Nick Dexter

682 total citations · 1 hit paper
14 papers, 308 citations indexed

About

Nick Dexter is a scholar working on Statistics, Probability and Uncertainty, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Nick Dexter has authored 14 papers receiving a total of 308 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Statistics, Probability and Uncertainty, 4 papers in Computational Mechanics and 4 papers in Artificial Intelligence. Recurrent topics in Nick Dexter's work include Probabilistic and Robust Engineering Design (6 papers), Model Reduction and Neural Networks (3 papers) and Numerical methods in inverse problems (3 papers). Nick Dexter is often cited by papers focused on Probabilistic and Robust Engineering Design (6 papers), Model Reduction and Neural Networks (3 papers) and Numerical methods in inverse problems (3 papers). Nick Dexter collaborates with scholars based in Canada, United States and United Kingdom. Nick Dexter's co-authors include Maxwell W. Libbrecht, Sara Mostafavi, Wyeth W. Wasserman, Gherman Novakovsky, Ben Adcock, Clayton Webster, Abdellah Chkifa, Hoang Tran, Guannan Zhang and Simone Brugiapaglia and has published in prestigious journals such as Nature Reviews Genetics, Mathematics of Computation and Neural Networks.

In The Last Decade

Nick Dexter

13 papers receiving 297 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.

Obtaining genetics insights from deep learning via explainable artificial intelligence

2022 185 citations Gherman Novakovsky, Nick Dexter et al. Nature Reviews Genetics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nick Dexter Canada	6	105	81	47	32	30	14	308
Melvin K. Simmons United States	10	86 0.8×	60 0.7×	21 0.4×	6 0.2×	12 0.4×	17	379
Cheng Tai China	6	71 0.7×	128 1.6×	40 0.9×	4 0.1×	21 0.7×	11	355
Andreas Artemiou United Kingdom	12	37 0.4×	70 0.9×	50 1.1×	8 0.3×	5 0.2×	39	354
Bartosz Bosak Poland	8	19 0.2×	19 0.2×	19 0.4×	17 0.5×	9 0.3×	17	205
Brodie Lawson Australia	10	72 0.7×	87 1.1×	2 0.0×	15 0.5×	5 0.2×	23	349
Zhanhao Zhang China	4	19 0.2×	55 0.7×	9 0.2×	4 0.1×	6 0.2×	9	184
Daniel E. Worrall Netherlands	5	21 0.2×	126 1.6×	38 0.8×	3 0.1×	18 0.6×	7	401
Chao Pang China	9	99 0.9×	24 0.3×	12 0.3×	93 2.9×	13 0.4×	15	279
Adityanarayanan Radhakrishnan United States	8	137 1.3×	57 0.7×	9 0.2×	1 0.0×	8 0.3×	14	282

Countries citing papers authored by Nick Dexter

Since Specialization

Citations

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

Fields of papers citing papers by Nick Dexter

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nick Dexter

This figure shows the co-authorship network connecting the top 25 collaborators of Nick Dexter. A scholar is included among the top collaborators of Nick Dexter 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 Nick Dexter. Nick Dexter 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:

14 of 14 papers shown

1.

Adcock, Ben, et al.. (2025). Optimal approximation of infinite-dimensional holomorphic functions II: Recovery from i.i.d. pointwise samples. Journal of Complexity. 89. 101933–101933.

2.

Adcock, Ben, et al.. (2024). On Efficient Algorithms for Computing Near-Best Polynomial Approximations to High-Dimensional, Hilbert-Valued Functions from Limited Samples. 2 indexed citations

3.

Adcock, Ben, et al.. (2024). Optimal approximation of infinite-dimensional holomorphic functions. CALCOLO. 61(1). 5 indexed citations

4.

Adcock, Ben, et al.. (2024). Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks. Neural Networks. 181. 106761–106761. 3 indexed citations

5.

Adcock, Ben, et al.. (2024). Optimal deep learning of holomorphic operators between Banach spaces. 27725–27789. 1 indexed citations

6.

Adcock, Ben, et al.. (2023). An Adaptive Sampling and Domain Learning Strategy for Multivariate Function Approximation on Unknown Domains. SIAM Journal on Scientific Computing. 45(1). A200–A225. 3 indexed citations

7.

Adcock, Ben, et al.. (2022). CAS4DL: Christoffel adaptive sampling for function approximation via deep learning. 20(2). 1 indexed citations

8.

Novakovsky, Gherman, Nick Dexter, Maxwell W. Libbrecht, Wyeth W. Wasserman, & Sara Mostafavi. (2022). Obtaining genetics insights from deep learning via explainable artificial intelligence. Nature Reviews Genetics. 24(2). 125–137. 185 indexed citations breakdown →

9.

Dexter, Nick, et al.. (2021). INGOT-DR: an interpretable classifier for predicting drug resistance in M. tuberculosis. Algorithms for Molecular Biology. 16(1). 17–17. 7 indexed citations

10.

Adcock, Ben, et al.. (2021). Improved Recovery Guarantees and Sampling Strategies for TV Minimization in Compressive Imaging. SIAM Journal on Imaging Sciences. 14(3). 1149–1183. 7 indexed citations

11.

Adcock, Ben & Nick Dexter. (2021). The Gap between Theory and Practice in Function Approximation with Deep Neural Networks. SIAM Journal on Mathematics of Data Science. 3(2). 624–655. 51 indexed citations

12.

Dexter, Nick, et al.. (2020). An Interpretable Classification Method for Predicting Drug Resistance in M. Tuberculosis. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 18. 1 indexed citations

13.

Chkifa, Abdellah, Nick Dexter, Hoang Tran, & Clayton Webster. (2017). Polynomial approximation via compressed sensing of high-dimensional functions on lower sets. Mathematics of Computation. 87(311). 1415–1450. 39 indexed citations

14.

Dexter, Nick, Clayton Webster, & Guannan Zhang. (2016). Explicit cost bounds of stochastic Galerkin approximations for parameterized PDEs with random coefficients. Computers & Mathematics with Applications. 71(11). 2231–2256. 3 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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