Sergey Kirshner

777 total citations
23 papers, 591 citations indexed

About

Sergey Kirshner is a scholar working on Artificial Intelligence, Global and Planetary Change and Water Science and Technology. According to data from OpenAlex, Sergey Kirshner has authored 23 papers receiving a total of 591 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 9 papers in Global and Planetary Change and 5 papers in Water Science and Technology. Recurrent topics in Sergey Kirshner's work include Hydrology and Drought Analysis (7 papers), Bayesian Modeling and Causal Inference (6 papers) and Climate variability and models (6 papers). Sergey Kirshner is often cited by papers focused on Hydrology and Drought Analysis (7 papers), Bayesian Modeling and Causal Inference (6 papers) and Climate variability and models (6 papers). Sergey Kirshner collaborates with scholars based in United States, Canada and Australia. Sergey Kirshner's co-authors include Padhraic Smyth, Andrew W. Robertson, Stephen P. Charles, Guobin Fu, Sebastián Moreno, Jennifer Neville, Bryson C. Bates, Shivam Tripathi, Arthur M. Greene and Rao S. Govindaraju and has published in prestigious journals such as Journal of Climate, Journal of Hydrology and Quarterly Journal of the Royal Meteorological Society.

In The Last Decade

Sergey Kirshner

23 papers receiving 549 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sergey Kirshner United States	13	354	194	130	108	69	23	591
Aljoscha Rheinwalt Germany	11	416 1.2×	299 1.5×	34 0.3×	52 0.5×	48 0.7×	19	678
A. Bakker Netherlands	13	309 0.9×	195 1.0×	78 0.6×	97 0.9×	22 0.3×	32	753
Qi Lu United States	5	337 1.0×	162 0.8×	48 0.4×	79 0.7×	68 1.0×	7	614
Jien Chen Hong Kong	7	246 0.7×	120 0.6×	42 0.3×	71 0.7×	48 0.7×	8	582
Anton H. Westveld Australia	4	509 1.4×	459 2.4×	96 0.7×	133 1.2×	186 2.7×	5	829
Paul Bodesheim Germany	9	228 0.6×	102 0.5×	132 1.0×	29 0.3×	30 0.4×	24	474
Kunio Shimizu Japan	13	119 0.3×	92 0.5×	166 1.3×	25 0.2×	84 1.2×	62	607
Geli Wang China	13	403 1.1×	336 1.7×	34 0.3×	20 0.2×	75 1.1×	47	684
L. Cinquini United States	15	262 0.7×	227 1.2×	97 0.7×	23 0.2×	27 0.4×	38	722

Countries citing papers authored by Sergey Kirshner

Since Specialization

Citations

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

Fields of papers citing papers by Sergey Kirshner

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sergey Kirshner

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

All Works

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20 of 20 papers shown

Borisyuk, Fedor, et al.. (2019). MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data. 2518–2526. 13 indexed citations

Moreno, Sebastián, Jennifer Neville, & Sergey Kirshner. (2018). Tied Kronecker Product Graph Models to Capture Variance in Network Populations. ACM Transactions on Knowledge Discovery from Data. 12(3). 1–40. 8 indexed citations

Feldman, Guy, Anindya Bhadra, & Sergey Kirshner. (2014). Bayesian feature selection in high‐dimensional regression in presence of correlated noise. Stat. 3(1). 258–272. 1 indexed citations

Moreno, Sebastián, Joseph J. Pfeiffer, Jennifer Neville, & Sergey Kirshner. (2014). A Scalable Method for Exact Sampling from Kronecker Family Models. 440–449. 5 indexed citations

Moreno, Sebastián, Jennifer Neville, & Sergey Kirshner. (2013). Learning mixed kronecker product graph models with simulated method of moments. 1052–1060. 10 indexed citations

Kirshner, Sergey. (2012). Latent Tree Copulas. 4 indexed citations

Fu, Guobin, Stephen P. Charles, & Sergey Kirshner. (2012). Daily rainfall projections from general circulation models with a downscaling nonhomogeneous hidden Markov model (NHMM) for south‐eastern Australia. Hydrological Processes. 27(25). 3663–3673. 36 indexed citations

Póczos, Barnabás, Sergey Kirshner, & Csaba Szepesvári. (2010). REGO: Rank-based Estimation of Renyi Information using Euclidean Graph Optimization. International Conference on Artificial Intelligence and Statistics. 605–612. 11 indexed citations

Moreno, Sebastián, Sergey Kirshner, Jennifer Neville, & S. V. N. Vishwanathan. (2010). Tied Kronecker product graph models to capture variance in network populations. 1137–1144. 21 indexed citations

10.

Kirshner, Sergey & Barnabás Póczos. (2008). ICA and ISA using Schweizer-Wolff measure of dependence. 464–471. 13 indexed citations

11.

Greene, Arthur M., Andrew W. Robertson, & Sergey Kirshner. (2008). Analysis of Indian monsoon daily rainfall on subseasonal to multidecadal time‐scales using a hidden Markov model. Quarterly Journal of the Royal Meteorological Society. 134(633). 875–887. 29 indexed citations

12.

Kirshner, Sergey. (2007). Learning with Tree-Averaged Densities and Distributions. Neural Information Processing Systems. 20. 761–768. 40 indexed citations

13.

Kirshner, Sergey & Padhraic Smyth. (2007). Infinite mixtures of trees. 417–423. 2 indexed citations

14.

Ihler, Alexander, Sergey Kirshner, Michael Ghil, Andrew W. Robertson, & Padhraic Smyth. (2006). Graphical models for statistical inference and data assimilation. Physica D Nonlinear Phenomena. 230(1-2). 72–87. 43 indexed citations

15.

Robertson, Andrew W., Sergey Kirshner, Padhraic Smyth, Stephen P. Charles, & Bryson C. Bates. (2006). Subseasonal‐to‐interdecadal variability of the Australian monsoon over North Queensland. Quarterly Journal of the Royal Meteorological Society. 132(615). 519–542. 55 indexed citations

16.

Kirshner, Sergey, Padhraic Smyth, & Andrew W. Robertson. (2004). Conditional Chow-Liu tree structures for modeling discrete-valued vector time series. Uncertainty in Artificial Intelligence. 317–324. 28 indexed citations

17.

Robertson, Andrew W., Sergey Kirshner, & Padhraic Smyth. (2004). Downscaling of Daily Rainfall Occurrence over Northeast Brazil Using a Hidden Markov Model. Journal of Climate. 17(22). 4407–4424. 162 indexed citations

18.

Kirshner, Sergey, et al.. (2003). Unsupervised learning with permuted data. International Conference on Machine Learning. 345–352. 2 indexed citations

19.

Kirshner, Sergey, Igor V. Cadez, Padhraic Smyth, Chandrika Kamath, & Erick Cantú‐Paz. (2003). Probabilistic model-based detection of bent-double radio galaxies. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 2. 499–502. 1 indexed citations

20.

Kirshner, Sergey, Igor V. Cadez, Padhraic Smyth, & Chandrika Kamath. (2002). Learning to Classify Galaxy Shapes Using the EM Algorithm. Neural Information Processing Systems. 15. 1521–1528. 4 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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