Rishi Kulkarni, PhD

Data Science | Biostatistics | Machine Learning

Rishi Kulkarni, PhD | Data Science Biostatistics Machine Learning

Selected Publications

For a full list of publications, please see my Google Scholar page.

Kulkarni, RU, et al. Analyzing nested experimental designs: A user-friendly resampling method to determine experimental significance. PLoS Computational Biology (2022)

While hierarchical experimental designs are near-ubiquitous in neuroscience and biomedical research, researchers often do not take the structure of their datasets into account while performing statistical hypothesis tests.

We present Hierarch, a Python package for analyzing nested experimental designs. Using a combination of permutation resampling and bootstrap aggregation, Hierarch can be used to perform hypothesis tests that maintain nominal Type I error rates and generate confidence intervals that maintain the nominal coverage probability without making distributional assumptions about the dataset of interest.

Kulkarni, RU, et al. A Rationally Designed, General Strategy for Membrane Orientation of Photoinduced Electron Transfer-Based Voltage-Sensitive Dyes. ACS Chemical Biology (2017)

Voltage imaging with fluorescent dyes offers promise for interrogating the complex roles of membrane potential in coordinating the activity of neurons in the brain. Yet, low sensitivity often limits the broad applicability of optical voltage indicators.

We used a combination of computational modeling and experimental screening to develop a new class of voltage-sensitive dyes with improved sensitivity. We use this new voltage indicator to monitor voltage spikes in neurons from rat hippocampus and human pluripotent-stem-cell-derived dopaminergic neurons.

Kulkarni, RU, et al. In Vivo Two-Photon Voltage Imaging with Sulfonated Rhodamine Dyes. ACS Central Science (2018)

Optical methods that rely on fluorescence for mapping changes in neuronal membrane potential in the brains of awake animals provide a powerful way to interrogate the activity of neurons that underlie neural computations ranging from sensation and perception to learning and memory.

We show sRhoVR powerfully complements electrode-based modes of neuronal activity recording in the mouse brain by recording neuronal transmembrane potentials from the neuropil of layer 2/3 of the mouse barrel cortex in concert with extracellularly recorded local field potentials (LFPs).