David Haws

866 total citations
27 papers, 245 citations indexed

About

David Haws is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Genetics. According to data from OpenAlex, David Haws has authored 27 papers receiving a total of 245 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 7 papers in Computational Theory and Mathematics and 5 papers in Genetics. Recurrent topics in David Haws's work include Speech Recognition and Synthesis (8 papers), Polynomial and algebraic computation (4 papers) and Bayesian Modeling and Causal Inference (4 papers). David Haws is often cited by papers focused on Speech Recognition and Synthesis (8 papers), Polynomial and algebraic computation (4 papers) and Bayesian Modeling and Causal Inference (4 papers). David Haws collaborates with scholars based in United States, Czechia and Austria. David Haws's co-authors include Ruriko Yoshida, Sen-ching S. Cheung, Milan Studený, Peter Huggins, Jesús A. De Loera, Raymond Hemmecke, Irina Rish, Dan He, James Cussens and Xiaodong Cui and has published in prestigious journals such as PLoS ONE, BMC Bioinformatics and Systematic Biology.

In The Last Decade

David Haws

24 papers receiving 224 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David Haws United States	10	73	46	45	35	34	27	245
Gautam Garai India	8	116 1.6×	41 0.9×	28 0.6×	23 0.7×	11 0.3×	18	299
Peng Di China	10	124 1.7×	55 1.2×	15 0.3×	51 1.5×	19 0.6×	49	397
Jinfeng Pan China	12	67 0.9×	128 2.8×	46 1.0×	20 0.6×	13 0.4×	41	372
Luís M. S.Russo Portugal	9	187 2.6×	25 0.5×	112 2.5×	14 0.4×	8 0.2×	28	277
Naveen Sivadasan India	10	37 0.5×	41 0.9×	131 2.9×	4 0.1×	24 0.7×	34	293
E. G. Rajan India	10	81 1.1×	96 2.1×	14 0.3×	98 2.8×	16 0.5×	42	344
Tingting Zhao China	12	116 1.6×	274 6.0×	54 1.2×	14 0.4×	9 0.3×	31	439
Kurt Mehlhorn Germany	10	99 1.4×	66 1.4×	109 2.4×	65 1.9×	3 0.1×	29	297
Przemysław Spurek Poland	10	151 2.1×	103 2.2×	10 0.2×	42 1.2×	3 0.1×	41	358
A. Benkrid United Kingdom	12	91 1.2×	150 3.3×	39 0.9×	69 2.0×	3 0.1×	31	372

Countries citing papers authored by David Haws

Since Specialization

Citations

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

Fields of papers citing papers by David Haws

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Haws

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Saon, George, et al.. (2022). VQ-T: RNN Transducers using Vector-Quantized Prediction Network States. Interspeech 2022. 1656–1660.

Haws, David, et al.. (2022). Transplantation of Conversational Speaking Style with Interjections in Sequence-to-Sequence Speech Synthesis. Interspeech 2022. 5488–5492. 3 indexed citations

Shechtman, Slava, Raul Castro Fernandez, A. S. Sorin, & David Haws. (2021). Synthesis of Expressive Speaking Styles with Limited Training Data in a Multi-Speaker, Prosody-Controllable Sequence-to-Sequence Architecture. 4693–4697. 7 indexed citations

Cui, Xiaodong, Brian Kingsbury, George Saon, David Haws, & Zoltán Tüske. (2021). Reducing Exposure Bias in Training Recurrent Neural Network Transducers. 1802–1806. 2 indexed citations

Kurata, Gakuto, George Saon, Brian Kingsbury, David Haws, & Zoltán Tüske. (2021). Improving Customization of Neural Transducers by Mitigating Acoustic Mismatch of Synthesized Audio. 2027–2031. 8 indexed citations

Cussens, James, David Haws, & Milan Studený. (2015). Polyhedral aspects of score equivalence in Bayesian network structure learning. arXiv (Cornell University). 14 indexed citations

Haws, David, Irina Rish, Simon Teyssèdre, et al.. (2015). Variable-Selection Emerges on Top in Empirical Comparison of Whole-Genome Complex-Trait Prediction Methods. PLoS ONE. 10(10). e0138903–e0138903. 17 indexed citations

Studený, Milan & David Haws. (2013). Learning Bayesian network structure: Towards the essential graph by integer linear programming tools. International Journal of Approximate Reasoning. 55(4). 1043–1071. 11 indexed citations

Yoshida, Ruriko, et al.. (2013). Approximate Techniques in Solving Optimal Camera Placement Problems. International Journal of Distributed Sensor Networks. 9(11). 241913–241913. 48 indexed citations

10.

Studený, Milan & David Haws. (2013). On Polyhedral Approximations of Polytopes for Learning Bayesian Networks. 4(1). 5 indexed citations

11.

Haws, David, et al.. (2013). Markov degree of the three-state toric homogeneous Markov chain model. Beiträge zur Algebra und Geometrie / Contributions to Algebra and Geometry. 55(1). 161–188. 1 indexed citations

12.

Haws, David, et al.. (2012). Degree Bounds for a Minimal Markov Basis for the Three-state Toric Homogeneous Markov Chain Model. 63–98. 3 indexed citations

13.

Studený, Milan, et al.. (2012). Polyhedral Approach to Statistical Learning Graphical Models. ASEP. 346–372. 3 indexed citations

14.

Haws, David, Peter Huggins, Eric M. O’Neill, David W. Weisrock, & Ruriko Yoshida. (2012). A support vector machine based test for incongruence between sets of trees in tree space. BMC Bioinformatics. 13(1). 210–210. 7 indexed citations

15.

Yoshida, Ruriko, et al.. (2012). Estimating the number of zero-one multi-way tables via sequential importance sampling. Annals of the Institute of Statistical Mathematics. 65(4). 763–783.

16.

Huggins, Peter, et al.. (2011). Bayes Estimators for Phylogenetic Reconstruction. Systematic Biology. 60(4). 528–540. 11 indexed citations

17.

Haws, David, et al.. (2011). Optimality of the Neighbor Joining Algorithm and Faces of the Balanced Minimum Evolution Polytope. Bulletin of Mathematical Biology. 73(11). 2627–2648. 21 indexed citations

18.

Loera, Jesús A. De, David Haws, & Matthias Köppe. (2008). Ehrhart Polynomials of Matroid Polytopes and Polymatroids. Discrete & Computational Geometry. 42(4). 703–704. 2 indexed citations

19.

Loera, Jesús A. De, David Haws, Raymond Hemmecke, Peter Huggins, & Ruriko Yoshida. (2005). A computational study of integer programming algorithms based on Barvinok's rational functions. Discrete Optimization. 2(2). 135–144. 10 indexed citations

20.

Loera, Jesús A. De, David Haws, Raymond Hemmecke, et al.. (2004). Short rational functions for toric algebra and applications. Journal of Symbolic Computation. 38(2). 959–973. 26 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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