Jennifer Chayes

About

Jennifer Chayes is the Dean of the College of Computing, Data Science, and Society at the University of California Berkeley. She holds professorships in EECS, Mathematics, Statistics, and the School of Information. Prior to her tenure at Berkeley, she was at Microsoft for over 20 years, where she served as Technical Fellow and founded and managed three interdisciplinary labs: Microsoft Research New England, New York City, and Montreal. Her research areas include phase transitions in computer science, structural and dynamical properties of networks, modeling and graph algorithms, and the development of graphons, which are widely used in machine learning of large-scale networks. Her recent work focuses on machine learning, with applications in cancer immunotherapy, ethical decision-making, and climate change. Chayes has received numerous awards for leadership and scientific contributions, including the Anita Borg Institute Women of Vision Leadership Award, the John von Neumann Award from the Society for Industrial and Applied Mathematics, and an honorary doctorate from Leiden University. She is a member of the American Academy of Arts and Sciences and the National Academy of Sciences.

Research topics

• Computer Science
• Combinatorics
• Information Retrieval
• Political Science
• Chemistry
• Artificial Intelligence
• Data science
• Mathematics
• Statistics
• Algorithm

Selected publications

• Tackling Climate Change with Machine Learning

  ACM Computing Surveys · 2022 · 808 citations

  Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions…

  DOI

• ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis

  Journal of the American Chemical Society · 2023 · 463 citations

  We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic framework (MOF) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information, an issue that previously made the use of large language models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT…

  DOI

• A GPT‐4 Reticular Chemist for Guiding MOF Discovery**

  Angewandte Chemie International Edition · 2023 · 147 citations

  We present a new framework integrating the AI model GPT-4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT-4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success a…

  DOI

• Shaping the Water-Harvesting Behavior of Metal–Organic Frameworks Aided by Fine-Tuned GPT Models

  Journal of the American Chemical Society · 2023-12-13 · 116 citations

  articleOpen access

  We construct a data set of metal–organic framework (MOF) linkers and employ a fine-tuned GPT assistant to propose MOF linker designs by mutating and modifying the existing linker structures. This strategy allows the GPT model to learn the intricate language of chemistry in molecular representations, thereby achieving an enhanced accuracy in generating linker structures compared with its base models. Aiming to highlight the significance of linker design strategies in advancing the discovery of wa…

  PublisherOA PDFDOI

• Large language models for reticular chemistry

  Nature Reviews Materials · 2025-01-31 · 99 citations

  review
  PublisherDOI

Recent grants

• BD Hubs: Collaborative Proposal: West: Accelerating the Big Data Innovation Ecosystem

  NSF · $2.8M · 2019–2024

Frequent coauthors

• Christian Borgs

  381 shared

• L. Chayes

  University of California, Los Angeles

  50 shared

• Omar M. Yaghi

  King Abdulaziz City for Science and Technology

  45 shared

• Béla Bollobás

  43 shared

• Oliver Riordan

  39 shared

• Riccardo Zecchina

  31 shared

• Nakul Rampal

  Kavli Energy NanoScience Institute

  30 shared

• László Lovász

  Alfréd Rényi Institute of Mathematics

  29 shared

Education

• Ph.D., Physics

  Princeton University

  1983

• B.A., Physics and Biology

  Wesleyan University

  1979

Awards & honors

• Anita Borg Institute Women of Vision Leadership Award
• John von Neumann Award of the Society for Industrial and App…
• honorary doctorate from Leiden University
• member of the American Academy of Arts and Sciences
• member of the National Academy of Sciences

Similar researchers at University of California, Berkeley

• Michael I. Jordanh-175
• Matei Zahariah-64
• Jim Pitmanh-58