Cheng Li

About

Cheng Li is an Assistant Professor of Chinese Studies at Carnegie Mellon University, specializing in the literary and cultural history of modern China. He investigates the rise of modern China through cultural and historical perspectives, focusing on areas such as the environment, military, and infrastructure. After receiving his PhD in modern China studies from Yale University in 2022, he joined Carnegie Mellon University as an assistant professor. His primary research engages with modern Chinese environmental literature, also known as ecocriticism, as well as film and history. Additionally, his research interests encompass science fiction, infrastructure studies, and military studies. Cheng Li's scholarly work has been published in several academic journals, and his first book, "Contested Environmentalisms: Trees and the Making of Modern China," was published by Stanford University Press in 2025. The dissertation version of this book earned the Marston Anderson Prize for the best dissertation in the East Asian department at Yale University in 2022.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Political Science
• Business
• Geography
• Medicine
• Economics
• Econometrics
• Operations research

Selected publications

• FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

  arXiv (Cornell University) · 2020 · 2264 citations

  Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combination of two common SSL methods: consistency regularization and pseudo-labeling. Our algorithm, FixMatch, first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction. The model i…

  DOI

• Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

  Proceedings of the National Academy of Sciences · 2022 · 311 citations

  Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hu…

  DOI

• The United States COVID-19 Forecast Hub dataset

  Scientific Data · 2022 · 126 citations

  Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hosp…

  DOI

• Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

  medRxiv (Cold Spring Harbor Laboratory) · 2021 · 77 citations

  Abstract Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Fo…

  DOI

• LEAF: A Living Benchmark for Event-Augmented Forecasting

  ArXiv.org · 2026-05-09

  articleOpen access

  Large Language Models (LLMs) are increasingly applied to forecasting. To evaluate this capability while mitigating pre-training data contamination, several living benchmarks have been proposed. However, existing benchmarks either lack the multidimensional events essential for accurate forecasting due to data scarcity, or focus on relatively closed environments. To assess the predictive capabilities of LLMs in complex, real-world scenarios, we propose LEAF, the first living benchmark for event-au…

  PublisherOA PDF

Frequent coauthors

• Ying Jin

  Affiliated Hospital of Nantong University

  87 shared

• Junjie Gu

  Sichuan University

  57 shared

• Yu Ma

  Fudan University

  50 shared

• Tomas Pfister

  50 shared

• Barnabás Póczos

  33 shared

• Ying Yang

  Nanjing University

  29 shared

• Judith Hyle

  St. Jude Children's Research Hospital

  27 shared

• Shaela Wright

  St. Jude Children's Research Hospital

  27 shared

Education

• Ph.D, Developmental Biology

  Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences

  2009

• Bachelor, Department of Life Sciences

  Central China Normal University

  2004

Awards & honors

• Marston Anderson Prize for the best dissertation in the East…
• Falk Research Grant, Carnegie Mellon University (2023)
• East Asian Prize Fellowship, Yale University (2021-2022)
• Environmental Humanities Certificate, Yale University (2020)

Similar researchers at Carnegie Mellon University

• Sanghee Kangh-71
• Rémi Adam van Compernolleh-26
• Seth Wienerh-14