Naomi Ehrich Leonard
Office: D-234 E-Quad
Email: naomi@princeton.edu
Phone: (609) 258-5129
MAE Faculty Profile
Full Bio
Teaching

Prof. Leonard’s background includes feedback control theory, nonlinear dynamics, geometric mechanics, and robotics, where she has made contributions both to theory and to application. She studies and designs complex, dynamical systems comprised of many interacting agents, including, for example, animals, robots, and/or humans that move, sense, and decide together. Her research program emphasizes the development of analytically tractable mathematical models of collective dynamics that provide the systematic means to examine the role of feedback (responsive behavior), interconnection (who is communicating with whom), heterogeneity (individual differences) in the behavior, learning, and resilience of groups in changing environments.

Leveraging mathematical models, Leonard has studied mechanisms of collective behavior in fish schools, bird flocks, honeybee swarms, and ant colonies, and she has designed rules for distributed robotic vehicles to perform collaborative tasks ranging from adaptive ocean sampling to trash collection in human-populated spaces. A former student of dance and life-long dance enthusiast, she has grown increasingly interested in intersections with dance and composition.

Juncal Arbelaiz
Department: MAE
Office: H-116 E-Quad
Email: ja3941 [at] princeton.edu
Personal Webpage

In my doctoral research, I studied how to design tractable optimal distributed control and estimation architectures for systems with spatiotemporal dynamics. I also analyzed the role that uncertainty (noise) in models of the dynamics and in measurements plays in the spatial localization of optimal feedback architectures. The main application that motivated my theoretical work was soft robotics.

I am currently a Schmidt Science Postdoctoral Fellow, interested in exploring the intersection between biological and machine intelligence. In particular, I aim to understand how robustness, adaptation and learning capabilities emerge in biological collectives in the face of uncertainty and apply such insights to the design of human-made systems.

María Santos
Department: MAE
Office: H-116 E-Quad
Email: maria.santos [at] princeton.edu
Personal Webpage

My research focuses on the distributed coordination of multi-robot systems, with a particular focus on modeling heterogeneous capabilities within large swarms of robots. I am also very interested on exploring how to use swarm robotics in various forms of artistic expression, including theatre or painting.

Giovanna Amorim
Department: MAE
Office: H-120 E-Quad
Email: giamorim [at] princeton.edu
Personal Webpage

I am broadly interested in multi-agent dynamical systems, control theory, and robotics. My current research focuses on the distributed control of opinion patterns on signed networks of agents.

Charlotte Cathcart
Department: MAE
Office: H-120 E-Quad
Email: cathcart [at] princeton.edu
Student Profile

Broadly interested in feedback control, robotics, and people, my research focuses on what processes and control strategies can be used to aid human-robot interactions and encourage collaboration. I am currently exploring the use of opinion dynamics to dictate reactive motion control and collision avoidance for on-ground robots to safely pass by humans.

Isla Xi Han
Department: Architecture
Office: H-120 E-Quad
Email: xihan [at] princeton.edu
Student Profile
Personal Website

My research interests lie at the intersection of multi-agent systems, human-robot collaboration, and stigmergic construction. Specifically, I explore how heterogeneous teams consisting of both humans and robots can design and build creative, high-performance structures by making local on-the-fly decisions.

Justin Lidard
Department: MAE
Office: H-120 E-Quad
Email: jlidard [at] princeton.edu
Student Profile
Personal Webpage

My research interests lie at the intersection of machine learning, optimization, multi-agent systems, and control theory, primarily in (deep) multi-agent reinforcement learning (MARL) and efficient decentralized decision-making. I am currently working on new algorithms that allow learning agents to solve game-theoretic tasks while using communication to promote coordination and cooperation.

Udari Madhushani
Department: MAE
Office: H-120 E-Quad
Email: udarim [at] princeton.edu
Student Profile
Personal Webpage

I am broadly interested in the problems at the intersection of machine learning, control theory and robotics.

Justice Mason
Department: MAE
Office: H-120 E-Quad
Email: jjmason [at] princeton.edu
Student Profile

My research interests are at the intersection of robotics, control, and machine learning. I’m interested in questions about how robots perform cooperative tasks, specifically in space applications. Currently, I am working on using learning techniques to predict the dynamics of 3D rigid-body objects for the purposes of model-based control.

Yenan (Daniel) Shen
Department: MAE
Office: H-120 E-Quad
Email: yenan.shen [at] princeton.edu

I am interested in designing and controlling of physics-based deformable structures and soft robotics. Specifically, my research interest is using topological optimization and inverse learning to use physics-based mechanisms in designing soft robotics. In addition, I am passionate about controlling the soft robotics and using reinforcement learning to achieve controllable locomotion.

Kathryn Wantlin
Department: COS
Office: Computer Science Building 418b
Email: kw2960 [at] princeton.edu
Personal Webpage

My research focuses on the intersection of machine learning and multi-agent systems. Currently, I study statistical approaches for modeling emergent properties in swarm robotics and cellular morphogenesis as well as machine learning methods for training populations of cooperative agents.