Research Summary

My research focuses on the security, resilience, and optimization of socio-cyber-physical systems, with particular emphasis on intelligent transportation networks and urban mobility. As infrastructure systems increasingly integrate computational and sensing capabilities, they face new vulnerabilities from both cyberattacks and strategic manipulation by social agents.

Research Interests

I investigate the complex interplay between technology, social dynamics, and physical infrastructure through two primary research directions:

1) Secure Monitoring and Control of Cyber-Physical Systems

My work develops robust frameworks to maintain system integrity despite malicious attacks or hardware failures. Current projects include:

• Physics-informed neural networks for nonlinear system observer design

• Secure estimation and control strategies with security guarantees against cyberattacks

• Experimental analysis of attack propagation in autonomous vehicle platoons

2) Dynamic Incentive Design for Strategic Agents

I explore how to design effective mechanisms that promote social welfare when interacting with non-myopic, strategic agents. Current investigations include:

• Dynamic, budget-constrained incentive mechanisms for infrastructure sustainability

• Co-learning approaches for preference estimation under information asymmetry

• Network effects of eco-driving behaviors through laboratory and simulation experiments

Broader Impact

By addressing fundamental challenges in control theory, game theory, learning theory, and dynamical systems, my research aims to enhance the resilience of critical infrastructure while ensuring security, privacy, and fairness. The ultimate goal is to develop frameworks that policymakers and system designers can implement to safeguard the reliability of the infrastructure systems that underpin modern society.

Key Contributions

• Mathematical Framework for Incentive Design: We developed a theoretical foundation for eco-driving incentive mechanisms (EDIMs) within the context of Stackelberg games, where a transportation system operator (TSO) acts as a leader who offers incentives to drivers (followers) to encourage eco-driving behaviors.

• First-Best and Second-Best Mechanisms: We characterized two approaches to incentive design:

  • First-Best EDIM: Optimal when drivers truthfully report their preferences, implementing recommended eco-driving levels at Nash equilibrium.

  • Second-Best EDIM: Robust to strategic misreporting, incorporating incentive compatibility constraints to ensure truthful behavior.

• Learning-Based Approach: We created a learning-based framework that integrates reinforcement learning with regret minimization techniques to optimize incentives in complex, dynamic traffic environments where driver preferences are initially unknown.

• Adjustment Mechanisms: We designed innovative contract structures with adjustment mechanisms that penalize inconsistencies in driver behavior, expanding the space of feasible truthful incentive mechanisms.

• Practical Implementation: We implemented and tested our approaches in realistic traffic simulations using the SUMO platform, demonstrating significant reductions in emissions while maintaining acceptable travel times.

Broader Implications

This research addresses a critical challenge in sustainable transportation by providing practical tools for system operators to influence driver behavior in ways that reduce environmental impact. Our findings have several important implications:

• Climate Change Mitigation: By offering immediate pathways to emission reduction in urban transportation, our work contributes to broader climate change mitigation efforts without requiring major infrastructure changes.

• Policy Design: Transportation authorities can use our frameworks to design more effective eco-driving incentive programs based on sound economic principles and behavioral insights.

• Technological Integration: Our approaches can be integrated with existing technologies such as vehicle telematics, navigation apps, and smart infrastructure to create comprehensive eco-driving systems.

• Behavioral Economics Applications: The mechanisms we developed demonstrate how economic incentives can be effectively designed to address the tension between individual preferences and collective environmental goals.

• Interdisciplinary Approach: Our work showcases the power of combining tools from control theory, game theory, and machine learning to address complex societal challenges, establishing a template for future interdisciplinary research in sustainable systems.

The methodologies developed in this research extend beyond eco-driving and offer insights for incentive design in other domains where individual behaviors have significant collective impact, such as energy conservation, public health, and resource management.

Publications

• M. U. B. Niazi, J.-H. Cho, M. A. Dahleh, R. Dong, and C. Wu, "Eco-driving incentive mechanisms for mitigating emissions in urban transportation," IEEE Transactions on Control of Network Systems (under review), 2024.

Preprint

• M. A. Dahleh, T. Horel, M. U. B. Niazi, "Mitigating information asymmetry in two-stage contracts with non-myopic agents," IFAC Workshop on Cyber-Physical Human Systems, 2024.

Preprint

• J.-H. Cho, M. U. B. Niazi, S. Du, T. Zhou, R. Dong, and C. Wu, "Learning-based incentive design for eco-driving guidance," Conference in Emerging Technologies in Transportation Systems (TRC-30), 2024.

Preprint

• M. U. B. Niazi, J.-H. Cho, M. A. Dahleh, R. Dong, and C. Wu, "Incentive design for eco-driving in urban transportation networks," European Control Conference (ECC), 2024.

Preprint

Key Contributions

We present a novel learning-based approach that addresses these fundamental challenges through:

• A physics-informed neural network framework that learns both the transformation map and its inverse using synthetic data generated from system dynamics and sensor measurements.

• Direct integration of the governing partial differential equation constraints into the learning process, ensuring the resulting observer satisfies the mathematical requirements.

• A sequential learning methodology that first learns the transformation map and subsequently its inverse, significantly improving estimation accuracy and stability.

Our research advances the state of the art in several important ways:

• Guaranteed robustness of the learned KKL observers against both learning errors and system uncertainties.

• Practical applications beyond state estimation, including sensor fault detection and isolation capabilities.

• Enhanced generalization performance across the entire state space, avoiding the overfitting problems common in previous approaches.

• Improved training efficiency and estimation accuracy through our sequential learning architecture.

• Better robustness to initialization conditions outside of training examples, addressing a key limitation of learning methods.

Broader Implications

This research represents a significant step forward in making advanced observer technology accessible for complex nonlinear systems, with applications spanning industries from aerospace and manufacturing to energy systems and autonomous vehicles.

Publications

• M. U. B. Niazi, J. Cao, M. Barreau, and K. H. Johansson, "KKL observer synthesis for nonlinear systems via physics-informed learning," Automatica (under review), 2025.

Preprint

• M. U. B. Niazi, J. Cao, X. Sun, A. Das, K. H. Johansson, "Learning-based design of Luenberger observers for autonomous nonlinear systems," American Control Conference (ACC), 2023.

Preprint

Key Contributions

We introduced a closed-loop epidemic monitoring framework with three key innovations:

• Physics-Informed Neural Networks: We leverage PINNs to estimate unknown nonlinear functions in epidemic models, such as time-varying transmission rates, by combining mechanistic epidemiological models with data-driven learning.

• Nonlinear Observer Design: We developed specialized nonlinear Luenberger-like observers that combine feedback and feedforward data injection, creating a self-correcting system that continuously refines predictions against real-world observations.

• Robust Optimal Control: Using the reliable state estimates from our observers, we design dynamic optimal control algorithms that generate balanced policy recommendations, optimizing interventions like lockdowns, vaccination rates, and testing capacities.

This work has yielded several significant advances:

• Enhanced Robustness: Our nonlinear observers effectively handle measurement and model uncertainties while providing formal guarantees of estimate accuracy.

• Adaptive Modeling: The closed-loop structure enables the system to adapt to changing conditions, including social adaptations to virus spread and viral mutations.

• Balanced Policy Recommendations: Our optimal control framework considers both health outcomes and socioeconomic impacts, minimizing infections and deaths while reducing economic disruptions.

• Scalability Framework: We've laid the groundwork for incorporating geographic and demographic heterogeneities into large-scale networked epidemic models.

Future Directions

While we have made substantial progress, further development continues in:

• Fully integrating geographic and demographic factors into multi-layered model architectures

• Developing appropriate incentive mechanisms to improve public cooperation with health measures

• Establishing partnerships with health authorities to access high-quality epidemiological data

• Creating algorithms that automatically adapt these frameworks to different geographic and cultural contexts

Broader Implications

This research significantly advances our ability to monitor and control epidemic outbreaks. By improving the accuracy and reliability of epidemic forecasting through robust feedback mechanisms, our work contributes to more effective public health decision-making and potentially more balanced interventions during future epidemics.

Publications

• M. U. B. Niazi and K. H. Johansson, "Parameterization-free observer design for nonlinear systems: Application to the state estimation of networked SIR epidemics," IEEE Conference on Decision and Control (CDC), 2023.

Preprint

• M. U. B. Niazi and K. H. Johansson, "Observer design for the state estimation of epidemic processes," IEEE Conference on Decision and Control (CDC), 2022.

Preprint

• A. Alanwar, M. U. B. Niazi, and K. H. Johansson, "Data-driven set-based estimation of polynomial systems with application to SIR epidemics," 20th European Control Conference (ECC), 2022.

Preprint

• M. U. B. Niazi, A. Y. Kibangou, C. Canudas-de-Wit, D. Nikitin, L. Tumash, and P-A. Bliman, "Modeling and control of epidemics through testing policies," Annual Reviews in Control, 2021.

Preprint

• M. U. B. Niazi, A. Y. Kibangou, C. Canudas-de-Wit, and P-A. Bliman, "Optimal control of urban human mobility for epidemic mitigation," IEEE Conference on Decision and Control (CDC), 2021.

Preprint

• M. U. B. Niazi, A. Y. Kibangou, C. Canudas-de-Wit, D. Nikitin, L. Tumash, and P-A. Bliman, "Effective testing policies for controlling an epidemic outbreak," IEEE Conference on Decision and Control (CDC), 2021.

Preprint

Key Contributions

Our research has provided several significant advances in network monitoring theory and practice:

• Cluster-Based Monitoring Framework: We demonstrated that complex networks can be effectively monitored by focusing on cluster-level dynamics rather than tracking every individual node. This aggregation approach dramatically reduces computational requirements while maintaining high monitoring accuracy.

• Minimum-Order Average Observers: We developed mathematical criteria for optimizing monitoring systems, identifying the minimum number of observation points and clusters needed to achieve desired accuracy levels. This contribution provides a foundation for resource-efficient monitoring across various applications.

• Theoretical-Practical Integration: The research bridges sophisticated mathematical concepts with practical implementation requirements, creating monitoring solutions that are both theoretically sound and operationally viable.

Real-World Applications

The results obtained from this project have been successfully applied to the urban transportation network of Grenoble, where our methods proved valuable for:

• Monitoring traffic densities across aggregated network regions

• Providing optimal route recommendations based on cluster-level observations

• Demonstrating how abstract mathematical principles translate into tangible transportation management solutions

Broader Implications

The methodologies developed in this research have applications extending far beyond transportation systems. The principles of aggregated monitoring can be adapted for:

• Smart city infrastructure management

• Energy distribution network optimization

• Environmental monitoring systems

• Public health and disease surveillance networks

Our findings demonstrate that while network systems are inherently complex, they can be effectively monitored and managed through optimal aggregation and clustering approaches. This creates new possibilities for handling large-scale systems with limited resources, offering valuable tools for addressing future challenges in network management and control.

Publications

• M. U. B. Niazi, X. Cheng, C. Canudas-de-Wit, and J. M. A. Scherpen, "Clustering-based average state observer design for large-scale network systems," Automatica, 2023.

Preprint

• M. U. B. Niazi, C. Canudas-de-Wit, and A. Y. Kibangou, "Average state estimation in large-scale clustered network systems," IEEE Transactions on Control of Network Systems, 2020.

Preprint

• M. U. B. Niazi, D. Deplano, C. Canudas-de-Wit, and A. Y. Kibangou, "Scale-free estimation of the average state in large-scale systems," IEEE Control Systems Letters, 2020.

Preprint

• M. U. B. Niazi, C. Canudas-de-Wit, and A. Y. Kibangou, "State variance estimation in large-scale network systems," 59th IEEE Conference on Decision and Control (CDC), 2020.

Preprint

• M. U. B. Niazi, C. Canudas-de-Wit, and A. Y. Kibangou, "Thermal monitoring of buildings by aggregated temperature estimation," IFAC World Congress, 2020.

Preprint

• M.U.B. Niazi, X. Cheng, C. Canudas-de-Wit, and J. M. A. Scherpen, "Structure-based clustering algorithm for model reduction of large-scale network systems," 58th IEEE Conference on Decision and Control (CDC), 2019.

Preprint

• M. U. B. Niazi, C. Canudas-de-Wit, and A. Y. Kibangou, "Average observability of large-scale network systems," 18th European Control Conference (ECC), 2019. (Finalist for Best Student Paper Award)

Preprint

Overview

This project investigates how opinions form and evolve within complex social networks, with particular emphasis on the dynamics of belief formation across multiple interconnected issues. Our work examines the bidirectional nature of social influence, where individuals simultaneously shape and are shaped by the perspectives of those around them.

Motivation

Our work is motivated by observations of real-world social systems where complete consensus rarely emerges, yet societies often achieve stable equilibria despite persistent disagreements. Traditional models have typically oversimplified these dynamics by focusing on single-issue opinion formation, overlooking the interconnectedness of belief systems, and/or neglecting the complexity of social network structures through which influence propagates.

Key Contributions

Our analysis revealed several significant insights into opinion dynamics:

• Consensus Conditions: True consensus emerges only when individuals share homogeneous conceptual frameworks about the issues at hand. Alignment in how people understand and interpret information serves as a necessary precondition for agreement.

• Stable Disagreement States: When individuals operate from heterogeneous conceptual frameworks, even extensive social interaction fails to produce consensus. Instead, the system evolves toward meta-stable equilibria characterized by persistent yet non-extreme disagreement.

• Polarization Mechanisms: Fundamentally opposite conceptual frameworks can trigger bifurcations in opinion space, leading to extreme polarization as an emergent property of the system dynamics.

Broader Implications

These findings provide valuable insights for understanding contemporary social phenomena, from political polarization to persistent cultural divides. Our research suggests that effective strategies for social cohesion must address underlying differences in conceptual frameworks rather than merely increasing interaction frequency or communication channels.

The results challenge conventional approaches to conflict resolution by demonstrating that acknowledging fundamental differences may prove more productive than pursuing unrealistic consensus goals. This framework offers valuable perspectives for understanding and potentially addressing the complex dynamics of disagreement in today's interconnected and diverse societies.

Publications

• M. U. B. Niazi and A. B. Özgüler, "A differential game model of opinion dynamics: Accord and discord as Nash equilibria," Dynamic Games and Applications, 11, pp. 137-160, 2020.
  Preprint

• M. U. B. Niazi, A. B. Özgüler, and A. Yildiz, "Consensus as a Nash equilibrium of a dynamic game," International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2016.

Preprint