23-25 April 2024
Virtual & In-Person*
Toggle menu
Aishwarya Naresh
Reganti
Applied Scientist at Amazon/Search Science and AI
"Building Scalable Graph Models for Search and Recommendation "
"Building Scalable Graph Models for Search and Recommendation "
Don’t miss out and join visionaries, innovators, and thought leaders from all over the world at the Women in Tech Global Conference.
Vote by Sharing
Unite 100 000 Women in Tech to Drive Change with Purpose and Impact.
Do you want to see this session? Help increase the sharing count and the session visibility. Sessions with +10 votes will be available to career ticket holders.
Please note that it might take some time until your share & vote is reflected.
Session: Building Scalable Graph Models for Search and Recommendation
Graph based neural models have garnered a lot of attention over the past few years especially in Search and Recommendation technology. Large global web companies like Amazon, Facebook, LinkedIn and Google use graph based models both in production and offline to develop robust representations of their existing knowledge graph systems. In this talk, I will be going over the foundations of GNNs (Graph Neural Networks) and methods to build scalable graph based models for downstream production applications.Key Takeaways
- Understand foundations of graph based models- message passing and aggregation in GNNs, relational-GCN models, node-embedding methods
- Identify how graph based models can be used in the Search and Recommendation space (Incorporating Knowledge Graphs, link prediction as a generalized recommendation problem, some industry use-case examples)
- Review methods to scale and productionize GNN based models (distributed graph models, distributed inference, available GNN training platforms, serving GNN model representations)
Bio
Aishwarya is an Applied Scientist in the Amazon Search Science and AI Org. She works on developing large scale graph-based ML techniques that improve Amazon Search Quality, Trust and Recommendations. She obtained her Master's degree in Computer Science (MCDS) from Carnegie Mellon's Language Technology Institute, Pittsburgh. She has over 6+ years of hands-on Machine Learning experience and 20+ publications in top-tier conferences like AAAI, ACL, CVPR, NeurIPS, EACL e.t.c. She has worked on a wide spectrum of problems that involve Large Scale Graph Neural Networks, Machine Translation, Multimodal Summarization, Social Media and Social Networks, Human Centric ML, Artificial Social Intelligence, Code-Mixing e.t.c. She has mentored several Masters and PhD students in the aforementioned areas. She has also served as a reviewer in various NLP and Graph ML conferences like ACL, EMNLP, AAAI, LoG e.t.c. She has worked with some of the best minds in both academia and industry through collaborations and internships in Microsoft Research, University of Michigan, NTU Singapore, IIIT-Delhi, NTNU-Norway, University of South Carolina e.t.c.