| Time: | May 25, 2023 |
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Five early career researchers from the cluster took the opportunity to engage in an intensive exchange with our visiting professors Kirstin H. Petersen and Nils Napp, both from Cornell University.
After a short introduction to the professors' research areas, each of the ECRs was able to briefly present their work and discuss it with Kirstin Petersen and Nils Napp and the other participants of the seminar.
In addition to the professional exchange, professional issues and especially the compatibility of family and scientific career were discussed. Nils Napp and Kirstin Petersen also explained the differences between the American and German science systems.
Kirstin H. Petersen is Assistant Professor and Aref and Manon Lahham Faculty Fellow at the School of Electrical and Computer Engineering and Director of the Collective Embodied Intelligence Lab (CEI-lab). She has an NSF CAREER award, a Packard Fellowship for Science and Engineering, and was named among the 25 Amazing Women in Robotics to Know by Robohub in 2018.
Her research covers simple robotic solutions to complex problems, with a focus on bio-inspired design and coordination of robot collectives, as well as studies of their biological counterparts. Major research topics in her lab include swarm intelligence, embodied intelligence, autonomous construction, digital agriculture, bio-cyber physical systems, human-swarm interaction, and soft robot swarms. Although her work is centered in engineering, her close collaborators span entomology, plant sciences, and architecture.
Nils Napp is Assistant Professor at the School of Electrical and Computer Engineering and Director of the Napp-lab. He has an NSF CAREER award.
His research focuses on design and control strategies for systems that operate with uncertainty. His research is on developing models and control strategies for multi-robot systems that create permanent artifacts, ranging from self-assembly of simple agents to more capable robots that assemble large-scale structures. His goal is both to improve existing systems and to enable new ones by finding suitable representations for robust algorithm design. Fundamentally, the challenge is the tension is between tractability (simple models that can be used to develop correctness proofs) and applicability (how easy it is to build robotic systems that instantiate the models. Application areas span robotic construction, design synthesis, localization, and digital agriculture and pollination.
