Daniel Ullman

1.9k total citations
32 papers, 1.1k citations indexed

About

Daniel Ullman is a scholar working on Social Psychology, Sociology and Political Science and Cognitive Neuroscience. According to data from OpenAlex, Daniel Ullman has authored 32 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Social Psychology, 8 papers in Sociology and Political Science and 8 papers in Cognitive Neuroscience. Recurrent topics in Daniel Ullman's work include Social Robot Interaction and HRI (13 papers), Ethics and Social Impacts of AI (7 papers) and Psychopathy, Forensic Psychiatry, Sexual Offending (6 papers). Daniel Ullman is often cited by papers focused on Social Robot Interaction and HRI (13 papers), Ethics and Social Impacts of AI (7 papers) and Psychopathy, Forensic Psychiatry, Sexual Offending (6 papers). Daniel Ullman collaborates with scholars based in United States and Canada. Daniel Ullman's co-authors include Bertram F. Malle, Elizabeth Phillips, Mario J. Scalora, Jodi L. Viljoen, Brian Scassellati, Xuan Zhao, Iolanda Leite, Stefanie Tellex, Nicole Salomons and Marissa McCoy and has published in prestigious journals such as Cognitive Science, Criminal Justice and Behavior and Psychological Reports.

In The Last Decade

Daniel Ullman

32 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Ullman United States 20 598 312 296 291 169 32 1.1k
Lynne Hall United Kingdom 17 495 0.8× 274 0.9× 233 0.8× 110 0.4× 81 0.5× 75 1.1k
Elizabeth S. Kim United States 11 536 0.9× 316 1.0× 51 0.2× 106 0.4× 389 2.3× 16 1.0k
Rebecca Stafford New Zealand 13 522 0.9× 372 1.2× 211 0.7× 30 0.1× 147 0.9× 15 899
Solace Shen United States 10 615 1.0× 301 1.0× 94 0.3× 29 0.1× 252 1.5× 22 887
Maha Salem United Kingdom 12 736 1.2× 442 1.4× 76 0.3× 23 0.1× 137 0.8× 30 1.0k
Aaron Powers United States 9 1.3k 2.1× 802 2.6× 168 0.6× 27 0.1× 311 1.8× 24 1.6k
Rosemarijn Looije Netherlands 17 643 1.1× 462 1.5× 70 0.2× 28 0.1× 172 1.0× 31 1.1k
Kensuke Kato Japan 8 657 1.1× 389 1.2× 120 0.4× 22 0.1× 155 0.9× 17 884
Carlos Martinho Portugal 20 1.3k 2.2× 904 2.9× 153 0.5× 32 0.1× 300 1.8× 58 1.8k
Scott Brave United States 13 705 1.2× 556 1.8× 258 0.9× 40 0.1× 394 2.3× 20 1.8k

Countries citing papers authored by Daniel Ullman

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Ullman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Ullman

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

All Works

20 of 20 papers shown
1.
Ullman, Daniel, et al.. (2021). Challenges and Opportunities for Replication Science in HRI. 110–118. 20 indexed citations
2.
Rosen, Eric, et al.. (2020). Building Plannable Representations with Mixed Reality. 11146–11153. 3 indexed citations
3.
Rosen, Eric, David Whitney, Michael Fishman, Daniel Ullman, & Stefanie Tellex. (2020). Mixed Reality as a Bidirectional Communication Interface for Human-Robot Interaction. 11431–11438. 33 indexed citations
4.
Ullman, Daniel, et al.. (2019). Virtual Reality Training Improves Real-World Performance on a Speeded Task. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 63(1). 1218–1222. 5 indexed citations
5.
Ullman, Daniel & Bertram F. Malle. (2019). Measuring Gains and Losses in Human-Robot Trust: Evidence for Differentiable Components of Trust. 618–619. 54 indexed citations
6.
Ullman, Daniel & Bertram F. Malle. (2017). Human-Robot Trust. 309–310. 22 indexed citations
7.
Phillips, Elizabeth, Daniel Ullman, Maartje M.A. de Graaf, & Bertram F. Malle. (2017). What Does A Robot Look Like?: A Multi-Site Examination of User Expectations About Robot Appearance. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 61(1). 1215–1219. 38 indexed citations
8.
Ullman, Daniel & Bertram F. Malle. (2016). The Effect of Perceived Involvement on Trust in Human-Robot Interaction. Human-Robot Interaction. 641–642. 2 indexed citations
9.
Ullman, Daniel, et al.. (2015). Evidence that Robots Trigger a Cheating Detector in Humans. 165–172. 30 indexed citations
10.
Leite, Iolanda, Marissa McCoy, Monika Lohani, et al.. (2015). Emotional Storytelling in the Classroom. 75–82. 74 indexed citations
11.
Ullman, Daniel, Iolanda Leite, Jonathan Phillips, Julia Kim‐Cohen, & Brian Scassellati. (2014). Smart Human, Smarter Robot: How Cheating Affects Perceptions of Social Agency. Cognitive Science. 36(36). 21 indexed citations
12.
Hayes, Bradley, et al.. (2014). People help robots who help others, not robots who help themselves. 255–260. 16 indexed citations
13.
Admoni, Henny, Bradley Hayes, David Feil-Seifer, Daniel Ullman, & Brian Scassellati. (2013). Are you looking at me?: perception of robot attention is mediated by gaze type and group size. Human-Robot Interaction. 389–396. 17 indexed citations
14.
Admoni, Henny, Bradley Hayes, David Feil-Seifer, Daniel Ullman, & Brian Scassellati. (2013). Dancing With Myself: The effect of majority group size on perceptions of majority and minority robot group members. Cognitive Science. 35(35). 5 indexed citations
15.
Viljoen, Jodi L., et al.. (2012). Risk and Protective Factors for Recidivism Among Juveniles Who Have Offended Sexually. Sexual Abuse. 25(4). 347–369. 36 indexed citations
16.
Latzman, Natasha E., Jodi L. Viljoen, Mario J. Scalora, & Daniel Ullman. (2011). Sexual Offending in Adolescence: A Comparison of Sibling Offenders and Nonsibling Offenders across Domains of Risk and Treatment Need. Journal of Child Sexual Abuse. 20(3). 245–263. 40 indexed citations
17.
Viljoen, Jodi L., Natasha Elkovitch, Mario J. Scalora, & Daniel Ullman. (2009). Assessment of Reoffense Risk in Adolescents Who Have Committed Sexual Offenses. Criminal Justice and Behavior. 36(10). 981–1000. 69 indexed citations
18.
DeGue, Sarah, et al.. (2008). In-Home or Out-of-Home?: Predicting Long-Term Placement Recommendations for Juvenile Offenders. International Journal of Forensic Mental Health. 7(1). 15–27. 3 indexed citations
19.
Elkovitch, Natasha, Jodi L. Viljoen, Mario J. Scalora, & Daniel Ullman. (2008). Assessing risk of reoffending in adolescents who have committed a sexual offense: the accuracy of clinical judgments after completion of risk assessment instruments. Behavioral Sciences & the Law. 26(4). 511–528. 28 indexed citations
20.
Viljoen, Jodi L., et al.. (2008). The Predictive Validity of the ERASOR, PCL:YV, and YLS/CMI Among Adolescents Who Have Sexually Offended. 1 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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