA Systems.
IEEE Trans. Commun., March, 2025

HalluVerse25: Fine-grained Multilingual Benchmark Dataset for LLM Hallucinations.
CoRR, March, 2025

SINdex: Semantic INconsistency Index for Hallucination Detection in LLMs.
CoRR, March, 2025

SAFE: A Sparse Autoencoder-Based Framework for Robust Query Enrichment and Hallucination Mitigation in LLMs.
CoRR, March, 2025

Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs.
IEEE Open J. Comput. Soc., 2025

A Novel Discrete Time Series Representation With De Bruijn Graphs for Enhanced Forecasting Using TimesNet.
IEEE Access, 2025

Exploring Various Sequential Learning Methods for Deformation History Modeling.
Proceedings of the Engineering Applications of Neural Networks, 2025

2024
An extended de Bruijn graph for feature engineering over biological sequential data.
Mach. Learn. Sci. Technol., 2024

$p$-ClustVal: A Novel $p$-Adic Approach for Enhanced Clustering of High-Dimensional scRNASeq Data (Extended Abstract).
Proceedings of the 11th IEEE International Conference on Data Science and Advanced Analytics, 2024

A Novel Discrete Time Series Representation with De Bruijn Graphs for Enhanced Forecasting Using TimesNet (Extended Abstract).
Proceedings of the 11th IEEE International Conference on Data Science and Advanced Analytics, 2024

2023
Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics.
Proceedings of the IEEE International Conference on Big Data, 2023

2022
DCEM: An R package for clustering big data via data-centric modification of Expectation Maximization.
SoftwareX, 2022

ccImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data.
BMC Bioinform., 2022

2018
Using Data Analytics to Optimize Public Transportation on a College Campus.
Proceedings of the 5th IEEE International Conference on Data Science and Advanced Analytics, 2018

2017
Using data to build a better EM: EM* for big data.
Int. J. Data Sci. Anal., 2017

Improving expectation maximization algorithm over stellar data.
Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

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

Case Study: Clustering Big Stellar Data with EM.
Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, 2017

2016
EM*: An EM Algorithm for Big Data.
Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics, 2016

2015
Red-RF: Reduced Random Forest for Big Data Using Priority Voting & Dynamic Data Reduction.
Proceedings of the 2015 IEEE International Congress on Big Data, New York City, NY, USA, June 27, 2015

2014
Studying the Milky Way Galaxy Using ParaHeap-k.
Computer, 2014

A new set of Random Forests with varying dynamic data reduction and voting techniques.
Proceedings of the International Conference on Data Science and Advanced Analytics, 2014


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