Carnegie Mellon University

Ameet Talwalkar earns NSF CAREER award.

March 24, 2021

Talwalkar Earns NSF Career Award

By Roberto Iriondo

Aaron Aupperlee
  • Senior Director of Media Relations
  • 412-268-9068

Ameet Talwalkar, an assistant professor in the School of Computer Science's Machine Learning Department, has received a National Science Foundation CAREER award, the organization's most prestigious award for young faculty members.

Talwalkar's NSF-supported project focuses on the broad goal of democratizing deep learning. Deep learning has led to remarkable artificial intelligence breakthroughs on many important problems such as object recognition for autonomous vehicles, voice-activated assistants, and automated machine translation. At the heart of these breakthroughs is the design of complex, domain-specific deep neural network architectures. Unfortunately, only a small set of highly-trained researchers are equipped with the resources and expertise to undertake this arduous, ad-hoc design process.  Moreover, design efforts have been largely limited to applications in a handful of domains, most notably computer vision and natural language processing. While the burgeoning field of neural architecture search (NAS) aims to automate the design of neural network architectures, existing work on NAS has narrowly focused on these same well-studied domains. 

This project aims to develop, analyze, and implement novel methods that enable automated neural architecture design beyond these restricted domains. The project also involves collaborations with practitioners in new domains to empower them to develop new architectures for their applications. It also includes extensively educational efforts to create course material covering the complete lifecycle of machine learning workflows.

Professor Talwalkar earned his bachelor's degree in Computer Science at Yale University and his M.S. and Ph.D. in Computer Science at New York University. Before joining CMU, he was an assistant professor at the University of California, Los Angeles. He is also a co-founder and Chief Scientist of Determined AI, whose open-source deep learning training platform makes building models fast and easy.

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