Christopher McComb is a faculty member in Carnegie Mellon University’s Department of Mechanical Engineering. Previously, he was an assistant professor in the School of Engineering Design, Technology, and Professional Programs at Penn State. He also served as director of Penn State’s Center for Research in Design and Innovation and led its Technology and Human Research in Engineering Design Group.
He received dual B.S. degrees in civil and mechanical engineering from California State University-Fresno. He later attended Carnegie Mellon University as a National Science Foundation Graduate Research Fellow, where he obtained his M.S. and Ph.D. in mechanical engineering.
His research interests include human social systems in design and engineering; machine learning for engineering design; human-AI collaboration and teaming; and STEM education, with funding from NSF, DARPA, and private corporations.
Wrangling Manufacturing Data by Using Machine Learning
Building Simulations to Predict Human Behavior
- additive manufacturing
- advanced manufacturing
- agent-based modeling
- artificial intelligence
- computational engineering
- design and manufacturing
- design cognition
- design theory
- design theory, methods, and automation
- digital twins
- engineering design
- generative design
- human computer interaction
- human-centered machine learning
- human-machine teaming
- machine learning
- manufacturing workforce
- product design
- renewable energy systems
- sociotechnical systems
- STEM education
Head-to-head: Human vs. AI-human teams
New research from Carnegie Mellon University’s Department of Mechanical Engineering and the Human+AI Design Initiative underlines the adage “teamwork makes the dream work,” especially when it comes to human-AI collaboration.
Mindfulness may help engineering students’ experiences with stress
Chris McComb and collaborators at Penn State, found that mindfulness based interventions (MBI) had an impact on students in an introductory engineering design course.
Generating better mutli-lattice transitions for manufacturing
How can engineers produce smooth transitions between lattice cells in complex structures? Combining additive manufacturing and machine learning to look at latent space endpoints may help.
McComb weighs in on AI capabilities
MechE’s Chris McComb was interviewed about AI and its capabilities in a story by ABC News.
DfAI: The missing piece of Artificial Intelligence Engineering
Breakthrough improvements in how industries develop new technology using AI in engineering design has a starting point thanks to a framework developed by researchers at Carnegie Mellon and Penn State University.
ASME’s Journal of Mechanical Design
McComb named associate editor of JMD
MechE’s Chris McComb was named an associate editor of ASME’s Journal of Mechanical Design.
You can’t drive a Lamborghini to Mars
How can researchers encourage the “right” level of multidisciplinarity to identify the best solutions? A proposed common framework can transform how they collaborate across disciplines.
AI research featured in podcast
Featured on the podcast The Next Byte, new research by shows that AI may soon be taking over managerial positions and doing a better job at them.
Automating engineering’s ideal manager
A recent paper by a collaboration of CMU mechanical engineering and psychology researchers explored the use of artificial intelligence as a process manager for human design teams.
McComb selected as the recipient of ASME award
MechE’s Chris McComb was selected by ASME to receive the DTM (Design Theory and Methodology) Young Investigator Award.
Designing for a brighter future
New MechE faculty member and alumnus Christopher McComb wants to develop successful human-machine teams, create a student-centered learning environment, and give designers computational superpowers.
Construction Industry Institutue
McComb selected to lead new research team
MechE’s Chris McComb has been selected as the principal investigator for a new research team led by the Construction Industry Institute (CII). The team will find opportunities for ML, AI, and data analytics in advanced work packaging.