Daniel Schwartz

523 total citations
15 papers, 384 citations indexed

About

Daniel Schwartz is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Daniel Schwartz has authored 15 papers receiving a total of 384 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Molecular Biology, 3 papers in Computer Vision and Pattern Recognition and 3 papers in Artificial Intelligence. Recurrent topics in Daniel Schwartz's work include Protein purification and stability (3 papers), Drug Solubulity and Delivery Systems (2 papers) and Neural Networks and Applications (2 papers). Daniel Schwartz is often cited by papers focused on Protein purification and stability (3 papers), Drug Solubulity and Delivery Systems (2 papers) and Neural Networks and Applications (2 papers). Daniel Schwartz collaborates with scholars based in United States, Germany and Belgium. Daniel Schwartz's co-authors include Klaus Langer, Werner Mäntele, Winfried Haase, Vitali Vogel, J. S. Denker, Sara A. Solla, Wolfgang Frieß, Jörg Kreuter, Sylvia Wagner and Martin Michaelis and has published in prestigious journals such as Biomaterials, AIChE Journal and Journal of Pharmaceutical Sciences.

In The Last Decade

Daniel Schwartz

13 papers receiving 376 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 Schwartz United States 8 143 139 83 82 81 15 384
Sung Hun Kang South Korea 14 72 0.5× 139 1.0× 86 1.0× 84 1.0× 12 0.1× 21 482
Rishabh Singh United States 7 66 0.5× 104 0.7× 182 2.2× 23 0.3× 29 0.4× 24 579
Qiuyu Fang China 6 26 0.2× 68 0.5× 53 0.6× 77 0.9× 19 0.2× 7 303
Ziyi Lu China 10 24 0.2× 84 0.6× 104 1.3× 91 1.1× 16 0.2× 30 363
Yeqing Liu China 12 32 0.2× 171 1.2× 39 0.5× 21 0.3× 133 1.6× 28 656
Chao Xin China 11 33 0.2× 77 0.6× 72 0.9× 8 0.1× 86 1.1× 26 388
Sonja Wessel Germany 9 27 0.2× 122 0.9× 119 1.4× 75 0.9× 10 0.1× 12 656
Fengqiu Liu China 13 288 2.0× 171 1.2× 547 6.6× 20 0.2× 54 0.7× 29 895
Kaiyu Wang China 8 90 0.6× 207 1.5× 188 2.3× 9 0.1× 28 0.3× 31 502
Zongquan Zhang China 12 62 0.4× 48 0.3× 63 0.8× 20 0.2× 55 0.7× 24 361

Countries citing papers authored by Daniel Schwartz

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Schwartz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Schwartz

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

All Works

15 of 15 papers shown
1.
2.
Schwartz, Daniel, et al.. (2022). Linguistic Approach to Segmenting Source Code. 177–178. 1 indexed citations
3.
Tripathi, Satvik, et al.. (2022). RadGenNets: Deep learning-based radiogenomics model for gene mutation prediction in lung cancer. Informatics in Medicine Unlocked. 33. 101062–101062. 8 indexed citations
4.
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Schwartz, Daniel, et al.. (2021). HyNet: 3D Segmentation Using Hybrid Graph Networks. 38. 805–814. 1 indexed citations
7.
Schwartz, Daniel, et al.. (2020). Fusion Learning on Multiple-Tag RFID Measurements for Respiratory Rate Monitoring. PubMed. 1. 472–480. 2 indexed citations
8.
Haag, Scott, et al.. (2020). A fast algorithm to delineate watershed boundaries for simple geometries. Environmental Modelling & Software. 134. 104842–104842. 8 indexed citations
9.
Schwartz, Daniel, et al.. (2020). Learning Spiking Neural Network Models of Drosophila Olfaction. 1–5. 4 indexed citations
10.
Wagner, Sylvia, Florian Rothweiler, Daniel Sauer, et al.. (2009). Enhanced drug targeting by attachment of an anti αv integrin antibody to doxorubicin loaded human serum albumin nanoparticles. Biomaterials. 31(8). 2388–2398. 118 indexed citations
11.
Vogel, Vitali, et al.. (2009). Physico-chemical characterisation of PLGA nanoparticles after freeze-drying and storage. European Journal of Pharmaceutics and Biopharmaceutics. 72(2). 428–437. 111 indexed citations
12.
Frieß, Wolfgang, et al.. (2008). Liquid high concentration IgG1 antibody formulations by precipitation. Journal of Pharmaceutical Sciences. 98(9). 3043–3057. 26 indexed citations
13.
Schwartz, Daniel, S. Sofia, & Wolfgang Frieß. (2006). Integrity and stability studies of precipitated rhBMP-2 microparticles with a focus on ATR-FTIR measurements. European Journal of Pharmaceutics and Biopharmaceutics. 63(3). 241–248. 14 indexed citations
14.
Schwartz, Daniel, et al.. (1990). Exhaustive Learning. Neural Computation. 2(3). 374–385. 52 indexed citations
15.
Graf, Hans Peter, L. D. Jackel, Richard Howard, et al.. (1986). VLSI implementation of a neural network memory with several hundreds of neurons. AIP conference proceedings. 151. 182–187. 32 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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