Keegan Hines

1.1k total citations
12 papers, 336 citations indexed

About

Keegan Hines is a scholar working on Molecular Biology, Artificial Intelligence and Biophysics. According to data from OpenAlex, Keegan Hines has authored 12 papers receiving a total of 336 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 5 papers in Artificial Intelligence and 2 papers in Biophysics. Recurrent topics in Keegan Hines's work include Protein Structure and Dynamics (4 papers), Explainable Artificial Intelligence (XAI) (4 papers) and Adversarial Robustness in Machine Learning (4 papers). Keegan Hines is often cited by papers focused on Protein Structure and Dynamics (4 papers), Explainable Artificial Intelligence (XAI) (4 papers) and Adversarial Robustness in Machine Learning (4 papers). Keegan Hines collaborates with scholars based in United States. Keegan Hines's co-authors include Richard W. Aldrich, Thomas R. Middendorf, John P. Dickerson, John R. Bankston, Sahil Verma, Chirag Shah, Sahil Verma, C. Bayan Bruss and Avi Schwarzschild and has published in prestigious journals such as Biophysical Journal, ACM Computing Surveys and The Journal of General Physiology.

In The Last Decade

Keegan Hines

12 papers receiving 331 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Keegan Hines United States	8	132	100	50	22	18	12	336
Nicholas A. Heard United Kingdom	9	227 1.7×	154 1.5×	106 2.1×	4 0.2×	13 0.7×	33	563
Sabine Hug Germany	8	297 2.3×	50 0.5×	23 0.5×	6 0.3×	4 0.2×	10	448
Adityanarayanan Radhakrishnan United States	8	137 1.0×	57 0.6×	57 1.1×	11 0.5×	3 0.2×	14	282
Tolga Can Türkiye	15	646 4.9×	59 0.6×	18 0.4×	5 0.2×	6 0.3×	51	810
Joanna Tyrcha Sweden	10	82 0.6×	56 0.6×	10 0.2×	4 0.2×	13 0.7×	25	363
Patrick Rubin‐Delanchy United Kingdom	11	112 0.8×	86 0.9×	149 3.0×	2 0.1×	21 1.2×	24	440
Bruce S. Duncan United States	9	276 2.1×	44 0.4×	11 0.2×	3 0.1×	26 1.4×	15	452
Ádám Arany Belgium	11	193 1.5×	49 0.5×	94 1.9×	4 0.2×	2 0.1×	24	378
Ismail M. Khater Canada	10	134 1.0×	50 0.5×	157 3.1×	6 0.3×	21 1.2×	17	394

Countries citing papers authored by Keegan Hines

Since Specialization

Citations

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

Fields of papers citing papers by Keegan Hines

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Keegan Hines

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

12 of 12 papers shown

1.

Verma, Sahil, et al.. (2024). Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review. ACM Computing Surveys. 56(12). 1–42. 45 indexed citations

2.

Schwarzschild, Avi, et al.. (2023). Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective. 662–678. 3 indexed citations

3.

Verma, Sahil, Keegan Hines, & John P. Dickerson. (2022). Amortized Generation of Sequential Algorithmic Recourses for Black-Box Models. Proceedings of the AAAI Conference on Artificial Intelligence. 36(8). 8512–8519. 8 indexed citations

4.

Verma, Sahil, et al.. (2020). Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review. arXiv (Cornell University). 38 indexed citations

5.

Hines, Keegan, et al.. (2019). DeepTrax: Embedding Graphs of Financial Transactions. 126–133. 17 indexed citations

6.

Hines, Keegan, et al.. (2016). Gene-Ages v1.0. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

7.

Hines, Keegan, John R. Bankston, & Richard W. Aldrich. (2015). Analyzing Single-Molecule Time Series via Nonparametric Bayesian Inference. Biophysical Journal. 108(3). 540–556. 37 indexed citations

8.

Hines, Keegan. (2015). A Primer on Bayesian Inference for Biophysical Systems. Biophysical Journal. 108(9). 2103–2113. 45 indexed citations

9.

Hines, Keegan, Thomas R. Middendorf, & Richard W. Aldrich. (2014). Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach. The Journal of General Physiology. 143(3). 401–416. 105 indexed citations

10.

Hines, Keegan, Thomas R. Middendorf, & Richard W. Aldrich. (2013). On Parameter Identifiability in Non-Linear Biophysical Models. Biophysical Journal. 104(2). 405a–405a. 1 indexed citations

11.

Hines, Keegan. (2013). Inferring Subunit Stoichiometry from Single Molecule Photobleaching. Biophysical Journal. 104(2). 527a–527a. 2 indexed citations

12.

Hines, Keegan. (2013). Inferring subunit stoichiometry from single molecule photobleaching. The Journal of General Physiology. 141(6). 737–746. 34 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.

Explore authors with similar magnitude of impact