Directory

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.

Office
A207A Doherty Hall
Phone
412.268.9517
Fax
412.268.7139
Email
zulissi@andrew.cmu.edu
Google Scholar
Zachary Ulissi
Websites
Research Group

Designing New Molecules with Machine Learning

Education

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

Media mentions


Chemical Engineering

Undergraduates earn research experience through ChemE’s ChESS Program

Carnegie Mellon’s Chemical Engineering Summer Scholars Program (ChESS) provides rising juniors and seniors an opportunity to gain hands-on research experience.

CMU Engineering

Dean’s Early Career Fellows announced

Five College of Engineering faculty members have been awarded the Dean’s Early Career Fellowship in recognition of their exemplary contributions to their respective fields.

Scott Institute

Scott Institute announces 2022 seed grants for five projects

The Scott Institute has announced its latest seed grant awards worth $1.42 million to five research projects led by CMU Engineering faculty.

Engineering & Technology Magazine

Ulissi quoted on importance of reaction kinetics

ChemE’s Zachary Ulissi spoke with Engineering & Technology Magazine about how reaction kinetics factor in the search for a catalyst to replace platinum in experiments.

Chemical Engineering

New data set accelerates search for renewable energy sources

As part of the Open Catalyst Project collaboration, Meta AI and Carnegie Mellon University’s (CMU) Department of Chemical Engineering have announced an entirely new data set focused on oxide catalysts for the Oxygen Evolution Reaction (OER), a critical chemical reaction used in green hydrogen fuel production via wind and solar energy.

Physics World

Ulissi featured in Physics World

A feature in Physics World explains how ChemE’s Zachary Ulissi and his group have developed a deep reinforcement learning (DRL) program, dubbed CatGym, used to find the best surface atom configurations for a given chemical reaction.

Chemical Engineering

Berg Scholars, Chen and Pavlat will present posters at AIChE Conference

ChemE’s 6th Annual John Berg Undergraduate Research Symposium Poster Session winners, Ketong Chen and Benjamin Pavlat, earned the designation of Berg Scholars. They will travel on an all-expenses-paid trip to Boston for the AIChE Annual Student Conference and participate in its Undergraduate Student Poster Competition.

2021 Engineering faculty award winners selected

Congratulations to the 2021 CMU Engineering Faculty Awards winners.

Multiple outlets

Ulissi quoted on AI research

ChemE’s Zack Ulissi was quoted on his AI research with Facebook in multiple outlets, including CNBC, CNET, Engadget, Yahoo, Fortune, VentureBeat, and more.

CMU Engineering

Ulissi and Facebook AI create world’s largest catalysis dataset

Zack Ulissi and Facebook AI Research (FAIR) have created the Open Catalyst Project, the largest dataset of its kind, to accelerate the discovery of new catalysts for use in renewable energy storage.

American Institute of Chemical Engineers (AIChE)

Ulissi recognized for outstanding research

ChemE’s Zachary Ulissi was named one of AIChE’s 35 Under 35 for his work on the development and application of high-throughput simulation methods, active learning methods, and machine learning models for surface science and catalysis.

Chemical Engineering

Ulissi receives 3M award

ChemE’s Zack Ulissi received a 3M Non-tenured Faculty Award.