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)
In 2020, CBI awarded the following project:
- Materials innovation for sustainably degradable plastic films, Gerald Wang (PI, College of Engineering), Stefanie Sydlik (Co-PI, Mellon College of Science)
This project is on the topic of sustainability driven by technology innovations, taking into account ecological, economical and societal aspects. The following fields of activity are expected to be of importance for technology research and development, also and particularly comprising AI and ML methods, and thus of specific interest:
- Material recycling, product reuse and circular economy;
- Energy-efficient solutions for a significant reduction of CO2 emissions over the full life cycle of products and infrastructure;
- Disruptive approaches for the removal of CO2 from the ecosphere, thus mitigating the impact of this greenhouse major gas on climate change.
CBI announced the results of its initial call for research grant proposals in 2019. After a comprehensive review process, six research projects were selected for funding through a Carnegie Bosch Institute Research Award:
- Machine learning for connected intelligent systems, Virginia Smith (PI, College of Engineering) and Ameet Talwalkar (Co-PI, School of Computer Science)
- Explanations, trust, and AI, Tae Wan Kim (PI, Tepper School of Business) and David Danks, Dokyun Lee, and Joy Lu (Co-PIs, Tepper School of Business)
- Representing procedural knowledge as programs, Graham Neubig (PI, School of Computer Science) and Eduard Hovy (Co-PI, School of Computer Science)
- Computationally guided additive manufacturing of self-healing actuators and sensors, Mohammad Islam (PI, College of Engineering) and Lining Yao (Co-PI, Human-Computer Interaction Institute)
- Privacy-preserving inference and decision-making with IoT data, Osman Yagan (PI, College of Engineering) and Gauri Joshi (Co-PI, College of Engineering)
- Plug-and-play activity recognition in an ecosystem of microphones, Mayank Goel (PI, School of Computer Science) and Chris Harrison (Co-PI, Human-Computer Interaction Institute)
CBI received 44 submissions involving 75 unique faculty spanning six colleges and schools at Carnegie Mellon University. This tremendous response created a high level of competition for the awards. Carnegie Bosch Institute more than doubled its initial budget for funding, in order to support a total of six projects. The CBI Research Steering Committee utilized a thorough selection process, assessing all submissions based on a number of review criteria drawn directly from the call for proposals.
In alignment with the CBI research mission, the selected projects promote collaboration through involvement of multiple faculty, interdisciplinary interaction, and involvement or leveraging of industry partners where possible. All projects are expected to lead to applied research results with high impact and excellence, demonstrated through visibility (e.g. publication of research results) and/or potential utilization of applied results.
The selected research proposals involve four different topic areas of particular interest to the CBI research mission at the intersection of business and technology:
- Crowd centric computing and interaction: Approaches that enable humans and machines to collaborate on sophisticated cognitive tasks at internet-scale, thereby paving the way for the next-generation of AI applications
- Connected and intelligent systems and services: Innovative approaches to safeguard personally identifiable information collected in the IoT, enabling the development of personalized services while respecting individual privacy
- Disruptive materials and sustainable manufacturing: Creating breakthrough production techniques and functional materials via combinations of Computational Material Science (CMS), Artificial Intelligence (AI), and Quantum Simulation (QS)
- Ethics and AI: Understanding of the socio-economic implications of AI and how related technologies impact mankind, including questions around policy / legal frameworks, governance, fairness, and trust