Jared Willard

2.3k total citations · 2 hit papers
14 papers, 1.1k citations indexed

About

Jared Willard is a scholar working on Environmental Engineering, Water Science and Technology and Global and Planetary Change. According to data from OpenAlex, Jared Willard has authored 14 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Environmental Engineering, 6 papers in Water Science and Technology and 6 papers in Global and Planetary Change. Recurrent topics in Jared Willard's work include Hydrological Forecasting Using AI (11 papers), Hydrology and Watershed Management Studies (6 papers) and Meteorological Phenomena and Simulations (4 papers). Jared Willard is often cited by papers focused on Hydrological Forecasting Using AI (11 papers), Hydrology and Watershed Management Studies (6 papers) and Meteorological Phenomena and Simulations (4 papers). Jared Willard collaborates with scholars based in United States. Jared Willard's co-authors include Vipin Kumar, Xiaowei Jia, Michael Steinbach, Shaoming Xu, Jordan S. Read, Jacob A. Zwart, Anuj Karpatne, Samantha K. Oliver, Alison Appling and Gretchen J. A. Hansen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Water Resources Research and ACM Computing Surveys.

In The Last Decade

Jared Willard

12 papers receiving 1.1k citations

Hit Papers

Integrating Scientific Knowledge with Machine Learning fo... 2021 2026 2022 2024 2022 2021 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
    2022 317 citations Jared Willard, Xiaowei Jia et al. ACM Computing Surveys profile →
  2. Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles
    2021 173 citations Xiaowei Jia, Jared Willard et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jared Willard United States 8 366 319 188 181 157 14 1.1k
Lin Luo China 20 242 0.7× 127 0.4× 142 0.8× 230 1.3× 201 1.3× 155 1.9k
Matthew W. Farthing United States 23 591 1.6× 232 0.7× 115 0.6× 46 0.3× 122 0.8× 76 1.7k
John Jakeman United States 21 194 0.5× 99 0.3× 65 0.3× 116 0.6× 258 1.6× 57 1.2k
Mark French United States 15 659 1.8× 371 1.2× 374 2.0× 150 0.8× 18 0.1× 74 1.4k
Charles Tong United States 21 325 0.9× 228 0.7× 257 1.4× 39 0.2× 59 0.4× 37 1.3k
Maarten Keijzer Denmark 13 328 0.9× 316 1.0× 197 1.0× 550 3.0× 18 0.1× 34 1.4k
Sangchul Lee South Korea 19 119 0.3× 227 0.7× 149 0.8× 88 0.5× 49 0.3× 89 906
Shaoming Xu United States 8 116 0.3× 78 0.2× 91 0.5× 110 0.6× 107 0.7× 13 677
Jarosław J. Napiórkowski Poland 25 573 1.6× 747 2.3× 458 2.4× 508 2.8× 15 0.1× 74 2.0k
S. Ahmad United States 16 197 0.5× 236 0.7× 426 2.3× 54 0.3× 17 0.1× 39 1.0k

Countries citing papers authored by Jared Willard

Since Specialization
Citations

This map shows the geographic impact of Jared Willard's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jared Willard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jared Willard more than expected).

Fields of papers citing papers by Jared Willard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jared Willard. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jared Willard. The network helps show where Jared Willard may publish in the future.

Co-authorship network of co-authors of Jared Willard

This figure shows the co-authorship network connecting the top 25 collaborators of Jared Willard. A scholar is included among the top collaborators of Jared Willard based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jared Willard. Jared Willard is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
1.
Willard, Jared, et al.. (2025). Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 4(2). 2 indexed citations
2.
Willard, Jared & Charuleka Varadharajan. (2025). Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification. SHILAP Revista de lepidopterología. 2(3).
3.
Willard, Jared, Charuleka Varadharajan, Xiaowei Jia, & Vipin Kumar. (2025). Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources. SHILAP Revista de lepidopterología. 4. 6 indexed citations
4.
Mahesh, Ankur, William D. Collins, Noah Brenowitz, et al.. (2025). Huge ensembles – Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators. Geoscientific model development. 18(17). 5575–5603. 2 indexed citations
5.
Mahesh, Ankur, William D. Collins, Noah Brenowitz, et al.. (2025). Huge ensembles – Part 2: Properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators. Geoscientific model development. 18(17). 5605–5633. 1 indexed citations
6.
7.
Willard, Jared, Xiaowei Jia, Shaoming Xu, Michael Steinbach, & Vipin Kumar. (2022). Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems. ACM Computing Surveys. 55(4). 1–37. 317 indexed citations breakdown →
8.
Varadharajan, Charuleka, Alison Appling, Bhavna Arora, et al.. (2022). Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?. Hydrological Processes. 36(4). 56 indexed citations
9.
Willard, Jared, Jordan S. Read, Simon Topp, Gretchen J. A. Hansen, & Vipin Kumar. (2022). Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020). Limnology and Oceanography Letters. 7(4). 287–301. 28 indexed citations
10.
Willard, Jared, Jordan S. Read, Alison Appling, et al.. (2021). Predicting Water Temperature Dynamics of Unmonitored Lakes With Meta‐Transfer Learning. Water Resources Research. 57(7). 61 indexed citations
11.
Jia, Xiaowei, Jared Willard, Anuj Karpatne, et al.. (2021). Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles. 2(3). 1–26. 173 indexed citations breakdown →
12.
Jia, Xiaowei, Jared Willard, Anuj Karpatne, et al.. (2020). Process Guided Deep Learning for Modeling Physical Systems: An Application in Lake Temperature Modeling. 3494–3496. 1 indexed citations
13.
Willard, Jared, Xiaowei Jia, Shaoming Xu, Michael Steinbach, & Vipin Kumar. (2020). Integrating Physics-Based Modeling with Machine Learning: A Survey. 187 indexed citations
14.
Read, Jordan S., Xiaowei Jia, Jared Willard, et al.. (2019). Process‐Guided Deep Learning Predictions of Lake Water Temperature. Water Resources Research. 55(11). 9173–9190. 262 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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