Daniel J. Burns

1.5k total citations
87 papers, 1.1k citations indexed

About

Daniel J. Burns is a scholar working on Control and Systems Engineering, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, Daniel J. Burns has authored 87 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Control and Systems Engineering, 20 papers in Cognitive Neuroscience and 17 papers in Artificial Intelligence. Recurrent topics in Daniel J. Burns's work include Memory Processes and Influences (18 papers), Advanced Control Systems Optimization (13 papers) and Extremum Seeking Control Systems (12 papers). Daniel J. Burns is often cited by papers focused on Memory Processes and Influences (18 papers), Advanced Control Systems Optimization (13 papers) and Extremum Seeking Control Systems (12 papers). Daniel J. Burns collaborates with scholars based in United States, Canada and Switzerland. Daniel J. Burns's co-authors include Martin Guay, H. F. Helbig, Christopher R. Laughman, Joshua Hart, Kamal Youcef‐Toumi, Georg E. Fantner, Ian Underwood, Dávid Vass, David G. Payne and A. Edward O’Hara and has published in prestigious journals such as Journal of the American College of Cardiology, Journal of Experimental Psychology Learning Memory and Cognition and IEEE Transactions on Electron Devices.

In The Last Decade

Daniel J. Burns

82 papers receiving 1.0k 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 J. Burns United States 18 432 198 197 161 142 87 1.1k
Josh Tenenbaum United States 21 270 0.6× 331 1.7× 87 0.4× 100 0.6× 83 0.6× 76 1.6k
Chung Hyuk Park United States 16 235 0.5× 31 0.2× 122 0.6× 71 0.4× 91 0.6× 78 831
Yoshifumi Kitamura Japan 27 602 1.4× 51 0.3× 59 0.3× 149 0.9× 34 0.2× 231 2.3k
Chris Kyriakakis United States 15 310 0.7× 64 0.3× 144 0.7× 76 0.5× 74 0.5× 112 1.3k
Shuichi Nishio Japan 25 562 1.3× 43 0.2× 113 0.6× 211 1.3× 103 0.7× 103 1.8k
Hiroyuki Ito Japan 23 868 2.0× 90 0.5× 114 0.6× 15 0.1× 169 1.2× 141 1.8k
Semen Kurkin Russia 20 647 1.5× 32 0.2× 326 1.7× 176 1.1× 67 0.5× 157 1.3k
Miguel Arevalillo‐Herráez Spain 20 222 0.5× 101 0.5× 157 0.8× 12 0.1× 169 1.2× 114 1.6k
Jean‐Marie Normand France 18 414 1.0× 79 0.4× 21 0.1× 81 0.5× 71 0.5× 41 1.5k

Countries citing papers authored by Daniel J. Burns

Since Specialization
Citations

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

Fields of papers citing papers by Daniel J. Burns

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel J. Burns

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel J. Burns. A scholar is included among the top collaborators of Daniel J. Burns 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 J. Burns. Daniel J. Burns 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.
Chakrabarty, Ankush, Daniel J. Burns, Martin Guay, & Christopher R. Laughman. (2022). Extremum seeking controller tuning for heat pump optimization using failure-robust Bayesian optimization. Journal of Process Control. 120. 86–96. 8 indexed citations
2.
Isogai, Toshiaki, Amar Krishnaswamy, Ankit Agrawal, et al.. (2022). FEASIBILITY AND SAFETY OF SAME-DAY DISCHARGE FOLLOWING TRANSFEMORAL TRANSCATHETER AORTIC VALVE REPLACEMENT. Journal of the American College of Cardiology. 79(9). 652–652. 1 indexed citations
3.
Burns, Daniel J., et al.. (2020). Not all checking decreases memory confidence: Implications for obsessive-compulsive disorder. Journal of Behavior Therapy and Experimental Psychiatry. 69. 101573–101573. 2 indexed citations
4.
Burns, Daniel J., Christopher R. Laughman, & Martin Guay. (2016). Proportional-Integral Extremum Seeking for Optimizing Power of Vapor Compression Systems. Purdue e-Pubs (Purdue University System). 1 indexed citations
5.
Laughman, Christopher R., Hongtao Qiao, Daniel J. Burns, & Scott A. Bortoff. (2016). Dynamic Charge Management for Vapor Compression Cycles. Purdue e-Pubs (Purdue University System). 2 indexed citations
6.
Burns, Daniel J., et al.. (2014). Realtime Optimization of MPC Setpoints using Time-Varying Extremum Seeking Control for Vapor Compression Machines. Purdue e-Pubs (Purdue University System). 8 indexed citations
7.
Jain, Neera, Daniel J. Burns, & Stefano Di Cairano. (2014). Model predictive control of vapor compression systems.. 3 indexed citations
8.
Burns, Daniel J., et al.. (2013). Dying scenarios improve recall as much as survival scenarios. Memory. 22(1). 51–64. 12 indexed citations
9.
Burns, Daniel J. & Christopher R. Laughman. (2012). Extremum Seeking Control for Energy Optimization of Vapor Compression Systems. Purdue e-Pubs (Purdue University System). 27 indexed citations
10.
Hart, Joshua & Daniel J. Burns. (2012). Nothing concentrates the mind: thoughts of death improve recall. Psychonomic Bulletin & Review. 19(2). 264–269. 20 indexed citations
11.
Burns, Daniel J., et al.. (2012). Adaptive memory: The survival scenario enhances item-specific processing relative to a moving scenario. Memory. 21(6). 695–706. 27 indexed citations
12.
Erickson, Blake, et al.. (2012). Large-scale analysis of high-speed atomic force microscopy data sets using adaptive image processing. Beilstein Journal of Nanotechnology. 3. 747–758. 16 indexed citations
13.
Burns, Daniel J., et al.. (2011). Adaptive memory: Determining the proximate mechanisms responsible for the memorial advantages of survival processing.. Journal of Experimental Psychology Learning Memory and Cognition. 37(1). 206–218. 71 indexed citations
14.
Burns, Daniel J., Kamal Youcef‐Toumi, & Georg E. Fantner. (2011). Indirect identification and compensation of lateral scanner resonances in atomic force microscopes. Nanotechnology. 22(31). 315701–315701. 13 indexed citations
15.
Burns, Daniel J., et al.. (2006). An item gains and losses analysis of false memories suggests critical items receive more item-specific processing than list items.. Journal of Experimental Psychology Learning Memory and Cognition. 32(2). 277–289. 9 indexed citations
16.
Burns, Daniel J.. (2004). The Simultaneous Acquisition Effect: Simultaneous Task Learning Inhibits Memory for Order. The American Journal of Psychology. 117(2). 229–229. 1 indexed citations
17.
Burns, Daniel J.. (1996). The bizarre imagery effect and intention to learn. Psychonomic Bulletin & Review. 3(2). 254–257. 9 indexed citations
18.
Burns, Daniel J., et al.. (1993). The effects of generation on item and order retention in immediate and delayed recall. Memory & Cognition. 21(6). 846–852. 32 indexed citations
19.
Burns, Daniel J.. (1992). The consequences of generation. Journal of Memory and Language. 31(5). 615–633. 43 indexed citations
20.
Burns, Daniel J.. (1981). Truth in Testing: Arguments Examined.. Journal of legal education. 31. 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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