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

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.
To be written.

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.
To be written.

Union of Intersections (UoI) for interpretable data driven discovery and prediction in neuroscience
P. S. Sachdeva, S. Bhattacharyya, M. Balasubramanian, S. Ubaru, K. E. Bouchard

tl;dr: We applied the Union of Intersections framework to a battery of neuroscience datasets to obtain highly sparse but predictive models.
To be written.

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.
To be written.

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

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. [read more]
Neutron stars are among the most dense objects in the universe. But there are hypotheses that even denser objects could exist: hybrid stars. These stars are so dense that the neutrons in their core have broken down, forming a soup of strange quarks.

Physical systems typically like being in equilibrium. If we mess with that equilibrium - perturb it slightly - the system will strive to restore itself back to the equilibrium state. This manifests as oscillations, much like a pendulum will oscillate when you tap it. The same is true for stars, even those as dense as hybrid stars: they'll undergo a variety of oscillations when they're perturbed.

But sometimes, things go wrong. In the right circumstances, a perturbation might cause the system to lose control. In the case of hybrid stars, these perturbations could cause them to collapse into black holes. Such stars are considered unstable.

In this project, we analyzed the stability of these theoretical hybrid stars. We showed that a certain class of hybrid stars previously thought to be stable are actually unstable, and therefore doomed to collapse into black holes.

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.
To be written.