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A view of the sustainable computing landscape

Light bulbs have energy ratings—so why can’t AI chatbots?

Making scalable meta learning practical

Efficient methods for natural language processing: A survey

Efficient and equitable natural language processing in the age of deep learning (dagstuhl seminar 22232)

Emma Strubell is an Assistant Professor at Carnegie Mellon University's Language Technologies Institute (LTI). Their research lies at the intersection of natural language processing (NLP) and machine learning, and their broad research objective is bridging the gap between state-of-the-art NLP methods, and the wide variety of users who stand to benefit from that technology, but for whom that technology does not yet work in practice. 

Their work has been recognized with a Madrona AI Impact Award, best paper awards at ACL and EMNLP, and in 2024 they were named one of the most powerful people in AI by Business Insider.

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Natural Language Processing (NLP)

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Emma Strubell | AI Power List

Greater, newer AI models come with environmental impacts

An AI's Carbon Footprint Is 5 Times Bigger Than a Car's

AI and the Environment: Sustaining the Common Good

Assistant Professor, Language Technologies Institute

Today we’re joined by Emma Strubell, currently a visiting scientist at Facebook AI Research. Emma’s focus is on NLP and bringing state of the art NLP systems to practitioners by developing efficient and robust machine learning models. Her paper, Energy and Policy Considerations for Deep Learning in NLP, hones in on one of the biggest topics of the generation: environmental impact. In this episode we discuss:

  How training neural networks have resulted in an increase in accuracy, however the computational resources required to train these models is staggering - and carbon footprints are only getting bigger Emma’s research methods for determining carbon emissions How companies are reacting to environmental concerns What we, as an industry, can be doing better

Environmental Impact of Large-Scale NLP Model Training with Emma Strubell - TWIML Talk #286

Please join us for a UW Data Science Seminar event on Thursday, February 9th 4:30 to 5:20 p.m. PST. The seminar will feature Emma Strubell from the Language Technologies Institute at Carnegie Mellon University.

Abstract: Large, pre-trained language models (LMs) produce high quality, general purpose representations of word (pieces) in context, shifting the paradigm for representation learning in NLP. Unfortunately, training and deploying these models comes at a high computational cost, limiting their development and use to the relatively small set of individuals and organizations with access to substantial computational resources. In this talk I’ll discuss: Why this is a problem, what we can do to make large pre-trained language models more accessible, and how we can leverage our expertise to help mitigate climate change beyond the operational emissions due to large LM training.

Bio: Emma Strubell is an Assistant Professor at the Language Technologies Institute in the School of Computer Science at Carnegie Mellon University, and a Visiting Scientist at the Allen Institute for Artificial Intelligence. Previously she held research scientist roles at Google and FAIR after earning her doctoral degree in 2019 from the University of Massachusetts Amherst. Her research lies at the intersection of natural language processing and machine learning, with a focus on green (computationally efficient) AI and providing pragmatic solutions to practitioners who wish to gain insights from natural language text. Her work has been recognized with a Madrona AI Impact Award, best paper awards at ACL 2015 and EMNLP 2018, and cited in news outlets including the New York Times and Wall Street Journal.

UW Data Science Seminar: Emma Strubell

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