PeopleofSuperintelligence

Researcher

Jessica Grogan

Jessica Grogan is a theoretical computer science PhD student at SUNY Buffalo who has coauthored papers on structured matrices and sequence models. In 2022 she coauthored the Monarch paper with Tri Dao, Atri Rudra and Christopher Ré, among others, and it received an Outstanding Paper Runner Up award at ICML. She went on to coauthor Monarch Mixer, a 2023 architecture built on Monarch matrices that was presented as an oral at NeurIPS. Her 2025 ICLR paper, written with Jerry Liu and others, trains sequence models to carry out gradient descent for least squares at near machine precision. At Buffalo, she is advised by Atri Rudra.

Timeline

  1. April 2022

    The Monarch matrices paper

    She coauthors "Monarch: Expressive Structured Matrices for Efficient and Accurate Training" with Tri Dao, Beidi Chen, Atri Rudra, Christopher Ré and others, posted to arXiv in April 2022. It proposes Monarch matrices, a hardware-efficient and expressive class of structured matrices parameterized as products of two block-diagonal matrices, and reports 2x faster ViT and GPT-2 training with comparable model quality.

  2. 2022

    ICML Outstanding Paper Runner Up

    The Monarch paper appears at ICML, the 39th International Conference on Machine Learning, in July 2022, where it is named an Outstanding Paper Runner Up. It also receives an Outstanding Poster Award.

  3. 2023

    Monarch Mixer at NeurIPS

    In October 2023, she coauthors Monarch Mixer (M2) with Daniel Y. Fu, Simran Arora, Atri Rudra, Christopher Ré and others. M2 builds an architecture from Monarch matrices along both sequence length and model dimension, matches BERT-base and BERT-large in GLUE quality with up to 27% fewer parameters, and is a NeurIPS 2023 oral.

  4. 2025

    High-precision least squares at ICLR

    In March 2025, she coauthors "Towards Learning High-Precision Least Squares Algorithms with Sequence Models" with Jerry Liu, Owen Dugan, Atri Rudra, Christopher Ré and others. The paper trains gated convolutions and linear attention models to run gradient descent on least squares problems to near machine precision, and it appears at ICLR in Singapore in April 2025.

  5. As of October 2026

    PhD student at Buffalo

    She is a Ph.D. student in theoretical computer science at SUNY Buffalo, advised by Atri Rudra.

  1. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes University at Buffalo, State University of New York. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  2. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes University at Buffalo, State University of New York. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  3. Google has the highest combined h-index among AI organizationsIncludes University at Buffalo, State University of New York. We estimate that Google holds the highest combined h-index at 8,016, while universities account for 73% of the deduplicated total across AI organizations.
  4. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes University at Buffalo, State University of New York. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Jessica Grogan yet. Send us a free-licensed photo.

  1. [2204.00595] Monarch: Expressive Structured Matrices for Efficient and Accurate Training arxiv.org. Checked 21 October 2026.
  2. ICML 2022 Awards icml.cc. Checked 21 October 2026.
  3. Jessica Grogan jessgrogan.com. Checked 21 October 2026.
  4. [2310.12109] Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture arxiv.org. Checked 21 October 2026.
  5. NeurIPS Oral Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture neurips.cc. Checked 21 October 2026.
  6. [2503.12295] Towards Learning High-Precision Least Squares Algorithms with Sequence Models arxiv.org. Checked 21 October 2026.
  7. Towards Learning High-Precision Least Squares Algorithms with Sequence Models - SUNY University at Buffalo researchconnect.buffalo.edu. Checked 21 October 2026.
  8. Rudra, Atri - Office of the Provost - University at Buffalo buffalo.edu. Checked 21 October 2026.