Machine Learning for Meaningful Lives


Welcome to the Machine Learning for Meaningful Lives Lab (M2L2), also known as the M2Lab!

We focus on building machine learning methods and applying them to biomedical research to study biological mechanisms, improve disease detection, and support treatment selection, etc. From a machine learning point of view, we develop methods for high-dimensional and small sample size problems, optimization, structured variable selection, density estimation, functional data analysis, and neural networks. From a bioinformatics or computational biology point of view, we study gene regulation, including both transcription and translation, heterochromatin dynamics, and automated feature extraction for time series in electrocardiology and volumetric 3D images for cell deformation. In practice, these methods have been successfully applied to biomarker selection, cancer detection, protein synthesis estimation, etc. More details can be found on the research overview page.

Principal Investigator


Joyce Tzu-Yu Liu 劉子毓

Joyce is an Associate Professor and Yushan Young Fellow at the Electrical Engineering Department and Graduate Institute of Biomedical Electronics and Bioinformatics at National Taiwan University (NTU). In addition, she is affiliated with Master's Program in Smart Medicine and Health Informatics, TIGP Program on Bioinformatics, Global Undergraduate Program in Semiconductors, and Data Science Degree Program. Prior to her position at NTU, she was the Director of Machine Learning Science at Freenome, with multiple years of industry experiences in Silicon Valley from research to product development for building blood-based cancer screening tests. She was a Simons Postdoctoral Fellow an the Departments of Mathematics and Biology at the University of Pennsylvania and with the Electrical Engineering and Computer Sciences (EECS) department at the University of California, Berkeley. Joyce received her B.S. from the National Taiwan University (2007) and her Ph.D. from the University of Michigan, Ann Arbor (2013), both in Electrical Engineering. 

She is a researcher in machine learning and computational biology. Her research includes statistical learning from high dimensional and small sample size problems, optimization, structured variable selection, density estimation, functional data analysis, transfer learning, multi-task learning, data fusion, convolutional neural network and their applications to biomedical data. These research experiences involve analysis of large datasets, e.g., genomic sequencing data, proteomics data, 3D/4D microscopy images, 3D human faces, and time series of electrocardiograms and vital signs.

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