diff --git a/db/cv/okennedy.json b/db/cv/okennedy.json
index 805c0a35..8c079a60 100644
--- a/db/cv/okennedy.json
+++ b/db/cv/okennedy.json
@@ -158,6 +158,14 @@
       "type" : "award",
       "source" : "UB",
       "individual" : "YES"
+    },
+    {
+      "description" : 
+        "The SIGMOD 2019 paper titled \"Uncertainty Annotated Databases - A Lightweight Approach for Approximating Certain Answers\" received the 2020 SIGMOD Reproducability Award",
+      "year" : 2020,
+      "type" : "best-paper",
+      "source" : "SIGMOD",
+      "individual" : "NO"
     }
   ],
   "chairs" : [
diff --git a/src/news/2020-06-01-UADBs-Win-Reproducibility-Award.md b/src/news/2020-06-01-UADBs-Win-Reproducibility-Award.md
new file mode 100644
index 00000000..96b66cf4
--- /dev/null
+++ b/src/news/2020-06-01-UADBs-Win-Reproducibility-Award.md
@@ -0,0 +1,20 @@
+---
+title: UADBs win Reproducibility Award 
+author: Oliver Kennedy
+---
+
+Probabilistic and Incomplete databases are a principled way to handle data that 
+isn't perfect (and really, who's data is perfect).  Unfortunately, pretty much
+every PDB and IDB developed to date is insanely slower than their deterministic
+counterparts (to say nothing of how complex and finicky they are to use 
+correctly).  That's why, in collaboration with IIT, for the past five years, 
+we've been working towards a more user-friendly approach to incomplete data 
+management.  Instead of trying to give people perfect answers, we just help
+them keep track of *what* is uncertain through annotations and provenance
+trickery.  In other words, we're developing an Uncertainty Annotated Database
+System (or UADB).
+
+Thanks in large part to the heroic efforts of [Su Feng](http://cs.iit.edu/~dbgroup/members/sfeng.html), 
+our [latest UADB paper](https://odin.cse.buffalo.edu/papers/2019/SIGMOD-UADBs.pdf) 
+received the [SIGMOD 2020 Reproducibility Award](http://db-reproducibility.seas.harvard.edu/).
+