Before joining Georgia Tech, I was a Postdoctoral Fellow at the Center for Theoretical Neuroscience, the Zuckerman Mind Brain Behavior Institute, Columbia University. I received my Ph.D. degree in Computational and Quantitative Neuroscience and a graduate certificate in Statistics and Machine Learning from Princeton University.
If you are interested in working on machine learning and computational neuroscience with me, please directly reach out to me.
My research interest is to develop probabilistic modeling approaches and scalable and efficient inference algorithms, with applications to neural and behavior analyses, as well as many real-world problems, e.g. time series, geospatial data, speech data.
More specifically, the modeling topics involve: deep generative models, variational autoencoder, (deep) Gaussian process, Bayesian (convolutional) neural net, Bayesian nonparametric, Bayesian optimization and active learning, computer vision, hierarchical spatial and temporal models, latent dynamic models, (inverse) reinforcement learning, etc.
The applications cover but are not limited to: neural latent discovery, 3D full-body kinematic model estimation, identifying behavior syllables, studying intrinsic motives and reward representations of animal and human behaviors, fMRI decoding, neural sensory encoding, optimal experimental design, etc.