From 701f247430a1bec42d8564d1ff789f4e5394760c Mon Sep 17 00:00:00 2001 From: Changwen Xu Date: Sun, 23 Jun 2024 00:53:37 -0400 Subject: [PATCH] Update about.md --- _pages/about.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/_pages/about.md b/_pages/about.md index 3210be93601e6..973b4890fa3fd 100644 --- a/_pages/about.md +++ b/_pages/about.md @@ -10,11 +10,7 @@ redirect_from: About Me ------ -<<<<<<< HEAD I am a Ph.D. student in Mechanical Engineering at University of Michigan, working on Molecular Machine Learning in [EEG](https://eeg.engin.umich.edu/) advised by [Prof. Viswanathan](https://aero.engin.umich.edu/people/viswanathan-venkat/). Earlier, I received M.S. in Computational Materials Science and Engineering at Carnegie Mellon University and did research in [Mechanical and AI Lab](https://sites.google.com/view/barati) advised by [Prof. Barati Farimani](https://www.meche.engineering.cmu.edu/directory/bios/barati-farimani-amir.html). Besides, I received B.Eng in Materials Science and Engineering at South China University of Technology. -======= -I am a Ph.D. student in Mechanical Engineering at University of Michigan, working on Molecular Machine Learning in [EEG](https://www.cmu.edu/me/venkatgroup/) advised by [Prof. Viswanathan](https://aero.engin.umich.edu/people/viswanathan-venkat/). Earlier, I received M.S. in Computational Materials Science and Engineering at Carnegie Mellon University and did research in [Mechanical and AI Lab](https://sites.google.com/view/barati) advised by [Prof. Barati Farimani](https://www.meche.engineering.cmu.edu/directory/bios/barati-farimani-amir.html). Besides, I received B.Eng. in Materials Science and Engineering at South China University of Technology. ->>>>>>> f67714cf11bd394627638ac3ba4a2c878886411a My research interest lies in combining Artificial Intelligence with interdisciplinary science and engineering problems. My current research focuses on implementing and improving foundation models for material discovery and leveraging scientific machine learning for electrolyte optimization. I believe that deep learning models are able to learn representations from data so that we can understand scientific problems from a data science view, which is a significant transformation from traditional research strategies.