Amanda Prorok is Professor of Collective Intelligence and Robotics in the Department of Computer Science and Technology at the University of Cambridge, and a Fellow of Pembroke College. Prior to joining Cambridge, she was a postdoctoral researcher at the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory at the University of Pennsylvania, USA. She completed her PhD at EPFL, Switzerland.

Prof Prorok is an IEEE Senior Member and has been honored by numerous research awards, including an ERC Starting Grant, an Amazon Research Award, the EPSRC New Investigator Award, the Isaac Newton Trust Early Career Award, and several Best Paper awards. Her PhD thesis was awarded the Asea Brown Boveri (ABB) prize for the best thesis at EPFL in Computer Science.

She serves as Associate Editor for Autonomous Robots (AURO) and was the Chair of the 2021 IEEE International Symposium on Multi-Robot and Multi-Agent Systems.

Ajay’s research is that of a full-stack roboticist – with a focus on robust, optimal, and agile control + planning for various robots and robotic teams. Current focus is on scalable and learnt multirobot coordination.

Sally works with the team coordinating the lab space, various lab activities and experiments. She produces the newsletter and website.

Zhan works on understanding the relationship between the multi-agent system and the environment and exploits the latter to improve the system performance. For research, he focuses on techniques in the field of machine learning, particularly reinforcement learning and graph neural networks. 

Jasmine is interested in research at the intersection of mathematics, machine learning theory, and robotics that can enhance the way agents learn and interact. She focuses on parameterising uncertainty and strengthening resilience.

Sanim aims to create adaptive and resilient robots, a pursuit that hinges on the foundation of reliable localization and optimal control. His broader interests encompass dynamic modelling, collective localization, sensor fusion, path planning, and linear, nonlinear, and learning-based control of robots.

Guang works on designing controllers and motion planners for safety critical systems. In particular, his research focuses on encoding safety and mission requirements in optimization problems, such that autonomous agents are guaranteed to behave accordingly in an explainable manner. He also has interests in ensuring safety in multi-agents reinforcement learning and temporal logic based motion planning.

Jan’s research is about transferring Multi-Agent control policies trained in simulation to the real world (sim-to-real transfer), using Multi-Agent Reinforcement Learning and Graph Neural Networks. He is also interested in interpretability, resilience and robustness of such control policies, particularly in the context of real-world systems.

Steven studies how long-term memory can improve decision making in reinforcement learning. He focuses on arranging collections of memories into graph structures, which he queries using graph neural networks. His research aims to improve the reasoning capabilities of robots, allowing them to solve human-level tasks and learn from and correct mistakes in real-time.

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Rishabh’s work aims to make GNNs safer, with an emphasis on multi-agent systems. This includes making (certifiably) robust models and exploring explainable and interpretable models.

Keisuke has broad interests in controlling multiple moving agents. In particular, his research focuses on path planning, combinatorial search, learning and distributed systems.  He is a JSPS Overseas Research Fellow in Japan.

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Michael’s research is in multi-agent systems and swarm robotics, focusing on scalability and provable safety guarantees. He is particularly interested in how interactions between an increasing number of independent systems can scalably improve decision making or make it safer. Michael is also interested in alignment.

Matteo studies resilience and heterogeneity in multi-agent and multi-robot systems. For research, he employs techniques from the fields of Multi-Agent Reinforcement Learning and Graph Neural Networks.

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Jennifer works on understanding wireless data communications mechanisms and their applications to multi robot control algorithms, studying strategies and co-optimizations that allow predictable transfer to the real world. Her work may be applied to automotive inter-networking and drone swarm control.

Peter studies reinforcement learning, optimization and control. His research interests lies at the intersection between control theory and machine learning with applications to real-world autonomous, agile robot teams.

Matthew is a robotics researcher, an AI educator, and a science policy advocate. His research on coordination strategies enables robot teams to accomplish complex tasks in ways that consider fairness within the context of real world human systems. He is co-advised at the University of Pennsylvania by Dr. Vijay Kumar and Dr. George Pappas and collaborates with Dr. Amanda Prorok at the University of Cambridge as a visiting researcher