معرفی
Neil Shah is a Lead Research Scientist at Snap Inc., leading initiatives in user modeling, personalization, and trust and safety across Snapchat. His research focuses on advancing machine learning algorithms for large-scale structured data, including graph and sequential representations, with applications to recommendation systems and social platform security.
- PhD in Computer Science, Carnegie Mellon University (2017), advised by Christos Faloutsos
- B.S. in Computer Science, North Carolina State University
Current research interests span:
- Graph Neural Networks (GNNs) for real-time inference and scalable training
- Cross-domain recommendation systems and generative modeling
- Test-time augmentation and hyperbolic geometry in representation learning
- Explainability methods for GNNs and fairness-aware outlier detection
Recent publications highlight productionized GNN frameworks (GiGL), multimodal graph benchmarks, and novel approaches to link prediction and collaborative filtering. His work has appeared at top venues like KDD, ICLR, NeurIPS, and WWW.
Scientific recognition includes:
- Outstanding Service Award at WSDM 2022
- Best Paper Honorable Mention at CHI 2019
۰مقاله ثبتشده
