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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+<WRAP 70% center>
+^  **DATE:**|Thursday, Sept. 17, 2020 |
+^  **TIME:**|1:15pm -- 2:15pm |
+^  **LOCATION:**|Zoom meeting |
+^  **SPEAKER:**|Kexuan Li, Binghamton University |
+^  **TITLE:**|A Review of Deep Generative Models and Normalizing Flows  |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+Generative models are widely used in many subfields of AI and Machine
+Learning. Roughly speaking, there are four types of generative models in
+the deep learning field, including variational autoencoders (VAE),
+generative adversarial networks (GAN), autoregressive models, and
+normalizing flow models. In this talk, I will give a big picture of
+generative models in deep learning but focus on normalizing flow models,
+from the first normalizing flow model introduced in 2015, to the
+state-of-the-art models invented this year (2020). In the end, if time
+permits, I will also discuss some open problems in deep generative models
+and my solutions
+</WRAP>