Research


Identifying and Mitigating Statistical Biases in Neural Models of Tuning and Functional Coupling
P. S. Sachdeva, J. A. Livezey, M. E. Dougherty, S. Bhattacharyya, K. E. Bouchard


tl;dr: We investigated the interpretability of models for neural activity, discovered biases in underconstrained models that may harm interpretability, and developed methods to correct for those biases.

Accurate and Scalable Matching of Translators to Displaced Persons for Overcoming Language Barriers
D. Agarwal, Y. Baba, P. S. Sachdeva, T. Tandon, T. Vetterli, A. Alghunaim
[paper] [talk]
tl;dr: We showed that a lightweight and scalable logistic regression model could accurately match refugees and asylee seekers to volunteer translators via Tarjimly, the mobile application.

Accurate inference in parametric models reshapes neuroscientific interpretation and improves data-driven discovery
P. S. Sachdeva, J. A. Livezey, M. E. Dougherty, B. M. Gu, J. D. Berke, K. E. Bouchard
[preprint] [abstract]

tl;dr: We showed that improved parametric inference in common neuroscience models drastically reduces the size of the models at no cost to predictive accuracy, thereby changing their neuroscientific interpretation.

Heterogeneous synaptic weighting improves neural coding in the presence of common noise
P. S. Sachdeva, J. A. Livezey, M. R. DeWeese
[paper] [preprint] [abstract] [poster]

tl;dr: We showed that a neural population coding for a stimulus can overcome a common noise source as long as the population possesses diverse synaptic weights, even if those weights amplify the noise.

PyUoI: The Union of Intersections Framework in Python
P. S. Sachdeva, J. A. Livezey, A. J. Tritt, K. E. Bouchard
[paper] [github] [docs]

tl;dr: We developed a Python package that contains implementations for the Union of Intersections, a machine learning framework capable of building highly sparse and predictive models with minimal bias.

Sparse, Interpretable, and Predictive Functional Connectomics with UoILasso
P. S. Sachdeva, S. Bhattacharyya, K. E. Bouchard
[paper] [slides]

tl;dr: We constructed functional connectivity networks from neural data with fewer edges but equally good predictive performance, compared to standard procedures.

On the Stability of Strange Dwarf Hybrid Stars
M. G. Alford, S. P. Harris, P. S. Sachdeva
[paper] [abstract] [poster]

tl;dr: We showed that certain stars, previously thought to be stable, are actually unstable.

Beam Single Spin Asymmetries in Electron-Proton Scattering
P. S. Sachdeva, P. G. Blunden, W. Melnitchouk
[report] [poster]

tl;dr: We calculated some useful observable quantities for accelerator experimentalists.