Atiye Alaeddini

Atiye Alaeddini, Kristi Morgansen, Mehran Mesbahi

Utilizing the concept of observability, in conjunction with tools from graph theory and optimization, this paper develops an algorithm for network synthesis with privacy guarantees. In particular, we propose an algorithm for the selection of optimal weights for the communication graph in order to maximize the privacy of nodes in the network, from a control theoretic perspective. In this direction, we propose an observability-based design of the communication topology that improves the privacy of the network in presence of an intruder. The resulting adaptive network responds to the intrusion by changing the topology of the network-in an online manner- in order to reduce the information exposed to the intruder.

Atiye Alaeddini, Daniel Klein

Decisions in public health are almost always made in the context of uncertainty. Policy makers are responsible for making important decisions, faced with the daunting task of choosing from amongst many possible options. This task is called planning under uncertainty, and is particularly acute when addressing complex systems, such as issues of global health and development. Uncertainty leads to cautious or incorrect decisions that cost time, money, and human life. It is with this understanding that we pursue greater clarity on, and methods to address optimal policy making in health. Decision making under uncertainty is a challenging task, and all too often this uncertainty is averaged away to simplify results for policy makers. Our goal in this work is to implement dynamic programming which provides basis for compiling planning results into reactive strategies. We present here a description of an AI-based method and illustrate how this method can improve our ability to find an optimal vaccination strategy. We model the problem as a partially observable Markov decision process, POMDP and show how a re-active policy can be computed using dynamic programming. In this paper, we developed a framework for optimal health policy design in an uncertain dynamic setting. We apply a stochastic dynamic programming approach to identify the optimal time to change the health intervention policy and the value of decision relevant information for improving the impact of the policy.

Atiye Alaeddini, Kristi A. Morgansen, Mehran Mesbahi

This paper is concerned with the design of optimal control for finite-dimensional control-affine nonlinear dynamical systems. We introduce an optimal control problem that specifically optimizes nonlinear observability in addition to ensuring stability of the closed loop system. A recursive algorithm is then proposed to obtain an optimal state feedback controller to maximize the resulting non-quadratic cost functional. The main contribution of the paper is presenting a control synthesis procedure that provides closed loop asymptotic stability, on one hand, and empirical observability of the system, as a transient performance criteria, on the other.