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Emily Webber, Amazon Web Services. Emily is a Principal Solutions Architect in the Annapurna Labs service team, working directly on Trainium, Inferentia, and the Neuron Kernel Interface. She wrote a book on pretraining foundation models and spent six years developing distributed systems for customers on Amazon SageMaker.
Yida Wang, Amazon Web Services. Yida Wang is a principal scientist with Amazon Web Services. His research interest is in systems, high-performance computing, and big data analytics. He leads a team to work on cutting-edge deep learning systems, with a focus on compiling and optimizing foundation models for efficient training and inference on AWS Trainium. The mission is to bridge the high-level models from various frameworks and low-level hardware platforms for both high-performance and ease-of-use.
Alessandro Achilles, Amazon Web Services. Alessandro is a Principal Applied Scientist at AWS AI Labs. As a member of the Foundational Research team, he researches key challenges for Large Language Models, including emergence of efficient reasoning, methods for machine unlearning, and hardware-software architecture co-design. He works closely with other teams to help bringing the latest science innovations to products.
Serina Tan, Amazon Web Services. Serina is a senior ML architect at Annapurna Labs, where she is part of the ML acceleration group responsible for developing the AWS Trainium and Inferentia product lines. As a member of the architecture team, Serina operates at the intersection of software and hardware. She collaborates closely with software engineers to optimize customer workloads on production AWS Trn/Inf instances and also hardware teams to define new architectural features for future silicon.
Hongyi Jin, Hongyi is a third-year PhD student at Carnegie Mellon University, advised by Tianqi Chen and Todd C. Mowry. His research interest lies in LLM infrastructure and deep learning compiler. He has been working closely with Amazon scientists and engineers on optimizing large language model on Trainium
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