Moin Nadeem is a masters student at MIT, where he studies natural language generation. His research interests broadly include natural language processing, information retrieval, and software systems for machine learning.

He is currently studying sampling methods for natural language generation. In the past, he has worked on bias in natural language models, automated fake news detection, and was co-president of MIT’s Machine Intelligence Community

Outside of research, he likes hack on projects with friends, go swimming, and is a voracious reader.


  • Natural Language Processing
  • Knowledge Graphs
  • Databases
  • Public Policy


  • M.Eng in Artificial Intelligence, 2020 - 2021

    Massachusetts Institute of Technology

  • S.B. in Computer Science and Electrical Engineering, 2016 - 2020

    Massachusetts Institute of Technology

Recent Publications

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(2020). StereoSet: Measuring stereotypical bias in pretrained language models. arXiv preprint arXiv:2004.09456.

PDF Code Project Press

(2019). FAKTA: An automatic end-to-end fact checking system. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations).

PDF Project Press

(2019). Neural multi-task learning for stance prediction. Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER).


(2018). Context-Aware Systems for Sequential Item Recommendation. 2018 IEEE International Conference on Data Mining Workshops (ICDMW).


(2016). Identifying depression on Twitter. arXiv preprint arXiv:1607.07384.


Recent & Upcoming Talks

Context-Aware Systems for Sequential Item Recommendation



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