The Carnegie Bosch Institute (CBI) is a unique alliance between Carnegie Mellon University and the Bosch Group, a leading global supplier of technology and services. CBI was established in 1990 through a major endowment gift provided by Bosch. The core mission of CBI is to bring academia and industry together through its activities in research and education. In light of a rapidly changing global economy driven by technology and innovation, CBI focuses its activities in continually evolving areas of technical interest, expertise, and cutting-edge research that are of interest to both industry and CMU. Through funding applied research projects, CBI's goal is to stimulate relevant scientific research in core areas that are among CMU’s research competencies at the intersection of business and technology, and are of ever-growing importance for global industry, including (but not limited to):
- Internet of Things (IoT),
- artificial intelligence,
- big data, and
- similar technology-based areas of innovation.
The CBI Research Award program aims to identify and support cutting edge research and outstanding Carnegie Mellon faculty in fields relevant to our research agenda.
2021 projects awarded
CBI awarded projects in 2021 based on the topic of "research at the intersection of modern data-driven AI and classical scientific or engineering approaches."
Whereas current AI methods leverage an abundance of information to learn data-driven solutions, classical methods rely on mathematical modeling of expert knowledge derived from the laws of natural science / engineering. Hybrid models seek to bridge these two approaches (AI + classical) by incorporating both data-driven methods and knowledge of the physical world, e.g. augmenting “traditional” simulation tools with AI-driven methods.
This CfP aims to support research into these hybrid methods and models that incorporate the best of both data-driven approaches and knowledge of the physical world. Proposals should seek to improve the performance, robustness, resilience, and interpretability of intelligent and autonomous systems and are encouraged to target a broad set of domains.
- Hybrid 2D-to-3D localization in changing environments, Sebastian Scherer (PI, Robotics Institute), Burcu Akinci (Co-PI, Associate Dean for Research, College of Engineering)
- Using out-of-sample regularization of physics-informed neural networks to speed up computational fluid dynamics, Alex Davis (PI, College of Engineering), Aarti Singh (Co-PI, School of Computer Science), Satbir Singh (Co-PI, College of Engineering)
- Scheduling and queueing algorithms for resource-sharing in federated learning, Gauri Joshi (PI, College of Engineering), Weina Wang (Co-PI, School of Computer Science)
- Safe reinforcement learning integrating physic laws, control theories, and formal, Ding Zhao (PI, College of Engineering), Conrad Tucker (Co-PI, College of Engineering), Eunsuk Kang (Co-PI, CyLab)