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Our novel multimodal approach to improve electronic structure calculations for nanoparticles is to be published in Machine Learning: Science and Technology
Happy to share that our latest research, titled ‘Multimodal Neural Network-Based Predictive Modeling of Nanoparticle Properties from Pure Compounds by Can Polat, Mustafa Kurban, and Hasan Kurban’, has been accepted for publication in Machine Learning: Science and Technology. Our novel approach marks a unique advancement at the intersection of materials science and multimodal approaches, offering…
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Extended abstract of our IEEE DSAA24 paper is now available
The extended abstract of our paper, “p-ClustVal: A Novel p-adic Approach for Enhanced Clustering of High-Dimensional scRNASeq Data” is available at IEEE Xplore (https://ieeexplore.ieee.org/document/10722799)
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Enhancing Cluster Discernment in High Dimensional Single Cell RNA Sequencing Data
Excited to share that our recent work on using alternate data representation for enhancing cluster discernment in high dimensional single cell RNA Sequencing data has been accepted at the 11th IEEE International Conference on Data Science & Analytics (DSAA 2024).
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Excited to share that our recent research at the intersection of Computer Vision and Materials Science has been accepted for publication in Computational Materials Science!
Thrilled to share that our latest research, titled ‘QuantumShellNet: Ground-State Eigenvalue Prediction of Materials Using Electronic Shell Structures and Fermionic Properties via Convolutions by Can Polat, Hasan Kurban, and Mustafa Kurban’, has been accepted for publication in Computational Materials Science. This study marks a significant advancement at the intersection of materials science and computer vision,…
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Our latest work at the intersection of Materials Science and Machine learning has been accepted to Computational Materials Science!
We are excited to announce that our latest research, titled “Enhancing the Electronic Properties of TiO₂ Nanoparticles through Carbon Doping: An Integrated DFTB and Computer Vision Approach by Mustafa Kurban, Can Polat, and Hasan Kurban”, has been accepted for publication in Computational Materials Science. This work represents an advancement at the intersection of materials science…
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Group Pic
Our group pic. From left, Mehmet, Parichit, Dr. Kurban, Mert, Hasan and Fahrettin. We thought it would be nice to take the picture before some people leave for their parent campus.
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Special Session: Advancing Materials Science with Data Science – IEEE DSAA 2024
We are glad to announce that we are organizing the special session-“Advancing Materials Science Through Data Science:Innovations, Applications and Challenges” at 11th IEEE, International Conference on Data Science & Analytics, 2024. Many thanks to all those who submitted their papers. We will see you all there 🙂 More Info: https://www.dsaa2024-specialsession-data-driven-material-science.com/homepage
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Accepted Paper: Enhancing K-Means with Data-Centric AI
Our paper – “What Data-Centric AI can Do for Kmeans: a Faster, Robust Kmeans-d” has been accepted at the Proceedings of the 41st International Conference on Machine Learning (ICML), Data-Centric Machine Learning Workshop (DMLR), to be held in Vienna, Austria.
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Our paper has been accepted to Machine Learning: Science and Technology
Our paper “An Extended de Bruijn Graph for Feature Engineering Over Biological Sequential Data” has been accepted to Machine Learning: Science and Technology (Impact Factor: 6.8).
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KIL team dinner at Yasemin Palace
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Kurban Intelligence Lab 