Daniel Cullina

862 total citations
25 papers, 298 citations indexed

About

Daniel Cullina is a scholar working on Artificial Intelligence, Molecular Biology and Computer Networks and Communications. According to data from OpenAlex, Daniel Cullina has authored 25 papers receiving a total of 298 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 8 papers in Molecular Biology and 7 papers in Computer Networks and Communications. Recurrent topics in Daniel Cullina's work include Advanced Graph Neural Networks (8 papers), DNA and Biological Computing (7 papers) and Advanced biosensing and bioanalysis techniques (5 papers). Daniel Cullina is often cited by papers focused on Advanced Graph Neural Networks (8 papers), DNA and Biological Computing (7 papers) and Advanced biosensing and bioanalysis techniques (5 papers). Daniel Cullina collaborates with scholars based in United States, Switzerland and Italy. Daniel Cullina's co-authors include Negar Kiyavash, Prateek Mittal, Arjun Nitin Bhagoji, Matthias Grossglauser, H. Vincent Poor, Vikash Sehwag, Ankur A. Kulkarni, Liwei Song, Mung Chiang and Chawin Sitawarin and has published in prestigious journals such as IEEE Transactions on Information Theory, Real-Time Systems and ACM SIGMETRICS Performance Evaluation Review.

In The Last Decade

Daniel Cullina

23 papers receiving 277 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Cullina United States	10	220	85	61	52	42	25	298
Anahí Gajardo Chile	6	148 0.7×	60 0.7×	59 1.0×	27 0.5×	46 1.1×	14	350
Hongliang Fei United States	15	313 1.4×	160 1.9×	41 0.7×	22 0.4×	54 1.3×	43	446
Francisco Claude Chile	12	264 1.2×	95 1.1×	73 1.2×	55 1.1×	101 2.4×	21	341
Song Bian China	4	119 0.5×	75 0.9×	29 0.5×	17 0.3×	34 0.8×	4	209
Zheng Gao United States	8	96 0.4×	27 0.3×	45 0.7×	15 0.3×	35 0.8×	36	220
Lorenzo Sarti Italy	10	164 0.7×	201 2.4×	24 0.4×	34 0.7×	17 0.4×	17	314
Mukund Narasimhan United States	10	159 0.7×	84 1.0×	10 0.2×	39 0.8×	36 0.9×	14	272
Hanan Ayad Canada	4	232 1.1×	118 1.4×	44 0.7×	48 0.9×	18 0.4×	4	377
Srinivas Vadrevu United States	9	202 0.9×	55 0.6×	15 0.2×	27 0.5×	46 1.1×	17	287

Countries citing papers authored by Daniel Cullina

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Cullina

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Cullina

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Cullina. A scholar is included among the top collaborators of Daniel Cullina 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 Cullina. Daniel Cullina 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

Cullina, Daniel, et al.. (2020). Achievability of nearly-exact alignment for correlated Gaussian databases. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1230–1235. 6 indexed citations

Cullina, Daniel, Negar Kiyavash, Prateek Mittal, & H. Vincent Poor. (2020). Partial Recovery of Erdős-Rényi Graph Alignment via k-Core Alignment. 99–100. 6 indexed citations

Cullina, Daniel, Negar Kiyavash, Prateek Mittal, & H. Vincent Poor. (2019). Partial Recovery of Erdðs-Rényi Graph Alignment via k-Core Alignment. Proceedings of the ACM on Measurement and Analysis of Computing Systems. 3(3). 1–21. 12 indexed citations

Bhagoji, Arjun Nitin, Daniel Cullina, & Prateek Mittal. (2019). Lower Bounds on Adversarial Robustness from Optimal Transport. arXiv (Cornell University). 32. 7496–7508. 5 indexed citations

Cullina, Daniel, et al.. (2019). Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdős-Rényi Graphs. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 97–98. 10 indexed citations

Cullina, Daniel, et al.. (2019). Database Alignment with Gaussian Features. arXiv (Cornell University). 3225–3233. 6 indexed citations

Cullina, Daniel, et al.. (2019). Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdos-Rényi Graphs. Proceedings of the ACM on Measurement and Analysis of Computing Systems. 3(2). 1–25. 19 indexed citations

Cullina, Daniel, et al.. (2019). Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdős-Rényi Graphs. ACM SIGMETRICS Performance Evaluation Review. 47(1). 96–97. 4 indexed citations

Sehwag, Vikash, Arjun Nitin Bhagoji, Liwei Song, et al.. (2019). Analyzing the Robustness of Open-World Machine Learning. 105–116. 29 indexed citations

10.

Cullina, Daniel, Arjun Nitin Bhagoji, & Prateek Mittal. (2018). PAC-learning in the presence of adversaries. Neural Information Processing Systems. 31. 228–239. 13 indexed citations

11.

Bhagoji, Arjun Nitin, Daniel Cullina, & Prateek Mittal. (2017). Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers.. arXiv (Cornell University). 51 indexed citations

12.

Cullina, Daniel & Negar Kiyavash. (2016). Generalized Sphere-Packing Bounds on the Size of Codes for Combinatorial Channels. IEEE Transactions on Information Theory. 62(8). 4454–4465. 8 indexed citations

13.

Melani, Alessandra, Renato Mancuso, Daniel Cullina, Marco Caccamo, & Lothar Thiele. (2016). Speed Optimization for Tasks with Two Resources. 1072–1077. 3 indexed citations

14.

Cullina, Daniel & Negar Kiyavash. (2016). Improved Achievability and Converse Bounds for Erdos-Renyi Graph Matching. 63–72. 35 indexed citations

15.

Melani, Alessandra, Renato Mancuso, Daniel Cullina, Marco Caccamo, & Lothar Thiele. (2015). Resource Speed Optimization for Two-Stage Flow-Shop Scheduling. 1 indexed citations

16.

Cullina, Daniel. (2015). Constant composition deletion correcting codes.

17.

Cullina, Daniel & Negar Kiyavash. (2014). An Improvement to Levenshtein's Upper Bound on the Cardinality of Deletion Correcting Codes. IEEE Transactions on Information Theory. 60(7). 3862–3870. 21 indexed citations

18.

Cullina, Daniel & Negar Kiyavash. (2014). Generalized sphere-packing upper bounds on the size of codes for combinatorial channels. 10. 1266–1270. 3 indexed citations

19.

Cullina, Daniel & Negar Kiyavash. (2013). An improvement to Levenshtein's upper bound on the cardinality of deletion correcting codes. 26. 699–703. 5 indexed citations

20.

Cullina, Daniel, Ankur A. Kulkarni, & Negar Kiyavash. (2012). A coloring approach to constructing deletion correcting codes from constant weight subgraphs. 10. 513–517. 9 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.

Explore authors with similar magnitude of impact