Our mission is to create artificial intelligence (AI) technologies that accelerate the pace of engineering innovation.
Our applications span the range between purely technical (e.g., electric machines, high-performance materials) and sociotechnical (e.g., smart buildings, extra-terrestrial habitats).
Our research develops communication channels between theories and data. The communication protocol is based on probability theory (thought of as an extension of logic under uncertainty) with an additional layer of causality (expressed through physical laws and graphical models) and is realized using modern machine learning techniques based on deep neural networks.
We are extremely grateful for the support of our academic, government, and private sponsors:
Click here to see how past group members are doing.
Realize a novel hybrid material that self-conforms around an object of interest as a physical route for computing and reporting the …
Developing smart autonomous deep space habitats that will adapt, absorb and rapidly recover from disruptions.
Discovery of high-temperature, oxidation resistant, complex, concentrated alloys via data science driven multi-resolution experiments …
Baking known physics into machine learning algorithms.
Incentivizing subsidized communities to consume less energy.
Improving the resilience of the built environment through an automated vision-based system that generates probabilistic predictions.
Development of computational tools for electric machine design including manufacturing uncertainties.
Ultra-fast detection of faults in the power grid.
Learn potential causal structures with quantified uncertainties from observational data.
Understanding how incentive misalignment affects the design of complex systems.
Learning to replicate how engineers solve complex problems.
This is a list of the courses Prof. Bilionis teaches on a regular basis. He has also delivered special short courses on research topics, e.g., uncertainty quantification, Gaussian process regression, global optimization of expensive functions, sequential design of simulations/experiments, which will be added to the site gradually.*