David Rohde

1.2k total citations
24 papers, 668 citations indexed

About

David Rohde is a scholar working on Artificial Intelligence, Information Systems and Transportation. According to data from OpenAlex, David Rohde has authored 24 papers receiving a total of 668 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 4 papers in Information Systems and 4 papers in Transportation. Recurrent topics in David Rohde's work include Bayesian Methods and Mixture Models (6 papers), Urban Transport and Accessibility (4 papers) and Recommender Systems and Techniques (4 papers). David Rohde is often cited by papers focused on Bayesian Methods and Mixture Models (6 papers), Urban Transport and Accessibility (4 papers) and Recommender Systems and Techniques (4 papers). David Rohde collaborates with scholars based in Australia, Belgium and United Kingdom. David Rohde's co-authors include Jonathan Corcoran, Sui Tao, Iderlina Mateo‐Babiano, Tiebei Li, Elin Charles‐Edwards, Prem Chhetri, Chris Turney, Alan Williams, Sean Ulm and Gentry White and has published in prestigious journals such as PLoS ONE, Monthly Notices of the Royal Astronomical Society and Journal of Machine Learning Research.

In The Last Decade

David Rohde

23 papers receiving 638 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Rohde Australia 11 388 105 93 76 67 24 668
Lindsey Conrow New Zealand 11 334 0.9× 147 1.4× 48 0.5× 167 2.2× 33 0.5× 22 584
Milan Konečný Czechia 14 83 0.2× 73 0.7× 51 0.5× 159 2.1× 13 0.2× 67 567
Alain Chiaradia Hong Kong 15 430 1.1× 356 3.4× 20 0.2× 229 3.0× 40 0.6× 36 750
Robert Stewart United States 14 238 0.6× 52 0.5× 55 0.6× 139 1.8× 24 0.4× 50 632
Amin Mobasheri Germany 14 524 1.4× 143 1.4× 56 0.6× 232 3.1× 22 0.3× 24 997
Colin Ferster Canada 15 298 0.8× 70 0.7× 20 0.2× 176 2.3× 60 0.9× 32 611
Harold Moellering United States 14 71 0.2× 66 0.6× 26 0.3× 67 0.9× 13 0.2× 42 544
Kiril Stanilov United Kingdom 11 515 1.3× 356 3.4× 39 0.4× 291 3.8× 84 1.3× 22 1.1k
José Balsa‐Barreiro Spain 15 123 0.3× 105 1.0× 55 0.6× 112 1.5× 23 0.3× 30 580
Shino Shiode United Kingdom 13 246 0.6× 73 0.7× 8 0.1× 117 1.5× 88 1.3× 32 627

Countries citing papers authored by David Rohde

Since Specialization
Citations

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

Fields of papers citing papers by David Rohde

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Rohde

This figure shows the co-authorship network connecting the top 25 collaborators of David Rohde. A scholar is included among the top collaborators of David Rohde 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 David Rohde. David Rohde 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.
Rohde, David. (2024). Why the Shooting in the Dark Method Dominates Recommender Systems Practice. 847–849. 1 indexed citations
2.
Rohde, David, et al.. (2022). Reward Optimizing Recommendation using Deep Learning and Fast Maximum Inner Product Search. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4772–4773. 1 indexed citations
3.
Vasile, Flavian, et al.. (2020). A Gentle Introduction to Recommendation as Counterfactual Policy Learning. Institutional Repository University of Antwerp (University of Antwerp). 392–393. 8 indexed citations
4.
Rohde, David, et al.. (2020). Joint Policy-Value Learning for Recommendation. Institutional Repository University of Antwerp (University of Antwerp). 1223–1233. 16 indexed citations
5.
Rohde, David & M. P. Wand. (2016). Semiparametric mean field variational Bayes: general principles and numerical issues. Journal of Machine Learning Research. 17(1). 5975–6021. 11 indexed citations
6.
Rohde, David, et al.. (2016). The association between smoke alarm presence and injury and death rates: A systematic review and meta-analysis. Fire Safety Journal. 81. 58–63. 24 indexed citations
7.
Williams, Alan, Sean Ulm, Chris Turney, David Rohde, & Gentry White. (2015). Holocene demographic changes and the emergence of complex societies in prehistoric Australia. QUT ePrints (Queensland University of Technology). 2 indexed citations
8.
Williams, Alan, Sean Ulm, Chris Turney, David Rohde, & Gentry White. (2015). Holocene Demographic Changes and the Emergence of Complex Societies in Prehistoric Australia. PLoS ONE. 10(6). e0128661–e0128661. 95 indexed citations
9.
Rohde, David & Jonathan Corcoran. (2014). MCMC methods for univariate exponential family models with intractable normalization constants. 356–359. 2 indexed citations
10.
Tao, Sui, David Rohde, & Jonathan Corcoran. (2014). Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap. Journal of Transport Geography. 41. 21–36. 120 indexed citations
11.
Tao, Sui, Jonathan Corcoran, Iderlina Mateo‐Babiano, & David Rohde. (2014). Exploring Bus Rapid Transit passenger travel behaviour using big data. Applied Geography. 53. 90–104. 72 indexed citations
12.
Rohde, David, et al.. (2013). The Sensitivity of the Number of Clusters in a Gaussian Mixture Model to Prior Distributions. Mathematics in Computer Science. 7(4). 401–420. 1 indexed citations
13.
Rohde, David & Jonathan Corcoran. (2012). Graphical tools for conditional probabilistic exploration of multivariate spatial datasets. Computers Environment and Urban Systems. 36(5). 359–370. 1 indexed citations
14.
Stimson, Robert J., et al.. (2011). Using functional economic regions to model endogenous regional performance in Australia: implications for addressing the spatial autocorrelation problem. Regional Science Policy & Practice. 3(3). 131–145. 7 indexed citations
15.
Rohde, David & Olivier Cappé. (2011). Online maximum-likelihood estimation for latent factor models. 23. 565–568. 1 indexed citations
16.
Rohde, David, Jonathan Corcoran, & Prem Chhetri. (2009). Spatial forecasting of residential urban fires: A Bayesian approach. Computers Environment and Urban Systems. 34(1). 58–69. 30 indexed citations
17.
Corcoran, Jonathan, Gary Higgs, David Rohde, & Prem Chhetri. (2009). Investigating the association between weather conditions, calendar events and socio-economic patterns with trends in fire incidence: an Australian case study. Journal of Geographical Systems. 13(2). 193–226. 50 indexed citations
18.
Rohde, David, Marcus Gallagher, M. J. Drinkwater, & Kevin A. Pimbblet. (2006). Matching of catalogues by probabilistic pattern classification. Monthly Notices of the Royal Astronomical Society. 369(1). 2–14. 12 indexed citations
19.
Kilborn, V. A., Kenji Bekki, Sarah Brough, et al.. (2005). Galaxy Groups: Proceedings from a Swinburne University Workshop. Publications of the Astronomical Society of Australia. 22(4). 326–334. 2 indexed citations
20.
Rohde, David. (1999). Black parents prevail in embryo mix-up.. PubMed. B3–B3. 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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