Ulissi joined Carnegie Mellon University in 2016. He received his B.S. in physics and B.E. in chemical engineering from the University of Delaware in 2009, a master's of advanced studies in mathematics from the University of Cambridge in 2010, and a Ph.D. in chemical engineering from MIT in 2015. His thesis 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 S. Strano and Richard Braatz. Ulissi was then a postdoctoral fellow at Stanford with Jens K. 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.
Designing New Molecules with Machine Learning
2015 Ph.D., Chemical Engineering, Massachusetts Institute of Technology
2010 MA, Applied Mathematics, Cambridge University
2009 BE, Chemical Engineering, University of Delaware
2009 BS, Physics, University of Delaware
Jayan and Ulissi named Scott Institute Fellows
MechE’s B. Reeja Jayan and ChemE’s Zack Ulissi have been named Wilton E. Scott Institute for Energy Innovation Energy Fellows.
Department of Energy
DOE awards Litster and partners $3.7M for fuel cell tech research
MechE’s Shawn Litster is involved in two new projects on fuel cells for heavy-duty vehicles, which are both funded by the Department of Energy (DOE).
CMU among first to pilot brand new supercomputer
In 2020, the National Energy Research Scientific Computing Center will celebrate the arrival of the Perlmutter supercomputer—and ChemE’s Zack Ulissi will be one of the first to use it.
College of Engineering’s Celebration of Education Awards announced
Congratulations to the College of Engineering’s 2019 recipients of the Celebration of Education Awards, which recognize individuals who exemplify excellence in teaching, advising, and mentoring.
Ulissi group granted $500K for work on machine learning
ChemE’s Ulissi group was recently awarded $500K through the Department of Energy for their work on machine learning.