Daniel Schulman

38 papers receiving 1.4k citations

Peers

Daniel Schulman
Comparison fields: 5 of 131
  • Applied Psychology 287
  • Human-Computer Interaction 182
  • Social Psychology 441
  • Developmental Neuroscience 78
  • Artificial Intelligence 446
Replace Julie M. Robillard with:
Julie M. Robillard Canada
Laura Brown United Kingdom
Florian Schmitz Germany
Phil Adams United States
Julia Spaniol Canada
Sebastian Pintea Romania
Benjamin Zimmerman United States
Tracy D. Gunter United States
Mark K. Johansen United Kingdom
Christina Röcke Switzerland
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Citations per field
00.5×2.9×
Julie M. Robillard · 1×
Citations per year

Countries citing papers authored by Daniel Schulman

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Schulman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Schulman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Schulman Line = papers co-authored together Daniel Schulman links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 40 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010181
2 2013156
3 2001155
4 2002138
5 2011117
6 201078
7 200976
8 201570
9 200966
10 201162
11 200762
12 201350
13
An Intelligent Conversational Agent for Promoting Long-Term Health Behavior Change Using Motivational Interviewing.
201137
14 200928
15 200926
16 200622
17 200818
18 201214
19 198414
20 201913

About Daniel Schulman

Daniel Schulman is a scholar working on Social Psychology, Artificial Intelligence, Applied Psychology, Human-Computer Interaction and General Health Professions, having authored 40 papers that have together received 1.5k indexed citations. Recurring topics across this work include Social Robot Interaction and HRI (19 papers), AI in Service Interactions (14 papers), Speech and dialogue systems (7 papers), Digital Mental Health Interventions (6 papers), Innovative Human-Technology Interaction (4 papers), Mobile Health and mHealth Applications (3 papers), Language, Metaphor, and Cognition (3 papers) and Acupuncture Treatment Research Studies (2 papers). The work is most often cited by research in Applied Psychology (287 citations), Human-Computer Interaction (182 citations), Social Psychology (441 citations), Developmental Neuroscience (78 citations) and Artificial Intelligence (446 citations). Daniel Schulman has collaborated with scholars based in United States, United Kingdom and Iceland. Frequent co-authors include Timothy Bickmore, Candace L. Sidner, Derek M. Yellon, David S. Latchman, Langxuan Yin, Laura M. Pfeifer, Lazlo Ring, Laura Vardoulakis, Brian W. Jack and Ekaterina Sadikova. Their work appears in journals such as American Journal of Physiology-Heart and Circulatory Physiology, The Journal of Alternative and Complementary Medicine, Anesthesiology, Journal of Medical Ethics and IEEE Transactions on Affective Computing.

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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