Mohamad H. Kazma
PhD in Civil Engineering
Vanderbilt University
mohamad.h.kazma@vanderbilt.edu
Hi! I recently completed my PhD in Civil Engineering at Vanderbilt University, advised by Dr. Ahmad F. Taha.
My research develops control and systems-theoretic frameworks for monitoring and enhancing the resilience of complex built and natural infrastructure networks, including power grids, water distribution networks, and coupled hydrologic and hydrodynamic systems. The work is grounded in dynamical systems theory, combinatorial optimization, and probabilistic methods.
Recent News
Advances in Observability for Nonlinear Networks: Theory and Application
See the official announcement hereAbstract
Large-scale dynamic systems model a wide range of systems with great societal relevance. This includes the internet, power grids, water systems, river networks, and transportation systems. Interestingly, these systems share so much in common—their mathematical models are nearly identical, they are all nonlinear and high-dimensional, and are riddled with malicious or benign uncertainty. At its core this field can be segmented into four branches: (i) physics-based modeling; (ii) improved sensing and system monitoring; (iii) realtime regulation and control; and (iv) network analysis, design, resource allocation, and uncertainty propagation. This dissertation starts with utilizing well-known, nonlinear models of various systems—models developed and calibrated for decades in power, water, and combustion networks—and presents contributions in branches (ii)–(iv). First, it revisits two classical problems in transmission power networks and water quality: where to place sensors to maximize information gain, under more realistic models with significant uncertainty. The second contribution introduces new ways to assess observability in large-scale systems. The third studies uncertainty propagation in power networks at scale. The final contribution designs partitioning algorithms to dissect networks into smaller ones, enabling localized analysis without resorting to full system methods.
Abstract — Scalable Control Engineering for Built and Natural Infrastructure Networks
Large-scale built and natural infrastructure systems—power grids, water distribution networks, hydrological systems, the global climate, and watersheds—share a common mathematical structure: their dynamics are nonlinear, high-dimensional, subject to uncertainty, and often adhere to physical conservation laws. Ensuring the resilience and reliability of these systems depends on our ability to monitor, predict, and control their complex dynamical behavior; yet doing so requires first answering where to sense, how to quantify system properties, and how to localize analysis at a scale at which standard tools do not apply directly. This talk is delivered in two parts. First, I briefly overview contributions in control-theoretic problems: sensor placement for nonlinear differential-algebraic power grids and multi-species water distribution networks; a variational observability Gramian with connections to Lyapunov exponents; and stability quantification under renewable energy uncertainty. Then, I focus on two problems.
The first is a control-theoretic partitioning framework that decomposes a large network into subsystems while simultaneously selecting where to sense in each subsystem. This problem is posed as submodular welfare maximization, and theoretical bounds are derived that relate subsystem observability to global network observability. The second develops a probabilistic and spectral counterpart to the node selection problem. The work shows that the observability Gramian parameterized by sensor subsets is a valid determinantal point process (DPP) kernel, establishing the first connection between DPPs—probability models over subsets of a ground set that favor diverse selections while suppressing redundancy—and Gramians in control. This connection provides a probabilistic interpretation of node selection that yields near-optimal configurations rather than a single deterministic set. From the Gramian eigenspectrum, an effective observable rank is introduced, and a submodular approximation guarantee is recovered through a Schur-complement argument on the DPP kernel. Finally, I discuss some broader impacts related to the aforementioned perspectives.