Daifeng Wang

Daifeng Wang is an Assistant Professor in the Department of Biomedical Informatics at Stony Brook University, with affiliations in Electrical and Computer Engineering, Computer Science, and Applied Mathematics and Statistics. He holds a Ph.D. in Electrical and Computer Engineering from the University of Texas at Austin and completed postdoctoral training at Yale University (2012–2016). His research focuses on developing interpretable machine learning methods and bioinformatics tools to analyze multi-omics data for understanding functional genomics in brain disorders and cancers. Key contributions include predicting regulatory networks in the brain, linking genetic variants to psychiatric disorders like schizophrenia, and advancing deep-learning models for genomic data integration.

Recent work, published in Science (2018), demonstrates a 6-fold improvement in disease prediction using a conditional Deep Boltzmann Machine architecture. This model integrates regulatory networks and QTL linkages to prioritize genes and annotate hidden nodes for precision medicine applications.

No scientific awards were explicitly mentioned in the provided text. His research spans interdisciplinary collaborations, leveraging elastic-net modeling, single-cell data deconvolution, and Hi-C datasets to address molecular mechanisms in diseases.