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Anastasiya Belyaeva

Alumni

Publications

An AI system to help scientists write expert-level empirical software
Eser Aygün
Anastasiya Belyaeva
Gheorghe Comanici
Marc Coram
Hao Cui
Jake Garrison
Renee Johnston Anton Kast
Cory Y. McLean
Peter C. Norgaard
Zahra Shamsi
David Smalling
James Thompson
Subhashini Venugopalan
Brian P Williams
Chujun He
Sarah Martinson
Martyna Plomecka
Lai Wei
Yuchen Zhou
Qian-Ze Zhu … (see 21 more)
Matthew Abraham
Erica Brand
Anna Bulanova
Jeff Cardille
Chris Co
Scott Ellsworth
Grace Joseph
Malcolm Kane
Ryan K. Krueger
Johan Kartiwa
D. Liebling
Jan-Matthis Lueckmann
Paul Raccuglia
Xuefei Wang
Katherine Chou
James Manyika
Yossi Matias
J.C. Platt
Lizzie Dorfman
Shibl Mourad
Michael P. Brenner
The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. … (see more)To address this, we present an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. The system achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a wide range of benchmarks. In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, it generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. Our method also produced state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. By devising and implementing novel solutions to diverse tasks, the system represents a significant step towards accelerating scientific progress.
2025-09-08
ArXiv (preprint)
arxiv.org
arxiv.org
An AI system to help scientists write expert-level empirical software
Eser Aygün
Anastasiya Belyaeva
Gheorghe Comanici
Marc Coram
Hao Cui
Jake Garrison
Renee Johnston Anton Kast
Cory Y. McLean
Peter C. Norgaard
Zahra Shamsi
David Smalling
James Thompson
Subhashini Venugopalan
Brian P Williams
Chujun He
Sarah Martinson
Martyna Plomecka
Lai Wei
Yuchen Zhou
Qian-Ze Zhu … (see 21 more)
Matthew Abraham
Erica Brand
Anna Bulanova
Jeff Cardille
Chris Co
Scott Ellsworth
Grace Joseph
Malcolm Kane
Ryan K. Krueger
Johan Kartiwa
D. Liebling
Jan-Matthis Lueckmann
Paul Raccuglia
Xuefei Wang
Katherine Chou
James Manyika
Yossi Matias
J.C. Platt
Lizzie Dorfman
Shibl Mourad
Michael P. Brenner
The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. … (see more)To address this, we present an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. The system achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a wide range of benchmarks. In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, it generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. Our method also produced state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. By devising and implementing novel solutions to diverse tasks, the system represents a significant step towards accelerating scientific progress.
2025-09-08
ArXiv (preprint)
arxiv.org
arxiv.org
An AI system to help scientists write expert-level empirical software
Eser Aygün
Anastasiya Belyaeva
Gheorghe Comanici
Marc Coram
Hao Cui
Jake Garrison
Renee Johnston Anton Kast
Cory Y. McLean
Peter C. Norgaard
Zahra Shamsi
David Smalling
James Thompson
Subhashini Venugopalan
Brian P Williams
Chujun He
Sarah Martinson
Martyna Plomecka
Lai Wei
Yuchen Zhou
Qian-Ze Zhu … (see 21 more)
Matthew Abraham
Erica Brand
Anna Bulanova
Jeff Cardille
Chris Co
Scott Ellsworth
Grace Joseph
Malcolm Kane
Ryan K. Krueger
Johan Kartiwa
Daniel J. Liebling
Jan-Matthis Lueckmann
Paul Raccuglia
Xuefei Wang
Katherine Chou
James Manyika
Yossi Matias
J.C. Platt
Lizzie Dorfman
Shibl Mourad
Michael P. Brenner
The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. … (see more)To address this, we present an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. The system achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a wide range of benchmarks. In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, it generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. Our method also produced state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. By devising and implementing novel solutions to diverse tasks, the system represents a significant step towards accelerating scientific progress.
2025-09-08
ArXiv (preprint)
doi.org
arxiv.org
An AI system to help scientists write expert-level empirical software
Eser Aygün
Anastasiya Belyaeva
Gheorghe Comanici
Marc Coram
Hao Cui
Jake Garrison
Renee Johnston Anton Kast
Cory Y. McLean
Peter C. Norgaard
Zahra Shamsi
David Smalling
James Thompson
Subhashini Venugopalan
Brian P Williams
Chujun He
Sarah Martinson
Martyna Plomecka
Lai Wei
Yuchen Zhou
Qian-Ze Zhu … (see 21 more)
Matthew Abraham
Erica Brand
Anna Bulanova
Jeff Cardille
Chris Co
Scott Ellsworth
Grace Joseph
Malcolm Kane
Ryan K. Krueger
Johan Kartiwa
D. Liebling
Jan-Matthis Lueckmann
Paul Raccuglia
Xuefei Wang
Katherine Chou
James Manyika
Yossi Matias
J.C. Platt
Lizzie Dorfman
Shibl Mourad
Michael P. Brenner
The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. … (see more)To address this, we present an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. The system achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a wide range of benchmarks. In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, it generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. Our method also produced state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. By devising and implementing novel solutions to diverse tasks, the system represents a significant step towards accelerating scientific progress.
2025-09-08
ArXiv (preprint)
arxiv.org
arxiv.org