Emma Strubell
Associate Professor
Studies efficient and sustainable NLP and machine learning, including the energy cost of language models.
Emma Strubell's lab at CMU works on large language models and efficient training & inference. 25 papers since 2023, 9 at top venues, mostly ACL and Findings of EMNLP. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 36%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 0 papers2025 · T1 0
- 10 papers2024 · T1 4
- 15 papers2023 · T1 5
T1 venuesOtherVenues: ACL, Findings of EMNLP, FAccT, JMLR, Nature
Research focuspaper titles that use each topic's words
- Efficient training & inference10 papers
- Large language models9 papers
- energy2 papers
- sustainable0 papers
- technologies institute0 papers
Paper list
From the lab homepage.
202410 papers
- ACL (T1)AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters
- Proceedings of the Workshop on Natural Language Processing Meets Climate Change (ClimateNLPChallenges in End-to-End Policy Extraction from Climate Action Plans
- ACL (T1)Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
- NeurIPS WorkshopFicSim: An Ethically Constructed Dataset for Long-Context Semantic Similarity Comparison within Fiction
- Findings of EMNLPGradient Localization Improves Lifelong Pretraining of Language Models
- Nature (T1)Light bulbs have energy ratings – so why can’t AI chatbots?
- ACL (T1)OLMo: Accelerating the Science of Language Models
- FAccTPower Hungry Processing: Watts Driving the Cost of AI Deployment?
- Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)Scalable Data Ablation Approximations for Language Models through Modular Training and Merging
- COLMSource-Aware Training Enables Knowledge Attribution in Language Models
202315 papers
- HiLDAdapting to Gradual Distribution Shifts with Continual Weight Averaging
- JMLR (T1)An Empirical Investigation of the Role of Pre-training in Lifelong Learning
- ACL (T1)Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference Resolution
- Findings of EMNLPData-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training
- Workshop on Efficient Systems for Foundation Models @ ICML, JulDissecting Efficient Architectures for Wake-Word Detection
- Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)DSI++: Updating Transformer Memory with New Documents
- TACL (T1)Efficient Methods for Natural Language Processing: A Survey
- NeurIPS (T1)Making Scalable Meta Learning Practical
- Proceedings of The Fourth Workshop on Simple and Efficient Natural Language Processing (SustaiNLP), JulOn the Interactions of Structural Constraints and Data Resources for Structured Prediction
- Findings of EMNLPPre-training and Fine-tuning BERT: Energy and Carbon Considerations
- Queer in AI Workshop @ ACL, JulQueer People are People First: Deconstructing Sexual Identity Stereotypes in Large Language Models
- Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)The Framework Tax: Disparities Between Inference Efficiency in Research and Deployment
Where alumni went
Alumni are not listed on the lab homepage.
Apply to CMUFall 2027
CMU SCS Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL / IELTS / Duolingo (F-1/J-1 non-native speakers)
- Fee
- USD 100 (USD 80 by early deadline)
Confirmed on the program page.
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Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
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- Papers
- The lab homepage
- Members
- Not collected
- Alumni
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