Danny D'Agostino

According to our database, Danny D'Agostino authored at least 9 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Disparities in clinical studies of AI enabled applications from a global perspective.

Rui Yang, Sabarinath Vinod Nair, Yuhe Ke, Danny D'Agostino, Mingxuan Liu, Yilin Ning, Nan Liu

npj Digit. Medicine, 2024

Learning active subspaces and discovering important features with Gaussian radial basis functions neural networks.

Danny D'Agostino, Ilija Ilievski, Christine Annette Shoemaker

Neural Networks, 2024

Generative models for anomaly detection and design-space dimensionality reduction in shape optimization.

Danny D'Agostino

Eng. Appl. Artif. Intell., 2024

Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis.

Siqi Li, Xin Li, Kunyu Yu, Di Miao, Mingcheng Zhu, Mengying Yan, Yuhe Ke, Danny D'Agostino, Yilin Ning, Qiming Wu, Ziwen Wang, Yuqing Shang, Molei Liu, Chuan Hong, Nan Liu

CoRR, 2024

2023

Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches.

Siqi Li, Di Miao, Qiming Wu, Chuan Hong, Danny D'Agostino, Xin Li, Yilin Ning, Yuqing Shang, Huazhu Fu, Marcus Eng Hock Ong, Hamed Haddadi, Nan Liu

CoRR, 2023

2021

A lipschitzian global optimization algorithm and machine learning for fluid dynamics.

Danny D'Agostino

PhD thesis, 2021

Recurrent-type Neural Networks for Real-time Short-term Prediction of Ship Motions in High Sea State.

Danny D'Agostino, Andrea Serani, Frederick Stern, Matteo Diez

CoRR, 2021

2018

Augmented Design-Space Exploration by Nonlinear Dimensionality Reduction Methods.

Danny D'Agostino, Andrea Serani, Emilio Fortunato Campana, Matteo Diez

Proceedings of the Machine Learning, Optimization, and Data Science, 2018

2017

Nonlinear Methods for Design-Space Dimensionality Reduction in Shape Optimization.

Danny D'Agostino, Andrea Serani, Emilio Fortunato Campana, Matteo Diez

Proceedings of the Machine Learning, Optimization, and Big Data, 2017