Lead Research Scientist
Neil Shah is a Lead Research Scientist at Snap Inc, with interests spanning data mining, machine learning and computational social science, specifically in the contexts of graph-based modeling of user behavior and misbehavior. His work has resulted in 45+ conference and journal publications, in top venues such as ICLR, KDD, WSDM, WWW, AAAI and more, including several best-paper awards. He has also served as an organizer, chair and senior program committee member at a number of these. He has had previous research experiences at Lawrence Livermore National Laboratory, Microsoft Research, and Twitch. He earned a PhD in Computer Science in 2017 from Carnegie Mellon University’s Computer Science Department, funded partially by the NSF Graduate Research Fellowship.
Talks and Events
2022 Talk: Fast and Accurate Real-time Inference with Graph-less Neural Networks
Graph Neural Networks (GNNs) are increasingly popular tools for graph machine learning tasks of recent years, including those on knowledge graphs. Yet, these models come with major scalability challenges owing to their complex data dependency, requiring multiple hops of information away from an inference target. These challenges make real-time inference with GNNs at best slow, and at worst infeasible in practical settings. To circumvent these challenges, we propose Graph-less Neural Networks — a simple yet hugely impactful model training and inference paradigm which yields competitively accurate models which infer faster than naive GNNs by 146-273x, and faster than other acceleration methods by 14-27x across node classification benchmarks.