Mariya Chavarha

2.2k total citations
21 papers, 779 citations indexed

About

Mariya Chavarha is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cognitive Neuroscience. According to data from OpenAlex, Mariya Chavarha has authored 21 papers receiving a total of 779 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 9 papers in Cellular and Molecular Neuroscience and 6 papers in Cognitive Neuroscience. Recurrent topics in Mariya Chavarha's work include Neuroscience and Neural Engineering (7 papers), Neural dynamics and brain function (6 papers) and Photoreceptor and optogenetics research (6 papers). Mariya Chavarha is often cited by papers focused on Neuroscience and Neural Engineering (7 papers), Neural dynamics and brain function (6 papers) and Photoreceptor and optogenetics research (6 papers). Mariya Chavarha collaborates with scholars based in United States, France and Canada. Mariya Chavarha's co-authors include Michael Z. Lin, François St-Pierre, Stephen W. Evans, Dongqing Shi, Shankar B. Rananavare, Stephen B. Hall, Katalin Tóth, Simon Chamberland, Shuo Chen and Na Ji and has published in prestigious journals such as Cell, Journal of the American Chemical Society and Nature Communications.

In The Last Decade

Mariya Chavarha

18 papers receiving 772 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mariya Chavarha United States 12 405 236 233 218 97 21 779
Adrian Negrean United States 10 296 0.7× 216 0.9× 151 0.6× 169 0.8× 162 1.7× 14 642
Or A. Shemesh United States 10 343 0.8× 89 0.4× 181 0.8× 167 0.8× 56 0.6× 12 709
Qiuyuan Zhong China 8 164 0.4× 257 1.1× 147 0.6× 161 0.7× 89 0.9× 13 534
Grażyna Palczewska United States 23 315 0.8× 301 1.3× 1.0k 4.4× 57 0.3× 181 1.9× 47 1.5k
Lamiae Abdeladim France 9 169 0.4× 221 0.9× 149 0.6× 86 0.4× 172 1.8× 14 635
Raymond Molloy United States 6 345 0.9× 243 1.0× 311 1.3× 122 0.6× 179 1.8× 8 772
Bradley J. Baker South Korea 19 889 2.2× 265 1.1× 458 2.0× 358 1.6× 82 0.8× 45 1.1k
Kaspar Podgorski United States 13 451 1.1× 289 1.2× 329 1.4× 230 1.1× 152 1.6× 17 897
Yuki Bando Japan 13 593 1.5× 128 0.5× 351 1.5× 336 1.5× 63 0.6× 18 958
Charles N. Rafferty United States 18 335 0.8× 533 2.3× 379 1.6× 42 0.2× 99 1.0× 30 1.0k

Countries citing papers authored by Mariya Chavarha

Since Specialization
Citations

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

Fields of papers citing papers by Mariya Chavarha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mariya Chavarha

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

All Works

20 of 20 papers shown
1.
Thomas, Neil, David Belanger, Chenling Xu, et al.. (2025). Engineering highly active nuclease enzymes with machine learning and high-throughput screening. Cell Systems. 16(3). 101236–101236. 6 indexed citations
2.
Zhang, Yan, Rubén Sánchez Gómez, Stephen M. Evans, et al.. (2025). Imaging sensory transmission and neuronal plasticity in primary sensory neurons with a positively tuned voltage indicator. Nature Communications. 16(1). 6396–6396.
3.
Liao, Zhenrui, Kevin C. Gonzalez, Natalie McClain, et al.. (2024). Functional architecture of intracellular oscillations in hippocampal dendrites. Nature Communications. 15(1). 6295–6295. 5 indexed citations
4.
Bryant, Drew, Xiaoyu Qu, Avinava Dubey, et al.. (2024). Karyotype AI for Precision Oncology. Blood. 144(Supplement 1). 1544–1544.
5.
Fang, Min, Drew Bryant, Xiaoyu Qu, et al.. (2023). 42. Automated deep aberration detection from chromosome karyotype images. Cancer Genetics. 278-279. 13–13. 1 indexed citations
6.
Gibbons, Michael C., Mariya Chavarha, Shirley Shao, et al.. (2022). Million spot binding array platform for exploring and optimizing multiple simultaneous detection events. STAR Protocols. 3(4). 101829–101829. 1 indexed citations
7.
Kim, Benjamin, Haodi Wu, Yukun Hao, et al.. (2022). A red fluorescent protein with improved monomericity enables ratiometric voltage imaging with ASAP3. Scientific Reports. 12(1). 3678–3678. 11 indexed citations
8.
Wu, Jianglai, Yajie Liang, Shuo Chen, et al.. (2020). Kilohertz two-photon fluorescence microscopy imaging of neural activity in vivo. Nature Methods. 17(3). 287–290. 150 indexed citations
9.
Chavarha, Mariya, et al.. (2020). Two-Photon Voltage Imaging of Spontaneous Activity from Multiple Neurons Reveals Network Activity in Brain Tissue. iScience. 23(8). 101363–101363. 18 indexed citations
10.
Villette, Vincent, Mariya Chavarha, Ivan K. Dimov, et al.. (2019). Ultrafast Two-Photon Imaging of a High-Gain Voltage Indicator in Awake Behaving Mice. Cell. 179(7). 1590–1608.e23. 220 indexed citations
11.
Kumar, Kamlesh, et al.. (2018). The Lγ Phase of Pulmonary Surfactant. Langmuir. 34(22). 6601–6611. 12 indexed citations
12.
Dieudonné, Stéphane, Walther Akemann, Laurent Bourdieu, et al.. (2018). Multiphoton Ultrafast LOcal Volume Excitation (ULOVE) Through Acousto-optic Wavefront Shaping to Record and Control Neuronal Activity. BTh4C.2–BTh4C.2. 1 indexed citations
13.
St-Pierre, François, Mariya Chavarha, & Michael Z. Lin. (2015). Designs and sensing mechanisms of genetically encoded fluorescent voltage indicators. Current Opinion in Chemical Biology. 27. 31–38. 70 indexed citations
14.
Chavarha, Mariya, et al.. (2015). Hydrophobic Surfactant Proteins Strongly Induce Negative Curvature. Biophysical Journal. 109(1). 95–105. 26 indexed citations
15.
Fan, Linlin Z., Xin Zhou, Mariya Chavarha, & Michael Z. Lin. (2014). Improving Optical Control of Protein Activity by Light-Induced Fluorescent Protein Dissociation. Biophysical Journal. 106(2). 382a–382a. 1 indexed citations
16.
Chavarha, Mariya, et al.. (2013). An Anionic Phospholipid Enables the Hydrophobic Surfactant Proteins to Alter Spontaneous Curvature. Biophysical Journal. 104(3). 594–603. 16 indexed citations
17.
Chavarha, Mariya, et al.. (2012). Differential Effects of the Hydrophobic Surfactant Proteins on the Formation of Inverse Bicontinuous Cubic Phases. Langmuir. 28(48). 16596–16604. 23 indexed citations
18.
Chavarha, Mariya, et al.. (2010). Hydrophobic Surfactant Proteins Induce a Phosphatidylethanolamine to Form Cubic Phases. Biophysical Journal. 98(8). 1549–1557. 35 indexed citations
19.
Duncan, James A., et al.. (2008). Secondary Orbital Effect in the Electrocyclic Ring Closure of 7-Azahepta-1,2,4,6-tetraene—A CASSCF Molecular Orbital Study. Journal of the American Chemical Society. 130(21). 6740–6748. 19 indexed citations
20.
Lochner, Janis E., et al.. (2008). Efficient copackaging and cotransport yields postsynaptic colocalization of neuromodulators associated with synaptic plasticity. Developmental Neurobiology. 68(10). 1243–1256. 33 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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