Justin D. Weisz

2.7k total citations · 1 hit paper
37 papers, 856 citations indexed

About

Justin D. Weisz is a scholar working on Artificial Intelligence, Safety Research and Information Systems. According to data from OpenAlex, Justin D. Weisz has authored 37 papers receiving a total of 856 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 11 papers in Safety Research and 8 papers in Information Systems. Recurrent topics in Justin D. Weisz's work include Ethics and Social Impacts of AI (11 papers), AI in Service Interactions (7 papers) and Explainable Artificial Intelligence (XAI) (6 papers). Justin D. Weisz is often cited by papers focused on Ethics and Social Impacts of AI (11 papers), AI in Service Interactions (7 papers) and Explainable Artificial Intelligence (XAI) (6 papers). Justin D. Weisz collaborates with scholars based in United States, Germany and India. Justin D. Weisz's co-authors include Michael Müller, Q. Vera Liao, Zahra Ashktorab, Mohit Jain, Dakuo Wang, Casey Dugan, Stephanie Houde, Philipp Geyer, Parikshit Ram and Horst Samulowitz and has published in prestigious journals such as IBM Journal of Research and Development, ACM Transactions on Computer-Human Interaction and Proceedings of the ACM on Human-Computer Interaction.

In The Last Decade

Justin D. Weisz

35 papers receiving 819 citations

Hit Papers

align trajectories sort by overperformance

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.

Investigating Explainability of Generative AI for Code through Scenario-based Design

2022 116 citations Jiao Sun, Q. Vera Liao et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Justin D. Weisz United States	13	397	143	137	134	128	37	856
Dan Weld United States	6	450 1.1×	223 1.6×	109 0.8×	81 0.6×	161 1.3×	6	973
Danding Wang China	8	698 1.8×	290 2.0×	151 1.1×	146 1.1×	91 0.7×	18	1.0k
Tongshuang Wu United States	17	608 1.5×	93 0.7×	133 1.0×	55 0.4×	63 0.5×	48	971
Ashraf Abdul Singapore	6	657 1.7×	304 2.1×	99 0.7×	59 0.4×	90 0.7×	7	956
Todd Kulesza United States	11	1.0k 2.6×	314 2.2×	194 1.4×	92 0.7×	121 0.9×	18	1.6k
Gagan Bansal United States	11	471 1.2×	239 1.7×	62 0.5×	55 0.4×	159 1.2×	23	770
Toby Jia-Jun Li United States	16	319 0.8×	47 0.3×	150 1.1×	82 0.6×	38 0.3×	64	722
Ujwal Gadiraju Netherlands	23	734 1.8×	188 1.3×	268 2.0×	301 2.2×	165 1.3×	112	1.6k
Nava Tintarev United Kingdom	17	688 1.7×	111 0.8×	697 5.1×	167 1.2×	64 0.5×	49	1.3k
Huahai Yang United States	16	397 1.0×	22 0.2×	146 1.1×	150 1.1×	117 0.9×	31	757

Countries citing papers authored by Justin D. Weisz

Since Specialization

Citations

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

Fields of papers citing papers by Justin D. Weisz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Justin D. Weisz. 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 Justin D. Weisz. The network helps show where Justin D. Weisz may publish in the future.

Co-authorship network of co-authors of Justin D. Weisz

This figure shows the co-authorship network connecting the top 25 collaborators of Justin D. Weisz. A scholar is included among the top collaborators of Justin D. Weisz 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 Justin D. Weisz. Justin D. Weisz 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.

Weisz, Justin D., et al.. (2026). AgentCraft: Workshop on Developing Trustworthy Agentic AI Systems. 240–242.

2.

Anderson, A. W., et al.. (2025). Towards Personalized and Contextualized Code Explanations. 120–125. 1 indexed citations

3.

Ehsan, Upol, Elizabeth Anne Watkins, Philipp Wintersberger, et al.. (2025). New Frontiers of Human-centered Explainable AI (HCXAI): Participatory Civic AI, Benchmarking LLMs, XAI Hallucinations, and Responsible AI Audits. 1–6. 3 indexed citations

4.

Mokryn, Osnat, Orit Shaer, Werner Geyer, et al.. (2025). HAI-GEN 2025: 6th Workshop on Human-AI Co-Creation with Generative Models. 179–182. 2 indexed citations

5.

Weisz, Justin D., et al.. (2025). Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprise. 1–13. 3 indexed citations

6.

Houde, Stephanie, et al.. (2025). Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creation. 1–18. 3 indexed citations

7.

Müller, Michael, Justin D. Weisz, Stephanie Houde, & Steven Ross. (2024). Drinking Chai with Your (AI) Programming Partner: Value Tensions in the Tokenization of Future Human-AI Collaborative Work. 1–15. 9 indexed citations

8.

Houde, Stephanie, et al.. (2024). AI and the Future of Collaborative Work: Group Ideation with an LLM in a Virtual Canvas. 1–14. 18 indexed citations

9.

Geyer, Werner, Mary Lou Maher, Justin D. Weisz, Daniel Buschek, & Lydia B. Chilton. (2024). HAI-GEN 2024: 5th Workshop on Human-AI Co-Creation with Generative Models. 122–124. 2 indexed citations

10.

Piorkowski, David, et al.. (2023). Rebalancing Worker Initiative and AI Initiative in Future Work: Four Task Dimensions. 1–16. 4 indexed citations

11.

Ross, Steven, et al.. (2023). The Programmer’s Assistant User Experience. 102–104. 1 indexed citations

12.

Ehsan, Upol, Philipp Wintersberger, Elizabeth Anne Watkins, et al.. (2023). Human-Centered Explainable AI (HCXAI): Coming of Age. 1–7. 14 indexed citations

13.

Sun, Jiao, Q. Vera Liao, Michael Müller, et al.. (2022). Investigating Explainability of Generative AI for Code through Scenario-based Design. 212–228. 116 indexed citations breakdown →

14.

Weisz, Justin D., Mary Lou Maher, Hendrik Strobelt, et al.. (2022). HAI-GEN 2022: 3rd Workshop on Human-AI Co-Creation with Generative Models. 4–6. 7 indexed citations

15.

Müller, Michael, April Yi Wang, Steven Ross, et al.. (2021). How Data Scientists Improve Generated Code Documentation in Jupyter Notebooks.. 4 indexed citations

16.

Geyer, Werner, Lydia B. Chilton, Justin D. Weisz, & Mary Lou Maher. (2021). HAI-GEN 2021: 2nd Workshop on Human-AI Co-Creation with Generative Models. 15–17. 9 indexed citations

17.

Wang, Dakuo, Parikshit Ram, Sijia Liu, et al.. (2020). AutoAI. 77–78. 19 indexed citations

18.

Weisz, Justin D., Sara Kiesler, Hui Zhang, et al.. (2018). Watching Together: Integrating Conversation with Video. Figshare.

19.

Gowda, Mahanth, Justin Manweiler, Ashutosh Dhekne, Romit Roy Choudhury, & Justin D. Weisz. (2016). Tracking drone orientation with multiple GPS receivers. 280–293. 37 indexed citations

20.

Ashoori, Maryam, Rachel Bellamy, & Justin D. Weisz. (2015). Creating the Mood. 2001–2006. 2 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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