Differences

This shows you the differences between two versions of the page.

--- seminars:stat:nov302023 [2023/11/22 00:12] – created qyu
+++ seminars:stat:nov302023 [2023/11/22 00:13] (current) – qyu
@@ Line 1: / Line 1: @@
+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematics and Statistics</WRAP>
+<WRAP 70% center>
+^  **DATE:**|Thursday, November 30, 2023 |
+^  **TIME:**|1:15pm -- 2:15pm |
+^  **LOCATION:**|WH 100E |
+^  **SPEAKER:**|Praveen Niranda, Binghamton University |
+^  **TITLE:**|Network Reconstruction From High Dimensional Ordinary Differential Equations  |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+This presentation is about a paper by Chen, S., Shojaie, A. & Witten, D.
+that proposes a novel method for learning a dynamical system from
+high-dimensional time-course data. A dynamical system is a system of
+variables that change over time according to some rules, such as a gene
+regulatory network. The paper’s method uses a non-parametric model of
+additive ordinary differential equations (ODEs) and a sparsity-inducing
+penalty to estimate the network structure without estimating the
+derivatives of the variables, which are often noisy and inaccurate. This
+paper shows that the method can consistently recover the true network
+structure even in high dimensions and outperforms existing methods on
+synthetic and real data.
+</WRAP>