Title: "Algorithms for Mining Approximate Graph Patterns"   ABSTRACT            Many real-world graphs have complex labels on the nodes and edges. Mining only exact patterns yields limited insights, since it may be hard to find exact matches. However, in many domains it is relatively easy to define a cost (or distance) between different labels. Using this information, it becomes possible to mine a much richer set of approximate subgraph patterns, which preserve the topology but allow bounded label mismatches. We present novel and scalable methods to efficiently solve the approximate isomorphism problem. We show that approximate mining yields interesting patterns in several real-world graphs ranging from IT and protein interaction networks to protein structures. Later during the talk, we will discuss some of our very  recent work on "Graph Mining in GPUs" and mining interesting patterns from probabilistic graphs. BIOGRAPHICAL NOTE Pranay Anchuri is a Ph.D. candidate in the Department of Computer Science at Rennselaer Polytechnic Institute, Troy, USA where he is advised by Prof. Mohammed J. Zaki.  His research interests are in large scale graph mining, approximate pattern mining, and social network analysis.  Prior to Yahoo, he also worked as summer research intern at IBM T.J Watson, NY (parallel graph mining), LinkedIn, CA (mining service call graphs) and QCRI, Doha (Predicting real time events in airplane data with Boeing Research).  Earlier, he received his B.Tech in Computer Science with an Honors in Data Mining from IIIT Hyderabad, India.