PhD Candidate at Tufts University
Xinmeng is a graduating Ph.D. candidate in computer science at Tufts University. Her research interest is in Computational Biology, developing machine learning and deep learning models for feature selection, interaction prediction, time series regression, and multi-label regression on biochemistry data. She is recently working on metabolite annotation, enzyme-substrate interaction prediction, antibody sequence analysis, and drug concentration prediction tasks.
Develop an ensemble model based on graph neural network (GNN) and multi-layer perceptron (MLP) for muti-label regression. Enhance the model by applying attention and multi-tasking to address label dependency.
Design a general framework for recommender systems via multi-task learning. Develop enhanced neural network-based recommender systems to predict enzyme-substrate interactions.
Evaluate antibody features through machine learning methods, including random forest, SVM, and AdaBoost. Develop a novel pipeline to design antibody sequences using decision tree.
Develop a neural network model for time series regression on drug concentration. The model used for precise drug analysis to facilitate drug review procedures.