Dr. Weipeng Kuang
Research Foundation for SUNY, New York, USA

Biography:

PUBLICATION

• Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach, Translational Psychiatry, 11, 1– 10 
• Evaluating risk for alcohol use disorder: Polygenic risk scores and family history. Alcoholism: Clinical and Experimental Research, 46, 374– 383. 
• “Random Forest Classification of Alcohol Use Disorder Using EEG Source Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures”. Behav. Sci. 2020, 10, 62. \
• Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures”. Behav. Sci. 2022, 12, 128. 
• Statistical Nonparametric fMRI Maps in the Analysis of Response Inhibition in Abstinent Individuals with History of Alcohol Use Disorder. Behav. Sci. 2022, 12, 121. 
• Associations of parent–adolescent closeness with P3 amplitude, frontal theta, and binge drinking among offspring with high risk for alcohol use disorder. Alcohol: Clinical and Experimental Research, 00, 1– 13. 
• Predicting alcohol-related memory problems in older adults: A machine learning study with multi-domain features”. bioRxiv.. 2023,. 

EXPERIENCE

Research Foundation for SUNY, New York, NY
Data Scientist/Researcher (2019-present) 

– Advanced proficiency in machine learning techniques, including supervised and unsupervised learning.
– Data preprocessing and feature engineering for diverse modalities such as EEG, fMRI, and neuropsychological measures.
– Experience in longitudinal data analysis and predictive modeling.
– Expertise in the interpretation of results and their implications in the context of alcohol use disorder research.
– Strong statistical and computational skills for handling large datasets.
– Build API for users to provide easy access to Mongodb database
– Integrating up to 50 TB raw and processed data from multiple sites and grants

EDUCATION

New York University, New York, NY                                                                         
Master of Science, Information Systems (Jan 2018)

DeepLearing.AI 
– Certification of Neural Networks and Deep Learning 
– Certification of Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 
– Certification of Structuring Machine Learning projects