PATENTS

US Patent (Granted (to be published))
“Improving Multi-Modal Machine Learning Models Through Dimensionality Standardization“
Inventors: Onur Boyar, Indra Priyadarsini, Seiji Takeda, Lisa Hamada (IBM)

JP Patent JP2025-97645A
“Latent-space–based discovery method for inorganic crystal structures using generative models and Bayesian optimization”
(JP official title: 「探索方法、情報処理装置、プログラム」)
Inventors: Shunsuke Tonogai, Tomoya Itakura (DENSO Corp.), Ichiro Takeuchi, Onur Boyar, Yuji Tanaka, Yanheng GU (Nagoya University)

PUBLICATIONS

• Boyar, O., Hanada, H., Takeuchi, I. (2025), “Conditional Latent Space Molecular Scaffold Optimization for Accelerated Molecular Design”, AI Robot Driving Science Symposium.

• Boyar, O., Priyadarsini, I., Takeda, S., Hamada, L. (2025), “LLM-Fusion: LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery”, Association for the Advancement of Artificial Intelligence (AAAI) 2025 Workshop on AI to Accelerate Science and Engineering (to appear)

• Hamada, L., Priyadarsini, I., Takeda, S., Boyar, O. (2025), “TDiMS : A Topological Distance based Intra-Molecular Substructure Descriptor for Improved Machine Learning Predictions”, Association for the Advancement of Artificial Intelligence (AAAI) 2025 Workshop on AI to Accelerate Science and Engineering (to appear)

• Boyar, O., Hanada, H., Takeuchi, I. (2024), “Conditional Latent Space Molecular Scaffold Optimization for Accelerated Molecular Design”, arXiv:2411.01423

• Boyar, O. (2024), “De Novo Molecular and Crystal Design with Latent Space Bayesian Optimization”, Association for the Advancement of Artificial Intelligence (AAAI) 2025 Doctoral Consortium (to appear)

• Takeda, S, Priyadarsini I., Hamada, L., Shinohara H., Boyar, O, Brazil, E., Soares, E., Cipcigan, F, Braines, D. (2024), “An Open Multi-Modal Foundation Model for Materials and Chemistry”, MRS Fall Meeting 2024.

• Boyar, O., Gu, Y., Tanaka. Y., Tonogai, S., Itakura, T., Takeuchi, I. (2024), “Crystal-LSBO: Automated Design of De Novo Crystals with Latent Space Bayesian Optimization”, arXiv:2405.17881

• Boyar, O., Takeuchi, I. (2024) “Latent Space Bayesian Optimization with Latent Data Augmentation for Enhanced Exploration”, Neural Computation.

• Boyar, O., Takeuchi, I., (2023) “Latent Space Bayesian Optimization with Enhanced Exploration”, International Workshop on Pattern Recognition in Healthcare Analytics.

• Boyar, O., Iwata, K., Hanada, H., Takeuchi, I. (2023), “Exploration of novel compounds based on deep generative models”, AI Robot Driving Science Symposium.

• Boyar, O., Iwata, K., Hanada, H., Takeuchi, I. (2023) “Sample-Efficient De Novo Chemical Design with Latent Reconstruction-Aware Variational Autoencoder”, Japanese Society in Artificial Intelligence (JSAI)

• Boyar, O. (2022) “Variance Reduction in Markov Chain Monte Carlo Algorithms”, Boğaziçi University.

• Uysal, E., Bilici, S.., Zaza, S., ÅN Ozgen.c, M., Boyar, O. (2021) “Exploring the Limits of Data Augmentation in Medical Image Segmentation,” arXiv:2105.09365.

• Boyar, O., HÅNormann, W. (2020) “VarRedOpt: A Framework for Variance Reduction,” An R Library published on CRAN.

• Boyar, O., Badur, B., Metin, B. (2019), ”On applying autoencoders to solve intrusion detection problems,” 6. International Management Information Systems Conference, ˙Istanbul, Turkey, 09-12 October.

• Boyar, O., ÅN Ozen, E., Metin, B. (2018) “Detection of Denial-of-Service Attacks with SNMP/RMON,” 22nd IEEE International Conference on Intelligent Engineering Systems, Las Palmas de Gran Canaria, Spain.

 

Optimal Data Augmentation for Medical Image Segmentation, 2021

Recompy: A Framework for Recommendation, 2020