Adaptive learning rate algorithms such as RMSProp are widely used for training deep neural
networks. RMSProp offers efficient training since it uses first order gradients to approximate …

It is widely accepted that cellular requirements and environmental conditions dictate the
architecture of genetic regulatory networks. Nonetheless, the status quo in regulatory …

One of the main focus in federated learning (FL) is the communication efficiency since a
large number of participating edge devices send their updates to the edge server at each …

We consider a high-dimension low sample-size multivariate regression problem that
accounts for correlation of the response variables. The system is underdetermined as there …

We propose the AKRON-Kalman filter for the problem of inferring sparse dynamic networks
from a noisy undersampled set of measurements. Unlike the Lasso-Kalman filter, which uses …

Background Most existing algorithms for modeling and analyzing molecular networks
assume a static or time-invariant network topology. Such view, however, does not render the …

It is widely accepted that cellular requirements and environmental conditions dictate the
architecture of genetic regulatory networks. Nonetheless, the status quo in regulatory …

In this thesis, we tackle three different problems, all related to optimization techniques for
inference and classification of genetic profiles. First, we extend the deterministic Non …