Brunskill, Emma

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 2,676

Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2020) · arXiv

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 393

Designing mobile interfaces for novice and low-literacy users

Medhi, I., Patnaik, S., Brunskill, E., Gautama, S. N. N., Thies, W., & Toyama, K. (2011) · ACM Transactions on Computer-Human Interaction

Usability and User Interface DesignICT in Developing Communities

Cited by 285

Scaling up behavioral science interventions in online education

Kizilcec, R. F., Reich, J., Yeomans, M., Dann, C., Brunskill, E., Lopez, G., Turkay, S., Williams, J. J., & Tingley, D. (2020) · Proceedings of the National Academy of Sciences

Online Learning and AnalyticsOnline and Blended Learning

Cited by 208

New Potentials for Data‐Driven Intelligent Tutoring System Development and Optimization

Koedinger, K. R., Brunskill, E., Baker, R. S. J. D., McLaughlin, E. A., & Stamper, J. (2013) · AI Magazine

Intelligent Tutoring Systems and Adaptive LearningOnline Learning and Analytics

Cited by 178

Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning

Dann, C., Lattimore, T., & Brunskill, E. (2017) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 137

BookBuddy: Turning Digital Materials Into Interactive Foreign Language Lessons Through a Voice Chatbot

Ruan, S., Willis, A., Xu, Q., Davis, G. M., Jiang, L., Brunskill, E., & Landay, J. A. (2019) · Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale

Intelligent Tutoring Systems and Adaptive LearningSpeech and dialogue systems

Cited by 81

MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

Fleming, S. L., Lozano, A., Haberkorn, W. J., Jindal, J. A., Reis, E., Thapa, R., Blankemeier, L., Genkins, J. Z., Steinberg, E., Nayak, A., et al. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 66

MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

Fleming, S. L., Lozano, A., Haberkorn, W. J., Jindal, J. A., Reis, E. P., Thapa, R., Blankemeier, L., Genkins, J. Z., Steinberg, E., Nayak, A., et al. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 65

The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performances

Nie, A., Chandak, Y., Suzara, M., Malik, A., Woodrow, J., Peng, M., Sahami, M., Brunskill, E., & Piech, C. (2024) · arXiv

Teaching and Learning ProgrammingArtificial Intelligence in Education

Cited by 19

The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performances

Nie, A., Chandak, Y., Suzara, M., Malik, A., Woodrow, J., Peng, M., Sahami, M., Brunskill, E., & Piech, C. (2024)

Teaching and Learning ProgrammingArtificial Intelligence in Education

Cited by 19

Supervised Pretraining Can Learn In-Context Reinforcement Learning

Lee, J., Xie, A., Pacchiano, A., Chandak, Y., Finn, C., Nachum, O., & Brunskill, E. (2023) · Advances in Neural Information Processing Systems 36

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 11

Supervised Pretraining Can Learn In-Context Reinforcement Learning

Lee, J. N., Xie, A., Pacchiano, A., Chandak, Y., Finn, C., Nachum, O., & Brunskill, E. (2023) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 7