Sharing and analyzing large biomedical data requires tackling privacy and efficiency in a way to preserve the utility. My background in data privacy and in machine learning gives me a unique perspective for integrated solutions. Currently, I am an assistant professor of the Division of Biomedical Informatics (DBMI) at UCSD. Before joining DBMI, I was a founding member of the Data Mining and Information System Laboratory at the University of Iowa, and later joined in the Data Privacy Laboratory at Carnegie Mellon as a PhD student, advised by Dr. Latanya Sweeney. During the last two years of my graduate study, I had the unique opportunity to be a visiting student at the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT and at the Division of Biomedical Informatics (DBMI) at UCSD. During my postdoctoral training, I worked closely with Dr. Ohno-Machado on calibrating predictive models and developing data anonymization techniques. These experiences gave me a deeper way to think about applying machine learning in medical informatics to simultaneously address data privacy and efficiency issues. In 2013, I joined the DBMI faculty to continue my adventure in healthcare research. I received a K99/R00 grant from NLM for healthcare privacy research, co-chaired the 2nd IEEE Conference on Health Informatics, Imaging, and System Biology, and organized the first and second privacy workshop for iDASH (a National Center for Biomedical Computing of data analysis, anonymization, and sharing). I am serving as the associate editor for BMC medical informatics and decision making and the guest lead editor for Cancer Informatics supplement. I am deeply committed to solving data science challenges in biomedical informatics.