Ying Yang (杨颖)

I am a PhD candidate in neural computation and machine learning, at Carnegie Mellon University. I am generally interested statistical models for neuoimaging and human visual cognition. I have been working on source localization in magnetoencephalography (MEG) and electroencephalogram (EEG), and developed models to study correlation between neural signals and external features, as well as models to estimate connectivity between regions. Using these models, we study the spatio-temporal dynamics in human visual cortex. Specifically, we explore what information is coded at different time points and brain areas and how information flows.


2011-2016, Ph.D. student in Neural Computation and Machine Learning, Carnegie Mellon University, coadvised by Prof. Michael J. Tarr and Prof. Robert E. Kass

2007-2011, B.S. in Biology, Peking University, Beijing, China


Yang, Y., Aminoff, E. M., Tarr, M. J. and Kass, R. E. (2016). A state-space model of cross-region dynamic connectivity in MEG/EEG. In Advances In Neural Information Processing Systems (pp. 1226-1234).

Yang, Y., Tarr, M. J. and Kass, R. E.(2014). Estimating Learning Effects: A Short-Time Fourier Transform Regression Model for MEG Source Localization (2014). In the 4th Workshop on Machine Learning and Interpretation in NeuroImaging (MLINI) at the Conference on Neural Information Processing Systems.


Fall 2014, Teaching assistant for 10601 Machine Learning, Carnegie Mellon University. Instructors: William Cohen and Ziv Bar-Joseph

Summer 2014, Teaching assistant for summer workshop for the Multimodel Neuroimaging Training Program, University of Pittsburgh

Summer 2013, Teaching assistant undergraduate program in neural computation, Center for the Neural Basis of Cognition, Carnegie Mellon University