Chhaya Kulkarni

Selected Publications:

• Kulkarni, C. R., Privé, N., & Janeja, V. P. (2025). Machine learning-based variance analysis of brightness temperature in simulated satellite footprints.
• Kulkarni, C. (2025). Multi-contextual learning in spatio-temporal neighborhoods [Doctoral dissertation, University of Maryland, Baltimore County].
• Chakraborty, S., Devnath, M. K., Jabeli, A., Kulkarni, C., Boteju, G., & Wang, J. (2025). Impact of increased anthropogenic Amazon wildfires on Antarctic Sea ice melt via albedo reduction. Environmental Data Science, 4, e18.
• Kulkarni, C., Privé, N., & Janeja, V. (2024). Analyzing the variance of simulated brightness temperatures within footprints using machine learning. AGU Fall Meeting Abstracts, 2024, A43K-2134.
• Kulkarni, C., Privé, N., & Janeja, V. P. (2024). Interactive assessment of variances of high-resolution model features in digital twin simulations. Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL).
• Kulkarni, C., Tama, B. A., Schlegel, N. J., & Janeja, V. P. (2024). Anomaly detection using graph deviation networks within spatiotemporal neighborhoods: A case study in Greenland. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
• Chakraborty, S., Kulkarni, C., Jabeli, A., Wang, J., & Janeja, V. (2023). Extreme slash and burn practices over the Amazon rainforest in 2019 wreaked havoc on sea ice extent over the Antarctic. AGU Fall Meeting Abstracts, 2023, C51D-0975.
• Kulkarni, C., Janeja, V., & Schlegel, N. J. (2023). Multi-contextual learning: Analyzing melt over the Greenland ice sheet. IGARSS 2023 IEEE International Geoscience and Remote Sensing Symposium.
• Chakraborty, S., Kulkarni, C., Jabeli, A., Sampath, A., Boteju, G., & Wang, J. (2023). Understanding the role of 2019 Amazon wildfires on Antarctic Sea ice extent using data science approaches. Fragile Earth Workshop 2023, co-located with ACM SIG KDD 2023.
• Maisha, N., Kulkarni, C., Pandala, N., Zilberberg, R., Schaub, L., & Neidert, L. (2022). PEGylated polyester nanoparticles trigger adverse events in a large animal model of trauma and in naïve animals: Understanding cytokine and cellular correlations with these events. ACS Nano, 16(7), 10566–10580.
• Kulkarni, C., Dey, S., & Janeja, V. (2022). Discovery of robust distributions of COVID-19 spread. In Use of AI, Robotics and Modelling Tools to Fight COVID-19 (pp. 89–110).
• Kulkarni, C., Maisha, N., Schaub, L. J., Glaser, J., Lavik, E., & Janeja, V. (2021). Computational design map for heterogeneous experimental studies. Frontiers in Big Data, 111.
• Kulkarni, C., Maisha, N., Schaub, L. J., Glaser, J., Lavik, E., & Janeja, V. P. (2021). Temporal progression: A case study in porcine survivability through hemostatic nanoparticles. bioRxiv. https://doi.org/10.1101/2021.05.25.445627
• Kulkarni, C., Maisha, N., Schaub, L. J., Glaser, J., Lavik, E., & Janeja, V. P. (2021). Computational design map for heterogeneous experimental studies. bioRxiv. https://doi.org/10.1101/2021.05.25.445627