Research includes scanning tunneling microscopy and ultrafast photoemission spectroscopy.
Dr. Qing Li is an Assistant Professor in the ECE department of Carnegie Mellon University. He received his Ph.D. from Georgia Institute of Technology, with his doctoral research focused on developing optical signal processing technologies in both silicon and silicon nitride platforms. He then worked as a CNST/UMD postdoctoral researcher in National Institute of Standards and Technology, where he developed techniques for chip-scale quantum frequency conversion, octave-spanning microresonator frequency combs for optical frequency synthesis, and photonic interfaces for interrogating rubidium atomic systems. His current research interests include the study of nonlinear optical processes and quantum information science using nanophotonics.
James "Jim" Bain holds a B.S., a M.S., and a Ph.D. in Materials Science and Engineering. He received his first degree from the University of Pennsylvania and the other two from Stanford University. Following his graduation in 1993, he joined CMU, where he is now a professor of Electrical and Computer Engineering. He also holds a courtesy appointment in the Department of Materials Science and Engineering and is associate director of the Data Storage Systems Center (DSSC). He is a member of the Materials Research Society and the IEEE Magnetics, IEEE Electron Devices, and IEEE Photonics Societies.
Zachary W. Ulissi joined Carnegie Mellon University in 2017, after doing his PhD at MIT and post-doc at Stanford. His research at MIT focused on the the applications of systems engineering methods to understanding selective nanoscale carbon nanotube devices and sensors under the supervision of Michael Strano and Richard Braatz. Prof. Ulissi did his postdoctoral work at Stanford with Jens Nørskov where he worked on machine learning techniques to simplify complex catalyst reaction networks, applied to the electrochemical reduction of N2 and CO2 to fuels. The Ulissi group builds on this foundation to model, understand, and design nanoscale interfaces using modern predictive methods to guide detailed molecular simulations.