2022

• 
  Meta (Facebook): Building Efficient, Scalable, and Inclusive Federated Learning Systems. 2022
• 
  Hong Kong University of Science and Technology: From Neural Architecture Search to Data-Centric AutoML. 2022
• 
  Rutgers University: From Neural Architecture Search to Data-Centric AutoML. 2022

2021

• 
  Stanford University: Empowering the Next Billion Devices with Deep Learning. 2021
• 
  University of Southern California: Empowering the Next Billion Devices with Deep Learning. 2021
• 
  ICML'21 AutoML Workshop Keynote: Encoding is an Important Design Decision in Neural Architecture Search. 2021
• 
  Workshop on Energy Efficient Machine Learning and Cognitive Computing: Efficient Neural Architecture Search at Scale. 2021

2020

• 
  Facebook AI Systems Faculty Summit: Efficient On-Device Distributed Deep Learning via Importance Sampling. 2020
• 
  ECCV'20 Tutorial on Automated Deep Learning: Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? 2020