Add SIAM UQ 2022 slides.

Author: grimmmyshini

_data/preslist.yml

@@ -1,3 +1,35 @@
+- title: "Estimating Floating-Point Errors Using Automatic Differentiation"
+  description: |
+    Floating-point errors are a testament to the finite nature of computing 
+    and if left uncontrolled they can have catastrophic results. As such, for 
+    high-precision computing applications, quantifying these uncertainties 
+    becomes imperative. There have been significant efforts to mitigate such 
+    errors by either extending the underlying floating-point precision, using 
+    alternate compensation algorithms or estimating them using a variety of 
+    statistical and non-statistical methods. A prominent method of dynamic 
+    floating-point error estimation is using Automatic Differentiation (AD).
+    However, most state-of-the-art AD-based estimation software requires 
+    manually adapting or annotating the source code by some amount. Moreover, 
+    operator overloading AD based error estimation tools call for multiple 
+    gradient recomputations to report errors over a large variety of inputs
+    and suffer from all the shortcomings of the underlying operator 
+    overloading strategy such as reduced efficiency. In this work, we propose 
+    a customizable way to use AD to synthesize source code for estimating 
+    uncertainties arising from floating-point arithmetic in C/C++ applications. 
+    
+    Our work presents an automatic error annotation framework that can be used 
+    in conjunction with custom user defined error models. We also present our 
+    progress with error estimation on GPU applications.
+
+  location: "[SIAM UQ 2022](https://www.siam.org/conferences/cm/conference/uq22)"
+  date: 2022-04-14
+  speaker: V Vassilev, G Singh
+  id: "FPErrorEstADSIAMUQ2022"
+  artifacts: |
+    [Video](https://www.youtube.com/watch?v=pndnawFPKHA&list=PLeZvkLnDkqbS8yQZ6VprODLKQVdL7vlTO&index=8), 
+    [Link to slides](/assets/presentations/G_Singh-SIAMUQ22_FP_Error_Estimation.pdf)
+  highlight: 1
+
 - title: "GPU Acceleration of Automatic Differentiation in C++ with Clad"
   description: |
     Automatic Differentiation (AD) is instrumental for science and industry. It