Jason Hickey

3.4k total citations · 3 hit papers
55 papers, 1.5k citations indexed

About

Jason Hickey is a scholar working on Artificial Intelligence, Computer Networks and Communications and Hardware and Architecture. According to data from OpenAlex, Jason Hickey has authored 55 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 16 papers in Computer Networks and Communications and 14 papers in Hardware and Architecture. Recurrent topics in Jason Hickey's work include Logic, programming, and type systems (13 papers), Formal Methods in Verification (10 papers) and Distributed systems and fault tolerance (9 papers). Jason Hickey is often cited by papers focused on Logic, programming, and type systems (13 papers), Formal Methods in Verification (10 papers) and Distributed systems and fault tolerance (9 papers). Jason Hickey collaborates with scholars based in United States, Hong Kong and Switzerland. Jason Hickey's co-authors include Stephan Hoyer, Yohai Bar‐Sinai, Michael P. Brenner, Jiaqi Jiang, D. D. Sell, Jianji Yang, Jonathan A. Fan, W.S. Marcus, Martin Littlewood and W.D. Sincoskie and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Journal of Geophysical Research Atmospheres.

In The Last Decade

Jason Hickey

46 papers receiving 1.4k citations

Hit Papers

align trajectories

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.

Learning data-driven discretizations for partial differential equations

2019 326 citations Yohai Bar‐Sinai, Stephan Hoyer et al. Proceedings of the National Academy of Sciences profile →
Free-Form Diffractive Metagrating Design Based on Generative Adversarial Networks

2019 277 citations Stephan Hoyer, Jason Hickey et al. profile →
Deep learning for twelve hour precipitation forecasts

2022 145 citations Lasse Espeholt, Shreya Agrawal et al. Nature Communications profile →

Peers

Jason Hickey

CNC

AS

AI

EEE

SNP

Robert D. Russell Canada

Atılım Güneş Baydin United Kingdom

R. Eykholt United States

C. S. Hsu United States

Chao Yang China

Vance Faber United States

William Wright United States

B. Cannas Italy

Michael Dellnitz Germany

Francis J. Alexander United States

Robert D. Russell Canada

Jason Hickey

151 ×0.5

CNC

126 ×0.4

AS

51 ×0.2

AI

308 ×1.2

EEE

549 ×2.2

SNP

Citations per year, relative to Jason Hickey Jason Hickey (= 1×) peers Robert D. Russell

Countries citing papers authored by Jason Hickey

Since Specialization

Citations

This map shows the geographic impact of Jason Hickey'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 Jason Hickey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jason Hickey more than expected).

Fields of papers citing papers by Jason Hickey

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jason Hickey. 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 Jason Hickey. The network helps show where Jason Hickey may publish in the future.

Co-authorship network of co-authors of Jason Hickey

This figure shows the co-authorship network connecting the top 25 collaborators of Jason Hickey. A scholar is included among the top collaborators of Jason Hickey 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 Jason Hickey. Jason Hickey is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Hickey, Jason, et al.. (2024). Innovating clinical nursing education using virtual technology to combat the effects of COVID-19: A qualitative study. Nurse Education Today. 144. 106478–106478.

2.

Espeholt, Lasse, Shreya Agrawal, Casper Kaae Sønderby, et al.. (2022). Deep learning for twelve hour precipitation forecasts. Nature Communications. 13(1). 5145–5145. 145 indexed citations breakdown →

3.

Lopez‐Gomez, Ignacio, Amy McGovern, Shreya Agrawal, & Jason Hickey. (2022). Global Extreme Heat Forecasting Using Neural Weather Models. 2(1). 19 indexed citations

4.

McGovern, Amy, Ann Bostrom, Phillip Davis, et al.. (2022). NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). Bulletin of the American Meteorological Society. 103(7). E1658–E1668. 11 indexed citations

5.

Heavlin, William D., et al.. (2022). Case‐Control Study on a Decade of Ground‐Based Magnetometers in California Reveals Modest Signal 24–72 hr Prior to Earthquakes. Journal of Geophysical Research Solid Earth. 127(10). 13 indexed citations

6.

Bar‐Sinai, Yohai, Stephan Hoyer, Jason Hickey, & Michael P. Brenner. (2019). Learning data-driven discretizations for partial differential equations. Proceedings of the National Academy of Sciences. 116(31). 15344–15349. 326 indexed citations breakdown →

7.

Bar‐Sinai, Yohai, Stephan Hoyer, Jason Hickey, & Michael P. Brenner. (2018). Data-driven discretization: a method for systematic coarse graining of partial differential equations. arXiv (Cornell University). 3 indexed citations

8.

Bar‐Sinai, Yohai, Stephan Hoyer, Jason Hickey, & Michael P. Brenner. (2018). Data-driven discretization: machine learning for coarse graining of partial differential equations. arXiv (Cornell University). 1 indexed citations

9.

Minsky, Yaron, Anil Madhavapeddy, & Jason Hickey. (2013). Real world OCaml. 7 indexed citations

10.

Bouchez, Antonin, Richard Dekany, John Angione, et al.. (2007). Palomar Laser Guide Star Adaptive Optics Observations of Globular Cluster GLIMPSE-C01. AAS. 211. 1 indexed citations

11.

Hickey, Jason, et al.. (2007). Practical Reflection for Sequent Logics. Electronic Notes in Theoretical Computer Science. 174(5). 79–94.

12.

Smith, J.D., Cristian Ţăpuş, & Jason Hickey. (2007). The Mojave Compiler: Providing Language Primitives for Whole-Process Migration and Speculation for Distributed Applications. 1419. 1–8. 2 indexed citations

13.

Ţăpuş, Cristian & Jason Hickey. (2006). Speculations: providing fault-tolerance and recoverability in distributed environments. 10–10.

14.

Hickey, Jason, et al.. (2006). Formal compiler construction in a logical framework. LISP and Symbolic Computation. 19(2-3). 197–230. 4 indexed citations

15.

Ţăpuş, Cristian, et al.. (2004). A Mechanism for Sequential Consistency in a Distributed Objects System.. 284–289.

16.

Πάλλη, Ε., et al.. (2002). The Earthshine project: Update on photometric and spectrometric measurements. 34. 798. 1 indexed citations

17.

Hickey, Jason. (2002). Introduction to the Objective Caml Programming Language. 4 indexed citations

18.

Constable, Robert L. & Jason Hickey. (2000). The metaprl logical programming environment. 20 indexed citations

19.

Liu, Xiaoming, Christoph Kreitz, Robbert van Renesse, et al.. (1999). Building reliable, high-performance communication systems from components. ACM SIGOPS Operating Systems Review. 33(5). 80–92. 9 indexed citations

20.

Marcus, W.S. & Jason Hickey. (1990). A CMOS Batcher and banyan chip set for B-ISDN. 32–33. 13 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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