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PLDI 2020
Mon 15 - Fri 19 June 2020
Tue 16 Jun 2020 08:30 - 09:30 at MAPL live stream - Graph Neural Networks for Program Reasoning Chair(s): Ke Wang

Graph neural networks (GNNs) have received more and more attention in past several years, due to the wide applications of graphs and networks in different domains, and the superiority of their performance compared to traditional heuristics-driven approaches. In this tutorial, we will provide a gentle introduction of GNNs and its potential applications to the field of programming language. Specifically, this tutorial will cover the following parts. First, the basic principles and general architecture of GNNs will be introduced, followed by several GNN instances, including the most well-known Graph Convolutional Network. Second, different graph-related downstream tasks will be introduced, which will be partitioned into node-level and graph-level tasks, with the goal to connect these tasks with potential PL applications. Third, several recently developed GNN algorithms that are designed for programming language applications will be introduced. In the end, we will provide some discussions on the open questions in this direction.

Yizhou Sun is an associate professor at department of computer science of UCLA. Prior to that, she was an assistant professor in the College of Computer and Information Science of Northeastern University. She received her Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2012. Her principal research interest is on mining graphs/networks, and more generally in data mining, machine learning, and network science, with a focus on modeling novel problems and proposing scalable algorithms for large-scale, real-world applications. She is a pioneer researcher in mining heterogeneous information network, with a recent focus on deep learning on graphs/networks. Yizhou has over 100 publications in books, journals, and major conferences. Tutorials of her research have been given in many premier conferences. She received 2012 ACM SIGKDD Best Student Paper Award, 2013 ACM SIGKDD Doctoral Dissertation Award, 2013 Yahoo ACE (Academic Career Enhancement) Award, 2015 NSF CAREER Award, 2016 CS@ILLINOIS Distinguished Educator Award, 2018 Amazon Research Award, and 2019 Okawa Foundation Research Grant.

Tue 16 Jun

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08:00 - 10:00
Graph Neural Networks for Program ReasoningMAPL at MAPL live stream
Chair(s): Ke Wang Visa Research
Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs
Elizabeth Dinella University of Pennsylvania
A Gentle Tutorial on Graph Neural Networks and Its Application to Programming Languages
LambdaNet: Probabilistic Type Inference using Graph Neural Networks
Işıl Dillig University of Texas at Austin, USA