Strubell, Emma

Energy and Policy Considerations for Deep Learning in NLP

Strubell, E., Ganesh, A., & McCallum, A. (2019) · arXiv

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 3,735

Energy and Policy Considerations for Deep Learning in NLP

Strubell, E., Ganesh, A., & McCallum, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 3,684

Energy and Policy Considerations for Modern Deep Learning Research

Strubell, E., Ganesh, A., & McCallum, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 774

Aligning artificial intelligence with climate change mitigation

Kaack, L. H., Donti, P. L., Strubell, E., Kamiya, G., Creutzig, F., & Rolnick, D. (2022) · Nature Climate Change

Green IT and SustainabilityClimate Change Policy and Economics

Cited by 570

Power Hungry Processing: Watts Driving the Cost of AI Deployment?

Luccioni, S., Jernite, Y., & Strubell, E. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 392

Measuring the Carbon Intensity of AI in Cloud Instances

Dodge, J., Prewitt, T., Tachet des Combes, R., Odmark, E., Schwartz, R., Strubell, E., Luccioni, A. S., Smith, N. A., DeCario, N., & Buchanan, W. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 257

From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate

Luccioni, A. S., Strubell, E., & Crawford, K. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency

Green IT and SustainabilityEnvironmental Impact and Sustainability

Cited by 71

OLMo: Accelerating the Science of Language Models

Groeneveld, D., Beltagy, I., Walsh, E., Bhagia, A., Kinney, R., Tafjord, O., Jha, A., Ivison, H., Magnusson, I., Wang, Y., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 70

Dolma: an open corpus of three trillion tokens for language model pretraining research

Soldaini, L., Kinney, R., Bhagia, A., Schwenk, D., Atkinson, D., Authur, R., Bogin, B., Chandu, K., Dumas, J., Elazar, Y., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 56

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

Luccioni, S., Gamazaychikov, B., Hooker, S., Pierrard, R., Strubell, E., Jernite, Y., & Wu, C.-J. (2024) · Nature

Green IT and SustainabilityEthics and Social Impacts of AI

Cited by 32

DSI++: Updating Transformer Memory with New Documents

Mehta, S., Gupta, J., Tay, Y., Dehghani, M., Tran, V., Rao, J., Najork, M., Strubell, E., & Metzler, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingInformation Retrieval and Search Behavior

Cited by 21

International AI Safety Report

Bengio, Y., Mindermann, S., Privitera, D., Besiroglu, T., Bommasani, R., Casper, S., Choi, Y., Fox, P., Garfinkel, B., Goldfarb, D., et al. (2025) · arXiv

Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Cited by 16

Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

Soldaini, L., Kinney, R., Bhagia, A., Schwenk, D., Atkinson, D., Authur, R., Bogin, B., Chandu, K., Dumas, J., Elazar, Y., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 10

OLMo: Accelerating the Science of Language Models

Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A. H., Ivison, H., Magnusson, I., Wang, Y., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 10

Source-Aware Training Enables Knowledge Attribution in Language Models

Khalifa, M., Wadden, D., Strubell, E., Lee, H., Wang, L., Beltagy, I., & Peng, H. (2024) · arXiv

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 1

Kinetics: Rethinking Test-Time Scaling Laws

Sadhukhan, R., Chen, Z., Zheng, H., Zhou, Y., Strubell, E., & Chen, B. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0