Publications

NB: Convention on authors listing order differs by field.
= corresponding author; * = equal contribution.

Online resources

NeuroMechFly documentation, complete with tutorials and a workshop recording.
I wrote a tutorial on doing machine learning with custom datasets and using transfer learning to facilitate training
I wrote a tutorial on processing geospatial data with Python (Caveat: As I wrote this tutorial quite a while ago, it might not follow current best practices. Also, know that I was only a master student when I wrote it.)

Wang-Chen, S., Stimpfling, V. A., Lam, T. K. C., Özdil, P. G., Genoud, L., Hurtak, F., & Ramdya, P. (2024). NeuroMechFly v2: a framework for simulating embodied sensorimotor control in adult Drosophila. Nature Methods.
Braun, J.^*, Hurtak, F.^*, Wang-Chen, S., & Ramdya, P. (2024). Descending networks transform command signals into population motor control. Nature, 630, 686–694.

Khan, A., Huerta, E. A., Wang, S., Gruendl, R., Jennings, E., & Zheng, H. (2019). Deep learning at scale for the construction of galaxy catalogs in the Dark Energy Survey. Physics Letters B, 795, 248–258.
Rebei, A., Huerta, E. A., Wang, S., Habib, S., Haas, R., Johnson, D., & George, D. (2019). Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers. Physical Review D, 100(4), 044025.

Accessing agriculture productivity and sustainability, US 2022/0061236 A1, 2021.
Systems and methods for quantifying agroecosystem variables through multi-tier scaling from ground data, to mobile platforms, and to satellite observations, WO 2022/011236 A3, 2021.
Apparatus and method for crop yield prediction US 2022/0067614 A1, 2019.