Bryce Ferguson

Overview

Professor Ferguson is a controls engineer interested in understanding and improving autonomy in multi-agent systems. His research group is focused on developing new learning and control paradigms for systems formed by the interconnection of many engineered devices and human users. By considering the technical and social implications of an engineer’s design decisions, this group's work drives innovation for fleet robotics and autonomous transportation, power grid demand management, network security, and other cyber-physical systems.

Before joining Dartmouth, Ferguson was a postdoctoral researcher at the University of California, Berkeley, in the Departments of Electrical Engineering & Computer Science and Civil & Environmental Engineering. He received his PhD at the University of California, Santa Barbara, in the Department of Electrical & Computer Engineering while part of the Center for Control, Dynamical Systems, and Computation. Ferguson's work spans several research disciplines, including control theory, machine learning, economics, and operations research.

Research Interests

Controls; optimization; machine learning; multi-agent systems; game theory; operations researchControls; optimization; distributed autonomy; fleet robotics; socio-technical systems; adversarial systems; machine learning; multi-agent systems; game theory; operations research

Education

• PhD, Electrical & Computer Engineering, University of California, Santa Barbara 2024
• MS, Electrical & Computer Engineering, University of California, Santa Barbara 2020
• BS, Electrical & Computer Engineering, University of California, Santa Barbara 2018
• AA, Mathematics, Santa Rosa Junior College 2016

Research Projects

• Security and Safety in the Face of Uncertainty for Network Systems

  Security and Safety in the Face of Uncertainty for Network Systems

  As systems grow large and additional communication channels are implemented, new avenues for noise, hazards, and bad actors are introduced. Unlike in a single-agent system, when many agents are connected, individual failures or attacks may be hard to detect, and local perturbations can cascade into much more expansive failures. This project studies different adversarial and risk-aware design environments through the lens of zero-sum game theory. Intended outcomes are secure strategies in networked adversarial environments and fundamental analysis of the relationship between information and the ability to guarantee security.

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• Information Design for Social Influencing

  Information Design for Social Influencing

  In social systems, like transportation networks, human users make decisions despite having uncertainty about the underlying state of the system (e.g., the current state of traffic). If a central authority is able to gather this information (e.g., Google or Apple maps), then they are presented with the opportunity to reveal that information to the system's users or strategically reveal pieces of information as a mechanism to shape the users' beliefs and ultimate behavior. In this project, we seek to identify policies for strategically signaling information to incentivize more desirable decisions by the systems users. Intended outcomes will be insights on how to design information signaling policies as well as as formal analysis on the effects of heterogeneity among the human user population.

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• Between centralized and distributed control: inter-agent coordination mechanism

  Between centralized and distributed control: inter-agent coordination mechanism

  In large-scale multi-agent systems like robotic fleets, distributing the computational and decision-making tasks to be performed locally by individual robots (or agents) is a popular method of reducing the complexity and communication overhead of a fleet level control policy. However, the emergent behavior of these distributed systems can be inferior to those that are controlled in a centralized manner. This project seeks to understand how agents can partially coordinate their behavior by collaborating on group control actions. Intended outcomes are control algorithms for new, collaborative paradigms for multi-agent systems and formal guarantees on the behavior of these systems.

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Selected Publications

• Ferguson, B.L., Brown, P.N., & Marden, J.R. (2024). Information Signaling With Concurrent Monetary Incentives in Bayesian Congestion Games. IEEE Transactions on Intelligent Transportation Systems.
• Ferguson, B.L., Paccagnan, D., Pradelski, B.S., & Marden, J.R. (2023). Collaborative coalitions in multi-agent systems: Quantifying the strong price of anarchy for resource allocation games. In 2023 62nd IEEE Conference on Decision and Control (CDC) (pp. 3238-3243). IEEE.
• Ferguson, B.L., & Marden, J.R. (2023). Robust utility design in distributed resource allocation problems with defective agents. Dynamic Games and Applications, 13(1), 208-230.
• Ferguson, B.L., Brown, P.N., & Marden, J.R. (2021). The effectiveness of subsidies and tolls in congestion games. IEEE Transactions on Automatic Control, 67(6), 2729-2742.