My doctoral research focuses on the nexus of applied statistics and deep learning with differential privacy. In the current Ph.D. project funded by National Science Foundation(NSF), I am working to develop a deep learning model which can objectively quantify clinical trials' outcomes and cognitive assessment. In another recent Ph.D. project funded by the National Institute of Health(NIH), I have looked into how pregnancy(yes/no) effect the DNAm of the pregnancy and birth using survival analysis/linear regression. I aim to build state-of-the-art statistical machine learning and deep learning models to solve issues from the internet of things(IoT), social science, learning science, public health, and consumer behavior.
Research Themes:
Statistical ML, Deep Learning with Differential Privacy(DP), Others:
Research papers:
AI and Cognitive Diagnostic Modeling:
Research papers:
Applied Statistics, Bayesian Inference, and Biostatistics:
Research papers:
Consumer Behavior, and AI in Marketing:
Research papers:
2022
2021
2020
2019
2018
2017 & Beyond:
Research Themes:
Statistical ML, Deep Learning with Differential Privacy(DP), Others:
Research papers:
AI and Cognitive Diagnostic Modeling:
Research papers:
Applied Statistics, Bayesian Inference, and Biostatistics:
Research papers:
Consumer Behavior, and AI in Marketing:
Research papers:
2022
2021
2020
2019
2018
2017 & Beyond:
