I am a researcher in computational materials science interested in the intersection of machine learning with traditional simulation techniques. My recent work has spanned computational materials discovery, machine learning applied to first principles simulation, and battery informatics.

I currently work as a research scientist at Toyota Research Institute. You can view some of the papers that I’ve written on my Google Scholar.

Prior to that, I was a DOE Computational Science Graduate Fellow, and a Barry Goldwater scholar. I obtained my Ph.D. in Physics with a secondary in computational science & engineering from Harvard University, and B.S. in Physics and a B.A. in Mathematics from the University of Rochester.

On The Horizon

• April 24: I will be giving two invited talks at MRS 2024 in Seattle, WA!

News!

• December 23:
  • I presented at the Advanced Automotive Battery Conference in San Diego, CA!
  • My collaboration with Eli Gerber and Eun-ah Kim of Cornell University developing a tool called InterMatch is live now in Nature Communications!
  • Work by Bianca Baldasarri from the group of Chris Wolverton on a database and feature development for Oxygen Vacancy formation energy prediction is now live on Chemistry of Materials!
• September 23:
  • Look for a work by Mehrad Ansari, intern Summer ‘22, on machine learning for short-term battery agnostic degradation prediction.
• Aug 23:
  • August 23: I gave a lecture on the representation of materials at the SUNCAT summer school at Stanford University. It was an honor to present along other distinguished speakers and to have a chance to reach an audience of more early-career scientists!
• June 23:
  • I gave a talk at Argonne National Labs in their Machine Learning & Science seminar series!
  • I spoke at the Telluride Summer Science Conference in Colorado at their Machine Learning in Chemistry & Materials Science conference.
• May 23:
  • I will be hosting Viktoriia Baibakova (UC Berkeley) and Felix Jimenez (Texas A&M) as an intern mentor this summer at TRI!
  • A perspective paper I wrote with Shijing Sun (of TRI) and with many members of our consortium is now live on Applied Physics Letters- Machine Learning!
  • This website is started. Thanks for visiting!

Older News

Nothing yet.

My interests

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Create content & metadata

For site content, there is one markdown file for each type of content, which are stored in directories like _publications, _talks, _posts, _teaching, or _pages. For example, each talk is a markdown file in the _talks directory. At the top of each markdown file is structured data in YAML about the talk, which the theme will parse to do lots of cool stuff. The same structured data about a talk is used to generate the list of talks on the Talks page, each individual page for specific talks, the talks section for the CV page, and the map of places you’ve given a talk (if you run this python file or Jupyter notebook, which creates the HTML for the map based on the contents of the _talks directory).