My research interests lie in the fields of machine learning and Bayesian statistics. Specifically, I develop new methods and models to discover latent structure in data, including cluster structure, using Bayesian nonparametrics, hierarchical Bayes, techniques for Bayesian model comparison, and other Bayesian statistical methods. I apply these methods to problems in medicine, neuroscience, or social science.
While a clinician at heart, my research focuses on applying methods from machine learning to find underlying structure within neurological disease. My current project focus is on combining mobile-based data with MRI to better understand multiple sclerosis.
I'm a fourth-year PhD Student (since 2013) in the Computational Biology and Bioinformatics program at Duke University. My research is mainly focused on variational inference for Bayesian deep generative models, GPU computing, and developing practical machine learning algorithms for healthcare applications.
My research interests are in the development of new statistical models that can be incorporated into clinical decision support tools to solve real problems in healthcare. I am also interested in Bayesian nonparametrics, Bayesian deep learning, and scalable methods for Bayesian inference.
My research focuses on developing machine learning methods to solve problems in health care applications. Currently, we are working on developing a decision-support tool for predicting surgical complications for patients at Duke Hospital. My research focuses on transfer learning and clustering techniques to improve prediction for this tool.
My research focuses on applying methods from machine learning to brain data, in which capacity I collaborate with faculty in the Duke Statistical Machine Learning Group and the Information Initiative at Duke. I'm particulary interested in neuronal recording and functional imaging data, as well as eye tracking and choice modeling.
I am interested in Bayesian statistics, machine learning and biostatistics. More specifically, my work focuses on developing interpretable statistical models to tackle predictive and hypothesis generating tasks in biomedical and clinical research.
Cynthia Rudin is an associate professor of computer science and electrical and computer engineering. She works on topics in machine learning, data mining, and statistics, and is particularly interested in machine learning models that are interpretable, or transparent to human experts. She runs the Prediction Analysis Lab at Duke.
Vivek is a fifth year graduate student in biomedical engineering. He is currently studying autism and its connection with endogenous neural electromagnetic fields.
Xin is implementing variational inference models that are helpful to build up nonparametric machine learning models that requires minimum knowledge in advance to infer latent states.
Shariq is interested in the applications of generative machine learning approaches to biological data in order to gain understanding of complex natural processes
On the theory side, I am interested in Bayesian nonparameterics, scalable Bayesian inference and stochastic processes. For applications, I attempt to understand the patterns of human behaviors with machine learning models. I am also curious about the connection between ML and statistical firstname.lastname@example.orgMail GitHub Twitter LinkedIn
My research interests include estimation and model selection under both "large p, small n" and "large n, small p" settings as well as their applications in machine learning, change point detection, network analysis and recommender system. I'm worked with Prof. David B. Dunson developing fast Bayesian methods for huge data sets, and with Prof. Chenlei Leng developing novel algorithms for variable screening and binary graph email@example.comMail GitHub LinkedIn