assistant professor of aerospace engineering
Multifidelity Modeling and Search Using Adaptive Field PredictionLeifsson will create new computation tools that will enhance uncertainty analysis and design optimization under uncertainty in complex engineering systems, such as aerodynamics, electromagnetics and mechanical structures. The advanced methods will combine metamodeling techniques and machine learning, as well as novel adaptation techniques – offering rapid and reliable design of complex engineered systems, such as in transportation, energy harvesting, weather forecasting and communication.
Leifsson will also create a new undergraduate short course and organize a symposium on computational design – and will launch an online hub to make his advanced simulation-based design techniques available to other engineers across the country.
assistant professor of chemical and biological engineering
Synthesis and Properties of Group IV Colloidal Quantum WellsPanthani will develop new types of ultrathin semiconductor materials that may help improve the capabilities, efficiency and costs of computing and telecommunication. He will use novel materials synthesis techniques to create single- to few-atom thick silicon-germanium alloys with controlled composition, structure and surface chemistry. He will also coordinate molecules to the surfaces of these 2-D semiconductors, which can be deposited onto substrates to improve processing these materials into electronic devices.
Panthani will also partner with high school teachers to incorporate research lab experiences into their curricula and implement a workshop designed to encourage underrepresented groups to pursue STEM careers.
associate professor of mechanical engineering
Robustifying Machine Learning for Cyber-Physical SystemsSarkar will build computational techniques to detect and mitigate risk in using machine learning and artificial intelligence for cyber-physical systems, such as self-driving cars. His framework and algorithms will help deep-learning models better address “edge cases” where the real-life situation isn’t represented well in the training data set – and to fend off adversarial attacks on machine-learning-based decision systems. Algorithms will be validated on experimental self-driving cars and robotics test beds.
Sarkar will also develop new curricula, research experiences and other outreach activities for high school students and teachers in the critical interdisciplinary areas of system theory and data science.