Daniel Haehn

759 total citations
31 papers, 357 citations indexed

About

Daniel Haehn is a scholar working on Computer Vision and Pattern Recognition, Biophysics and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Daniel Haehn has authored 31 papers receiving a total of 357 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 11 papers in Biophysics and 8 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Daniel Haehn's work include Cell Image Analysis Techniques (10 papers), Data Visualization and Analytics (5 papers) and Functional Brain Connectivity Studies (4 papers). Daniel Haehn is often cited by papers focused on Cell Image Analysis Techniques (10 papers), Data Visualization and Analytics (5 papers) and Functional Brain Connectivity Studies (4 papers). Daniel Haehn collaborates with scholars based in United States, Saudi Arabia and Switzerland. Daniel Haehn's co-authors include Hanspeter Pfister, James Tompkin, Jeff W. Lichtman, P. Ellen Grant, Narayanan Kasthuri, Johanna Beyer, Rudolph Pienaar, V.S. Caviness, April A. Benasich and Janine Bacic and has published in prestigious journals such as SHILAP Revista de lepidopterología, Cerebral Cortex and Sustainability.

In The Last Decade

Daniel Haehn

26 papers receiving 352 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Haehn United States	11	110	86	82	57	56	31	357
Fenqiang Zhao United States	10	110 1.0×	97 1.1×	128 1.6×	64 1.1×	166 3.0×	32	433
Suprosanna Shit Germany	10	126 1.1×	32 0.4×	91 1.1×	50 0.9×	171 3.1×	29	469
Joseph J. Capowski United States	8	81 0.7×	70 0.8×	127 1.5×	23 0.4×	113 2.0×	18	425
Gunvant Chaudhari United States	8	37 0.3×	80 0.9×	30 0.4×	13 0.2×	60 1.1×	16	314
Art Pope United States	6	206 1.9×	32 0.4×	93 1.1×	10 0.2×	15 0.3×	11	415
Monica K. Hurdal United States	11	152 1.4×	178 2.1×	18 0.2×	11 0.2×	168 3.0×	30	482
Amelio Vázquez-Reina United States	8	118 1.1×	52 0.6×	189 2.3×	4 0.1×	23 0.4×	12	308
Juntang Zhuang United States	10	39 0.4×	460 5.3×	17 0.2×	27 0.5×	135 2.4×	19	658
Kerry M. Brown United States	8	52 0.5×	60 0.7×	252 3.1×	5 0.1×	58 1.0×	8	425
Agisilaos Chartsias United Kingdom	7	206 1.9×	24 0.3×	30 0.4×	7 0.1×	188 3.4×	15	483

Countries citing papers authored by Daniel Haehn

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Haehn

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Haehn

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

Dorent, Reuben, Laura Rigolo, Nazim Haouchine, et al.. (2025). Optimizing registration uncertainty visualization to support intraoperative decision-making during brain tumor resection. International Journal of Computer Assisted Radiology and Surgery. 20(8). 1749–1757.

Rodosek, Gabi Dreo, et al.. (2024). WebGL-based Image Processing through JavaScript Injection. 1–5.

Haehn, Daniel, et al.. (2024). Non-Invasive Stress Monitoring From Video. 1–5. 1 indexed citations

Weiss, Matthew, et al.. (2024). Single-beam digital holographic reconstruction: a phase-support enhanced complex wavefront on phase-only function for twin-image elimination. Journal of Biomedical Optics. 29(7). 76502–76502. 1 indexed citations

Ning, Lipeng, et al.. (2024). AutoRL X: Automated Reinforcement Learning on the Web. ACM Transactions on Interactive Intelligent Systems. 14(4). 1–30.

Park, Tae‐Young, et al.. (2024). A review of algorithms and software for real-time electric field modeling techniques for transcranial magnetic stimulation. Biomedical Engineering Letters. 14(3). 393–405. 5 indexed citations

Chen, Lu, et al.. (2024). Generalization of CNNs on Relational Reasoning With Bar Charts. IEEE Transactions on Visualization and Computer Graphics. 31(9). 5611–5625. 1 indexed citations

Haehn, Daniel, et al.. (2024). Adversarial Text Generation using Large Language Models for Dementia Detection. PubMed. 2024. 21918–21933.

Bolton, Jeffrey, Scellig Stone, Daniel Haehn, et al.. (2023). Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?. Algorithms. 16(12). 567–567. 4 indexed citations

10.

Tan, Jack Wei Chieh, et al.. (2022). N-Tools-Browser: Web-Based Visualization of Electrocorticography Data for Epilepsy Surgery. SHILAP Revista de lepidopterología. 2. 857577–857577. 1 indexed citations

11.

Paulick, Katharina, et al.. (2022). Promoting Sustainability through Next-Generation Biologics Drug Development. Sustainability. 14(8). 4401–4401. 3 indexed citations

12.

Casser, Vincent, Kai Kang, Hanspeter Pfister, & Daniel Haehn. (2020). Fast Mitochondria Detection for Connectomics. 111–120. 12 indexed citations

13.

Lekschas, Fritz, Brant K. Peterson, Daniel Haehn, et al.. (2020). Peax: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning. Computer Graphics Forum. 39(3). 167–179. 24 indexed citations

14.

Lin, Zudi, Donglai Wei, Won-Dong Jang, et al.. (2020). Two Stream Active Query Suggestion for Active Learning in Connectomics. Lecture notes in computer science. 12363. 103–120. 10 indexed citations

15.

Haehn, Daniel, et al.. (2020). Modern Scientific Visualizations on the Web. Informatics. 7(4). 37–37. 12 indexed citations

16.

Haehn, Daniel, James Tompkin, & Hanspeter Pfister. (2018). Evaluating ‘Graphical Perception’ with CNNs. IEEE Transactions on Visualization and Computer Graphics. 25(1). 641–650. 37 indexed citations

17.

Haehn, Daniel, Seymour Knowles-Barley, Verena Kaynig, et al.. (2016). Automatic Neural Reconstruction from Petavoxel of Electron Microscopy Data. Microscopy and Microanalysis. 22(S3). 536–537. 7 indexed citations

18.

Im, Kiho, Banu Ahtam, Daniel Haehn, et al.. (2015). Altered Structural Brain Networks in Tuberous Sclerosis Complex. Cerebral Cortex. 26(5). 2046–2058. 35 indexed citations

19.

Ortiz‐Mantilla, Silvia, Nikos Makris, Matthew Gregas, et al.. (2012). Regional Infant Brain Development: An MRI-Based Morphometric Analysis in 3 to 13 Month Olds. Cerebral Cortex. 23(9). 2100–2117. 67 indexed citations

20.

Klein, Arno, Forrest Sheng Bao, Yrjö Häme, et al.. (2012). Mindboggle: automated human brain MRI feature extraction, labeling, morphometry, and online visualization. 3. 2 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