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Uncovering the Potential of Federated Learning: Addressing Algorithmic and Data-driven Challenges under Privacy Restrictions
(2023-12-15)
Federated learning is a groundbreaking distributed machine learning paradigm that allows for the collaborative training of models across various entities without directly sharing sensitive data, ensuring privacy and ...
Machine learning assisted molecular simulations / mass spectrometry data analysis and rational design of antibacterial Co3O4 nanowires flagella
(2023-05-12)
In chapter one, inspired by the recent work from Noé and coworkers on the development of machine learning based implicit solvent model for the simulation of solvated peptides [Chen et al., J. Chem. Phys. 155, 084101 (2021)], ...
Novel Computer-Aided Diagnosis Schemes for Radiological Image Analysis
(2022-05-13)
The computer-aided diagnosis (CAD) scheme is a powerful tool in assisting clinicians (e.g., radiologists) to interpret medical images more accurately and efficiently. In developing high-performing CAD schemes, classic ...
Applying novel machine learning technology to optimize computer-aided detection and diagnosis of medical images
(2021-05-14)
The purpose of developing Computer-Aided Detection (CAD) schemes is to assist physicians (i.e., radiologists) in interpreting medical imaging findings and reducing inter-reader variability more accurately. In developing ...
Optimization of deepwater channel seismic reservoir characterization using seismic attributes and machine learning
(2023-12-15)
Accurate subsurface reservoir mapping is essential for resource exploration. In uncalibrated basins, seismic data, often limited by resolution, frequency, quality, etc., algorithms become the primary information source due ...
Development of polymer gel systems for lost circulation treatment and wellbore strengthening
(2021-05-07)
Lost circulation is a frequent problem and a significant contributor to the non-productive time (NPT) in the drilling operation. Field reports and experimental studies have revealed that conventional solutions are doomed ...
Developing novel quantitative imaging analysis schemes based machine learning for cancer research
(2021-05-14)
The computer-aided detection (CAD) scheme is a developing technology in the medical imaging field, and it attracted extensive research interest in recent years. In this dissertation, I investigated the feasibility of ...
Machine learning for the subsurface characterization at core, well, and reservoir scales
(2020-05-08)
The development of machine learning techniques and the digitization of the subsurface geophysical/petrophysical measurements provides a new opportunity for the industries focusing on exploration and extraction of subsurface ...
Integrated characterization of tight siliciclastic reservoirs: examples from the Cretaceous Burro Canyon Formation, Colorado, and Mississippian Meramec Strata, Oklahoma
(2021-05-14)
Integration of multiscale data sources for reservoir characterization becomes problematic and challenging due to the collected information variable resolution. Core and well data provide high vertical resolution to evaluate ...
Developing and Applying CAD-generated Image Markers to Assist Disease Diagnosis and Prognosis Prediction
(2022-05-13)
Developing computer-aided detection and/or diagnosis (CAD) schemes has been an active research topic in medical imaging informatics (MII) with promising results in assisting clinicians in making better diagnostic and/or ...