Author

K. Srinivasan

University of California, Los Angeles

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Ecology· 2 Animal Behavior & Cognition· 1 Biochemistry· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:ecology & biodiversity· 2 Approach:experimental assay· 2 Assay:imaging· 2 Research Focus:cell type annotation· 1 Organism:mouse· 1 Research Focus:protein folding & dynamics· 1 Assay:proteomics· 1

Frequent Co-Authors

C. Dong· 1 shared Carrie C. WalliD· 1 shared D. Chen· 1 shared D. Hess· 1 shared Daniele BianchiiD· 1 shared G. Dong· 1 shared G. Feng· 1 shared J. B. Hyman· 1 shared

Activity by Year

2018-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 10 mutual co-author links

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1 shared paperBiochemistry

C. Dong

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4 papers

Recent·Most cited

Epipelagic to mesopelagic variability of acoustic backscatter in the California Current

Jérôme Guiet, Carrie C. Wall, K. Srinivasan, Daniele Bianchi

This paper analyzes 11 years of acoustic data to study spatiotemporal patterns of mid-trophic level organisms in the California Current, revealing consistent cross-shore, seasonal, and latitudinal variations.

Progress In Oceanography · Aug 5, 2026Peer-reviewedEcology

Probing the Plasticity in the Active Site of Protein N-terminal Methyltransferase 1 Using Bisubstrate Analogs

D. Chen, C. Dong, G. Dong, K. Srinivasan, J. Min, N. Noinaj, R. Huang

This study examines bisubstrate analog interactions with NTMT1 to understand active site plasticity and identifies a 4-C linker analog as the most potent inhibitor.

bioRxiv · Apr 13, 2020Biochemistry

Prediction of Algal Blooms in the Great Lakes through a Convolution Neural Network of Remote Sensing Data

K. Srinivasan, V. Duvvur, D. Hess

The paper trains a CNN on remote sensing data to predict algal bloom types in the Great Lakes with 80% accuracy using 900 images.

bioRxiv · Jan 29, 2019 · PreprintEcology

Deep Convolutional Network for Animal Sound Classification and Source Attribution using Dual Audio Recordings

T. P. Oikarinen, K. Srinivasan, O. Meisner, J. B. Hyman, S. Parmar, R. Desimone, R. Landman, G. Feng

The paper presents a convolutional neural network that classifies animal calls and identifies their source using dual audio recordings from marmosets.

bioRxiv · Oct 7, 2018Animal Behavior & Cognition