Citrine Research Team

Erin Antono

Erin joined as the first member of the Data Science group at Citrine . She has worked with Citrine’s customers and partners to derive data-driven insights in problems ranging from advanced coatings to polymer processing to ionic conductors. She received her M.S. and B.A. in Materials Science and Engineering from Stanford University where she studied energy materials.


Chris Borg

Chris is a Community Data Engineer and the NextGen Project Manager. Before Citrine, Chris studied experimental chemistry at the University of Maryland (M.Sc. 2015) and UCSB (B. Sc. 2013). Chris is interested in educating upcoming researchers on data mining and machine-learning techniques. Outside of the office, Chris can be found in the climbing gym or on the soccer field.



Enze Chen
Enze is a Data Science & Data Engineer Intern. He is currently a senior at Stanford University, where he is studying Materials Science and Engineering for his Bachelor’s degree and Computational and Mathematical Engineering for his Master’s degree. He does research in Evan Reed’s group and enjoys using computational tools to model and solve materials science problems. He is excited to be a Data Science and Engineering Intern at Citrine this summer where he has gained valuable skills not only for working with data but also for communicating results to the broader community. Outside of materials informatics, Enze enjoys swimming, teaching, and listening to movie soundtracks.


Brenna Gibbons

Brenna is a Data Science Intern Citrine intern and a 4th year PhD student at Stanford University. An experimentalist by training, her research interests include sputter deposition of nanoparticles, electrochemical catalysis, and in-situ synchrotron-based spectroscopy.



Max Hutchinson

Max is Lead Architect of Machine Learning. He develops numerical methods and tools in support of machine learning at Citrine Informatics. He received his Ph.D from the University of Chicago and B.S. from Carnegie Mellon University, both in physics. Max’s overarching research interest is in how computing and computability can inform science, which has led him to study efficient enumeration of quasicrystals, reduced order models of the Kohn-Sham equations, local spectral bases in computational fluid dynamics, and machine learning of physically grounded relations on materials composition, processing, and properties, e.g. PSP and QSAR. [Google Scholar]


Bryce Meredig

Bryce is the Chief Science Officer and Cofounder of Citrine. His research interest is the application of machine learning to materials science. He earned his Ph.D. in Materials Science from Northwestern University, where he focused on materials informatics, and his BAS and MBA at Stanford University, where he is also on the faculty of the Department of Materials Science and Engineering. He is the author of over 20 peer-reviewed publications and regularly gives invited talks at materials conferences including MRS, TMS, and MS&T, as well as plenaries and keynotes at workshops focused on data-driven materials research. He was an Arjay Miller Scholar and Terman Fellow at Stanford and a Presidential Fellow and NDSEG Fellow at Northwestern. [Google Scholar]


Kyle Michel

Kyle is a founder and Chief Technical Officer at Citrine Informatics. He is an author of nearly twenty peer-reviewed articles in materials science and has led the development of many of the core data infrastructure systems that power the Citrination Platform. Prior to working at Citrine, he earned a PhD in materials science and engineering from the University of California, Los Angeles and spent several years as a postdoctoral researcher at Northwestern University. [Google Scholar]



Katherine Nguyen

Katherine is a Project Coordinator. She previously managed research operations and academic programs at the University of California Berkeley. She received her B.A. in Political Science from Cal.



Julia Ling

Julia is a Principal Scientist.  She is interested in the intersection of machine learning and physics.  She has a PhD in mechanical engineering from Stanford, and was a Harry S. Truman Fellow at Sandia National Labs before joining Citrine.



Sean Paradiso

Sean is a Scientific Software Engineer of machine learning at Citrine. In a former life, he studied how single molecule properties influence material behavior in polymer solutions and thin films (PhD, polymer field theory/applied math). Now, he’s focused on squeezing as much information from available data as possible to help great scientists make (information theoretically) optimal decisions. [Google Scholar]