About

Fan Yao is an Assistant Professor at the University of North Carolina at Chapel Hill in the Department of Statistics & Operations Research. He received his Ph.D. in Computer Science from the University of Virginia in 2025. Prior to his doctoral studies, he earned a Master of Science in Computational Mathematics in 2016 and a Bachelor of Science in Mathematics in 2013, both from Peking University. From 2023 to 2025, he was a visiting researcher at the University of Chicago. His research focuses on modeling human–AI interactions to better understand their societal impacts and to design interventions that promote socially beneficial outcomes with theoretical guarantees. He draws on mathematical tools from algorithmic game theory, statistical learning theory, online learning, and optimization. A central application of his work is analyzing and shaping multi-party interactions in online recommendation platforms and social media, where the behaviors of users, creators, and algorithms intertwine.

Research topics

• Geology
• Oceanography
• Environmental science
• Geography
• Biology
• Climatology
• Meteorology
• Ecology
• Water resource management
• Environmental engineering
• Remote sensing
• Physical geography

Selected publications

• Localized Micro‐Solvent Field Engineering for Efficient and Reproducible Quasi‐Quantum‐Dot Perovskite Light‐Emitting Diodes

  Advanced Materials · 2026-04-01

  articleCorresponding

  Perovskite light-emitting diodes (PeLEDs) are promising candidates for next-generation display and lighting technologies. However, conventional strategies for controlling morphology and crystalline structure often face challenges such as inefficient carrier transport and poor batch-to-batch reproducibility, primarily due to the presence of long organic ligands and the environment-sensitive nature of crystallization dynamics. Here, we present a localized micro-solvent field engineering strategy that simultaneously enhances device efficiency and reproducibility. By applying a nitrogen micro-gas flow, we obtain a clean nitrogen atmosphere and a lower substrate temperature for subsequent film coating. By incorporating low-boiling-point solvent acetonitrile into the precursor solution as a nucleation promoter, we precisely control the nucleation and growth kinetics. This synergistic approach, which avoids chemical hot-injection synthesis and insulating long-chain ligands, produces uniform quasi-quantum-dot perovskite films with the grain size (7-15 nm) approaching the exciton Bohr diameter with higher exciton binding energy. PeLEDs fabricated using this method demonstrate a peak external quantum efficiency of 33.79%, an average efficiency approaching 31%, excellent batch-to-batch consistency, and successful integration in pixel array devices. This strategy not only overcomes critical limitations in efficiency and reproducibility for solution-processed PeLEDs but also provides a broadly applicable framework to advance the performance and scalability of other perovskite optoelectronic devices.

  PublisherDOI

• Wildfires drive multi-year water quality degradation over the western U.S.

  Zenodo (CERN European Organization for Nuclear Research) · 2025-05-22

  datasetOpen access

  Information on the 245 burned basins, 293 unburned basins, and 356 associated fires from across the U.S. West which were used in statistical analyses of post-wildfire water quality response. Included are physiographic characteristics, as well as ESRI Shapefile polygons representing delineations for each basin and fire. Additionally, daily carbon, nitrogen, phosphorus, sediment, and turbidity data sampled from the basins' outlets are provided from 1974-2022. R coding scripts used in data processing and modeling also included, as well as data directly used in generating manuscript and "Supplementary Information" plots. Water quality data used to create this dataset are from the Water Quality Portal and wildfire burn perimeters are from the Monitoring Trends in Burn Severity database.

  PublisherDOI

• Global intercomparison of satellite-derived variability in reservoir storage

  Environmental Research Letters · 2025-06-27

  articleOpen access

  Abstract Understanding surface water reservoir storage variability enables vital insights into water management techniques, the impact of humans on global water storage, and climate-driven influences on water availability. In the past few years, recent improvements in satellite data availability and cloud computing have led to rapid growth in satellite-derived global observation of reservoir storage fluctuations, yet little research has directly compared the growing number of published storage datasets. Here we perform a first global intercomparison between five global satellite-derived reservoir storage datasets, namely GLWS, GRS, GloLakes, GRDL-Y and GRDL-L. Overall, we find generally good agreement in relative storage time series (median RMSE between datasets = 8.7% of capacity), with little substantial difference in agreement between datasets. Agreement in absolute storage is much worse (median = 19.4%), and for absolute storage, GloLakes has higher error than the other datasets. We find that agreement is worse in highly variable reservoirs, new reservoirs, and, notably, in developing countries. All datasets agree that, globally, construction of new reservoirs is driving a net increase in reservoir storage over 1999-2018, yet there is disagreement in the magnitude of these trends. Overall, our results lend confidence to the utility of satellite-derived global reservoir storage datasets for water management applications. We suggest that future research should involve reducing errors in water area observations, increasing consistency in which reservoirs are observed, and improving storage algorithm performance especially in developing areas.

  PublisherOA PDFDOI

• Quantification of wetland vegetation communities features with airborne AVIRIS-NG, UAVSAR, and UAV LiDAR data in Peace-Athabasca Delta

  UNC Libraries · 2025-08-02

  articleOpen access
  PublisherDOI

• Wildfires drive multi-year water quality degradation over the western United States

  Communications Earth & Environment · 2025-06-23 · 5 citations

  articleOpen access

  Abstract Wildfires can dramatically alter water quality, resulting in severe implications for human and freshwater systems. However, regional-scale assessments of these impacts are often limited by data scarcity. Here, we unify observations from 1984–2021 in 245 burned watersheds across the western United States, comparing post-fire signals to baseline levels from 293 unburned basins. Organic carbon and phosphorus exhibit significantly elevated levels ( p ≤ 0.05) in the first 1–5 years post-fire, while nitrogen and sediment show significant increases up to 8 years post-fire. During peak post-fire response years, average carbon, nitrogen, and phosphorus concentrations are 3–103 times pre-fire levels, and sediment 19–286 times pre-fire concentrations. Higher responses are linked with greater forested and developed areas, which respectively explain up to 31 and 33% of inter-basin response variability. Overall, this analysis provides strong evidence of multi-year water quality degradation following wildfires in the western United States and highlights the influence of basin and wildfire features. These insights may aid water managers in preparation efforts, increasing resilience of water systems to wildfire impacts.

  PublisherOA PDFDOI

• Interpretable Multi-Sensor Fusion of Optical and SAR Data for GEDI-Based Canopy Height Mapping in Southeastern North Carolina

  Remote Sensing · 2025-04-25 · 4 citations

  articleOpen accessSenior author

  Accurately monitoring forest canopy height is crucial for sustainable forest management, particularly in southeastern North Carolina, USA, where dense forests and limited accessibility pose substantial challenges. This study presents an explainable machine learning framework that integrates sparse GEDI LiDAR samples with multi-sensor remote sensing data to improve both the accuracy and interpretability of forest canopy height estimation. This framework incorporates multitemporal optical observations from Sentinel-2; C-band backscatter and InSAR coherence from Sentinel-1; quad-polarization L-Band backscatter and polarimetric decompositions from the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR); texture features from the National Agriculture Imagery Program (NAIP) aerial photography; and topographic data derived from an airborne LiDAR-based digital elevation model. We evaluated four machine learning algorithms, K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGB), and found consistent accuracy across all models. Our evaluation highlights our method’s robustness, evidenced by closely matched R2 and RMSE values across models: KNN (R2 of 0.496, RMSE of 5.13 m), RF (R2 of 0.510, RMSE of 5.06 m), SVM (R2 of 0.544, RMSE of 4.88 m), and XGB (R2 of 0.548, RMSE of 4.85 m). The integration of comprehensive feature sets, as opposed to subsets, yielded better results, underscoring the value of using multisource remotely sensed data. Crucially, SHapley Additive exPlanations (SHAP) revealed the multi-seasonal red-edge spectral bands of Sentinel-2 as dominant predictors across models, while volume scattering from UAVSAR emerged as a key driver in tree-based algorithms. This study underscores the complementary nature of multi-sensor data and highlights the interpretability of our models. By offering spatially continuous, high-quality canopy height estimates, this cost-effective, data-driven approach advances large-scale forest management and environmental monitoring, paving the way for improved decision-making and conservation strategies.

  PublisherOA PDFDOI

• Bacteriophage lysin-MOFs nanomaterials for treating apical periodontitis

  Journal of Controlled Release · 2025-06-10 · 12 citations

  article
  PublisherDOI

• GIES Case Study on Feng County Burdock of the Ancient Yellow River Floodplain

  GCdataPR · 2025-04-08

  dataset1st authorCorresponding
  PublisherDOI

• Wildfires drive multi-year water quality degradation over the western U.S.

  Research Square · 2024-07-04

  preprintOpen access
  PublisherOA PDFDOI

• Leveraging ICESat, ICESat‐2, and Landsat for Global‐Scale, Multi‐Decadal Reconstruction of Lake Water Levels

  Water Resources Research · 2024-02-01 · 22 citations

  articleOpen access1st authorCorresponding

  Abstract Lakes provide important water resources and many essential ecosystem services. Some of Earth's largest lakes recently reached record‐low levels, suggesting increasing threats from climate change and anthropogenic activities. Yet, continuous monitoring of lake levels is challenging at a global scale due to the sparse in situ gauging network and the limited spatial or temporal coverage of satellite altimeters. A few pioneering studies used water areas and hypsometric curves to reconstruct water levels but suffered from large uncertainties due to the lack of high‐quality hypsometry data. Here, we propose a novel proxy‐based method to reconstruct multi‐decadal water levels from 1992 to 2018 for both large and small lakes using Landsat images and ICESat (2003–2009) and recently launched ICESat‐2 (2018+) laser altimeters. Using the new method, we evaluate reconstructed levels of 342 lakes worldwide, with sizes ranging from 1 to 81,844 km 2 . Reconstructed water levels have a median root‐mean‐square error (RMSE) of 0.66 m, equivalent to 57% of the standard deviation of monthly level variability. Compared with two recently reconstructed water level data sets, the proposed method reduces the median RMSE by 27%–32%. The improvement is attributable to the new method's robust construction of high‐quality hypsometry, with a median R 2 value of 0.92. Most reconstructed water level time series have a bi‐monthly or higher frequency. Given that ICESat‐2 and Landsat can observe hundreds of thousands of water bodies, this method can be applied to conduct an improved global inventory of time‐varying lake levels and thus inform water resource management more broadly than existing methods.

  PublisherOA PDFDOI

Frequent coauthors

• Jean‐François Crétaux

  Laboratoire d’Études en Géophysique et Océanographie Spatiales

  66 shared

• Chao Wang

  56 shared

• Ben Livneh

  University of Colorado Boulder

  48 shared

• Shuai Zhang

  46 shared

• Balaji Rajagopalan

  42 shared

• Jida Wang

  42 shared

• Kehan Yang

  University of Washington

  42 shared

• Xiao Yang

  Southern Methodist University

  42 shared

Education

• Ph.D., Department of Geography

  Kansas State University

  2020

• M.S., Institute of Remote Sensing and Digital Earth

  University of Chinese Academy of Sciences

  2015