Dylan Slack

1.3k total citations · 1 hit paper
6 papers, 478 citations indexed

About

Dylan Slack is a scholar working on Artificial Intelligence, Health Informatics and Safety Research. According to data from OpenAlex, Dylan Slack has authored 6 papers receiving a total of 478 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 2 papers in Health Informatics and 1 paper in Safety Research. Recurrent topics in Dylan Slack's work include Explainable Artificial Intelligence (XAI) (4 papers), Adversarial Robustness in Machine Learning (3 papers) and Artificial Intelligence in Healthcare and Education (2 papers). Dylan Slack is often cited by papers focused on Explainable Artificial Intelligence (XAI) (4 papers), Adversarial Robustness in Machine Learning (3 papers) and Artificial Intelligence in Healthcare and Education (2 papers). Dylan Slack collaborates with scholars based in United States. Dylan Slack's co-authors include Himabindu Lakkaraju, Sameer Singh, Emily Jia, Sophie Hilgard, Sorelle A. Friedler, Xiaorong Wang, Alexander J. Norquist, Joshua Schrier, Zhi Li and Vincent F. Yu and has published in prestigious journals such as The Journal of Chemical Physics, Nature Machine Intelligence and arXiv (Cornell University).

In The Last Decade

Dylan Slack

6 papers receiving 465 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.

Fooling LIME and SHAP

2020 386 citations Dylan Slack, Sophie Hilgard et al. Proceedings of the AAAI/ACM Conference on AI Ethics and Society profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dylan Slack United States	5	334	57	57	27	26	6	478
Sophie Hilgard United States	4	302 0.9×	50 0.9×	45 0.8×	20 0.7×	19 0.7×	4	431
Emily Jia United States	2	279 0.8×	45 0.8×	39 0.7×	20 0.7×	16 0.6×	4	399
Giulia Vilone Ireland	7	311 0.9×	85 1.5×	48 0.8×	29 1.1×	29 1.1×	11	490
Waddah Saeed Malaysia	4	254 0.8×	64 1.1×	20 0.4×	15 0.6×	26 1.0×	5	423
José Bobes-Bascarán Spain	4	155 0.5×	60 1.1×	36 0.6×	12 0.4×	21 0.8×	7	383
Sayash Kapoor United States	9	153 0.5×	44 0.8×	57 1.0×	12 0.4×	38 1.5×	15	435
Shayak Sen United States	7	410 1.2×	35 0.6×	98 1.7×	29 1.1×	77 3.0×	9	583
Yair Zick Singapore	11	333 1.0×	30 0.5×	103 1.8×	24 0.9×	47 1.8×	42	613
Davinder Kaur United States	5	183 0.5×	76 1.3×	104 1.8×	19 0.7×	57 2.2×	8	401
Andreas Sesing-Wagenpfeil Germany	2	226 0.7×	72 1.3×	100 1.8×	25 0.9×	22 0.8×	2	307

Countries citing papers authored by Dylan Slack

Since Specialization

Citations

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

Fields of papers citing papers by Dylan Slack

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dylan Slack

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

6 of 6 papers shown

1.

Slack, Dylan, et al.. (2023). Explaining machine learning models with interactive natural language conversations using TalkToModel. Nature Machine Intelligence. 5(8). 873–883. 46 indexed citations

2.

Najeeb, Mansoor Ani, Vincent F. Yu, Xiaorong Wang, et al.. (2022). Active meta-learning for predicting and selecting perovskite crystallization experiments. The Journal of Chemical Physics. 156(6). 64108–64108. 15 indexed citations

3.

Das, Sanjiv Ranjan, John Cijiang He, George Karypis, et al.. (2021). Context, Language Modeling, and Multimodal Data in Finance. 3(3). 52–66. 2 indexed citations

4.

Slack, Dylan, et al.. (2020). Fairness warnings and fair-MAML. 200–209. 18 indexed citations

5.

Slack, Dylan, Sophie Hilgard, Emily Jia, Sameer Singh, & Himabindu Lakkaraju. (2020). Fooling LIME and SHAP. Proceedings of the AAAI/ACM Conference on AI Ethics and Society. 180–186. 386 indexed citations breakdown →

6.

Slack, Dylan, Sophie Hilgard, Emily Jia, Sameer Singh, & Himabindu Lakkaraju. (2019). How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods.. arXiv (Cornell University). 11 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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