Trainees

Cohort 0

Jiaxing Qu

Jiaxing Qu

University of Illinois at Urbana-Champaign, Mechanical Engineering, PhD, 2018-current
Shanghai Jiao Tong University, Mechanics, M.S., 2016-2018
Hohai University, Mechanics, B.S., 2012-2016

Jiaxing's research interest focuses on computational discovery for novel thermoelectric materials.

Faculty Advisor: Elif Ertekin

Josh Vita

Josh Vita

University of Arizona, Materials Science and Engineering, Mathematics, B.S., 2017

Originally from Tucson, Josh did his undergraduate degree at the University of Arizona, receiving bachelor's degrees in both materials science and mathematics. Since joining Illinois in 2017 as a member of Dallas Trinkle's group, Josh's research has focused on developing efficient and scalable software for building inter-atomic potentials for molecular dynamics. Josh says, ultimately, the purpose of his research is to leverage modern computational resources and materials databases to help lower the barrier to entry for new studies in computational materials science.

Faculty Advisor: Dallas R. Trinkle

Mengchen Wang

Mengchen Wang

University of Science and Technology of China, Statistics, B.S., 2018

Mengchen's research interests focuses on spatio-temporal modeling and functional data analysis.

Faculty Advisor: Mengchen Wang

Xiaoyang Wang

Xiaoyang Wang

Northwestern University, Computer Science, M.S., 2017 Central South University (China), Automation, B.E., 2016

Xiaoyang is a Ph.D. student in Computer Science. He is currently working on causal reasoning. More specifically, he is developing algorithms to diagnose failures in material science experiments.

Faculty Advisor: Klara Nahrstedt

Erick Hernandez

Erick Hernandez

Massachusetts Institute of Technology, Materials Science and Engineering, B.S.
University of Illinois at Urbana-Champaign, Materials Science and Engineering, Ph.D. candidate

Quantum dots are a type of light-emitting nanocrystalline semiconductors with applications in consumer electronics and in biological imaging. Today's best performing quantum dots are toxic when they degrade, preventing their widespread use. In order to design better, safer quantum dots we need to understand how the nanocrystal's composition, shape, size, and internal structure affect its optical properties. The overarching goal of my research is to use experimental and computational techniques to explore the structure-function relationship between these characteristics and the optical properties of nanocrystals.

Faculty Advisor(s): Drs. André Schleife and Andrew Smith

Cohort 1

Ferdaushi Alam Bipasha

Ferdaushi Alam Bipasha

Bangladesh University of Engineering and Technology, Mechanical Engineering, B.S., 2016

Bipasha completed her Bachelors in Mechanical engineering from BUET in 2016 and after that worked in industries for three years. In Fall 2019, Bipasha joined MechSE at Illinois to pursue her PhD in Mechanical Engineering. She is currently working in Dr. Ertekin's research group and working on computational study of thermoelectric materials.

Faculty Advisor: Harley Johnson

Sharon Edward

Sharon Edward

University of Kentucky, Mechanical Engineering, B.S., 2019

Sharon's current research interests are in fluid mechanics, molecular dynamics and dynamical systems. Her research focuses on the study of hyperthermal collisions of atomic oxygen with graphene using molecular dynamics based programs like LAMMPS.

Faculty Advisor: Harley Johnson

Robert Charles Garrett

Robert Charles Garrett

Miami University, Mathematics & Statistics, B.S., 2019

Robert graduated with a B.S. in Mathematics & Statistics from Miami University in 2019. Recently, he has recently finished his first year in the Statistics PhD Program at Illinois. As part of DIGI-MAT, Robert will be working with Dr. Bo Li to develop a new method for functional kriging, which would allow for better predictions of curves and surfaces across spatial regions.

Faculty Advisor: Bo Li

Jeffrey Huang

Jeffrey Huang

Cornell University, Engineering Physics, B.S., 2019

(Scanning) transmission electron microscopy can be used to study the structure of materials at the atomic scale. Jeffrey is using machine learning to process TEM diffraction data and atomic resolution STEM images, especially in application to metallic antiferromagnetic materials.

Faculty Advisor: Pinshane Huang

Kevin Kleiner

Kevin Kleiner

University of Tennessee Knoxville, Physics, B.S., 2019
Summa Cum Laude
Chancellor's Honors Program

Kevin says modern technologies including quantum computers, transistors, and solar cells rely on semiconductor materials. Thanks to implanting small defects into the atomic pattern, semiconductors can host useful, controllable behaviors such as electrical conductivity, light sensing, and long-lived quantum information. These behaviors originate from the interplay of the electrons and atomic nuclei, but the precise role of defects in realistic systems is still under debate. Aided by first principles physics simulations and data science techniques, I will build simplified quantum models for the crystal vibrations and electronic structures in various semiconductors. These models link a system's atomic pattern to accurate descriptions of its behaviors, which can guide materials engineers when tuning semiconductors for relevant technologies.

Faculty Advisor: Lucas Wagner

Zachary Riedel

Zachary Riedel

Clemson University, Materials Science and Engineering, B.S., 2019
minors: mathematics, chemistry

Zach is a PhD student in Materials Science and Engineering, working in Dr. Daniel P. Shoemaker's group on two materials chemistry focused projects. The first involves isolating a novel Ni-V-O phase in order to solve its structure and probe its electronic and magnetic properties. His DIGI-MAT involvement stems from his second project. This project seeks to utilize the advantageous properties of rare earth materials for quantum information storage. Quantum information and computing can offer benefits ranging from massively increased computational capacity to enhanced cryptographic security. By cataloging previously discovered, environmentally stable candidates and by predicting undiscovered materials, Zach hopes to expand the materials options for the emerging quantum information field. Zach says, "I am excited about the (DIGI-MAT) program and look forward to learning about the intersection of materials and data science."

Faculty Advisor: Daniel Shoemaker

Aagam Shah

Aagam Shah

IIT-Gandhinagar, Materials Science and Engineering, B.S., 2019

Aagam received his Bachelor's in Materials Science and Engineering from the Indian Institute of Technology Gandhinagar, where he worked on biotemplating to synthesise inverse gyroid photonic crystals. He joined the Master's program in the same discipline at Illinois in Fall 2019. Here, he works with Prof. Sameh Tawfick and Prof. Elif Ertekin on the Gr-ResQ tool. This is a nanoHUB tool designed to be a one-stop solution for analysis of graphene synthesis by chemical vapor deposition, which acts as a database for recipes and provides tools for users to quantitatively analyse microscopy images and Raman spectra. Aagam studies the synthesis of graphene by chemical vapor deposition and works on the tool analyzing Raman spectra.

Faculty Advisor: Elif Ertekin

Cohort 2

Micah Armstrong

Micah Armstrong

University of Alabama at Birmingham, Materials Engineering, B.S., 2020

As a part of Dr. Nicola Perry's research group Micah is working on the development of high-throughput methods for the analysis of combinatorial thin films. These films can be used to analyze the transport properties and defect chemistries of ionic materials, such as those used in emerging energy conversion and storage technologies. The goal of this project is to develop efficient methods for analyzing the electrochemical properties of broad ranges of oxide compositions at a wide range of thermal and atmospheric conditions, using advanced data science methods to receive and analyze the large, complex data sets produced in the process.

Faculty Advisor: Nicola Perry

Rees Chang

Rees Chang

Cornell University, Materials Science and Engineering, B.S., 2020

Rees is a PhD student in materials science and engineering. He is broadly interested in advancing and applying methods at the intersection of computational materials science and machine learning to accelerate materials design.

Faculty Advisor: Elif Ertekin

Austin Ellis-Mohr

Austin Ellis-Mohr

Cornell University, Electrical & Computer Engineering, B.S., 2020

As the size and depth of neural networks utilized continues to increase, a critical bottleneck is the data communication between off-chip memory and the on-chip caches. non-von Neumann architectures in which memory and processing coexist in some form. Austin's work is focused on advancing machine learning hardware by creating new architectures designed specially for neural networks using standard CMOS technology and by developing new types of in-memory and neuromorphic computing devices.

Faculty Advisor: Qing Cao

Ben Jasperson

Ben Jasperson

University of Wisconsin-Madison, Mechanical Engineering, M.S.
University of Wisconsin-Madison, Mechanical Engineering, B.S.

Ben received his B.S. and M.S. in mechanical engineering from the University of Wisconsin-Madison. His current research topic involves using machine learning to identify 2D materials with desired non-linear optical properties.

Faculty Advisor: Harley Johnson

Sonali Joshi

Sonali Joshi

University of Central Florida, Physics, B.S., 2020 (with honors)

Sonali is a Ph.D. student in Physics at UIUC. She is interested in the development and use of computational techniques in physics that can be used towards finding potential new and existing materials for future technologies. An important part of realizing new materials if determining their thermodynamic stability, which requires exploring their formation energy. Currently she is introducing herself to various first principles techniques to calculate the formation energy of various systems. The hope for these new materials is that they could be valuable in catalytic reactions, energy storage, and many other areas.

Faculty Advisor: Lucas Wagner

Cindy Wong

Cindy Wong

Arizona State University, Materials Science and Engineering, B.S., 2020

The current paradigm in materials research which focuses on trial-and-error can be costly. Cindy's current research focuses on using machine learning to understand the structure-property relation of complex materials for inverse materials design. By using data-driven methods in materials design and discovery, we can overcome challenges in experimental and computational work that rely on human intuition.

Faculty Advisor: André Schleife

Carlos Juarez-Yescas

Carlos Juarez-Yescas

Carlos received his Bachelor's (2012-2016) and Master's (2017-2020) in Chemistry from the Autonomous Metropolitan University in Mexico City, where he worked on the synthesis and characterization of copper-based cathode materials for Li-ion batteries. He is currently working with Prof. Paul Braun, studying novel solid-state electrolytes via quasi-operando electrochemical impedance spectroscopy (EIS). The aim is to use emerging computational data techniques to efficiently manage and interpret massive data sets that are obtained from EIS.

Faculty Advisor: Paul Braun

Cohort 3

Dreycen Foiles

Dreycen Foiles

“Software-based neural networks have great predictive power, but poor energy efficiency. By manipulating magnetic textures such as skyrmions, spin waves, and vortices at the nanoscale, Dreycen hopes to design neuromorphic architectures that have the predictive power of software-based neural networks, but with the greatly improved energy efficiency of dedicated hardware. Using existing machine learning tools and micromagnetic simulations, he optimizes the design of magnetic systems before fabricating them in the lab with magnetron sputtering and electron-beam evaporation.”

Faculty Advisor: Axel Hoffman

Andrew Gracyk

Andrew Gracyk

University of California, Los Angeles, Applied Mathematics, B.S., Minor in Statistics, 2019,
University of California, Santa Barbara, Applied Mathematics, M.A., 2021
 

Andrew is a PhD student in Statistics working with Dr. Xiaohui Chen in the same department. His research employs techniques in machine learning to solve problems at the interface of data science and other areas of mathematics and statistics. His particular, current interest lies in developing neural network frameworks to solve problems in partial differential equations (PDEs). This notably includes special topics in PDEs for optimal transport and control theory, such as constrained optimization problems.

Faculty Advisor: Bo Li

Angela Pak

Angela Pak

University of Texas at Austin, Biomedical Engineering, B.S., 2021. 

Angela received her B.S. in biomedical engineering, with a technical focus on computational methods, from the University of Texas. Now a PhD student in the department of Materials Science and Engineering here at UIUC, Angela is advised by Drs. Elif Ertekin and André Schleife. Her research interests are largely centered on high-throughput computational design and selection of novel materials.

Faculty Advisors: Elif Ertekin and André Schleife

Kelly Hwang

Kelly Hwang

Sogang University, Mechanical Engineering, B.S., 2020

Kelly is a Ph.D. student in Mechanical Science and Engineering. She is interested in strain engineering of 2D materials for the discovery of new optoelectronic properties. As part of the DIGI-MAT program, she will be combining computational methods with materials science to intelligently guide the discovery of new materials with desired properties.

Faculty Advisor: Elif Ertekin & Arend van der Zande

Phillip Sin

Phillip Sin

Phillip is a PhD student in Materials Science and Engineering. He graduated from Carnegie Mellon University with a B.S. and M.S. in Materials Science and Engineering. He is currently a student in Prof. Nick Jackson’s research group developing machine learning models to predict the morphology dependence of electronic structure in insulating polymers with applications to dielectric breakdown, contact electrification, and charge transport.

Faculty Advisor: Nick Jackson

Dan Palmer

Dan Palmer

“Dan is a PhD student in the Materials Science and Engineering department, working with Dr. Harley Johnson. Dan is interested in developing simplified quantum mechanical models of materials with large electron correlations (such as twisted bilayer graphene), in a scalable manner.  Dan plans to leverage ab initio calculations, machine learning, and large datasets to improve the accuracy and scalability of these models.”

Faculty Advisor: Harley Johnson

Charith DeSilva

Charith DeSilva

Oklahoma State University, Physics, B.S., 2018Oklahoma State University, Physics, M.S., 2020
 
Charith is a PhD student in materials science and engineering and is a member of Dallas Trinkle's
research group. His research is focused on studying the diffusion of hydrogen though various metals
and alloys, utilizing both density-functional theory and machine learning.
 
Faculty Advisor: Dr. Dallas Trinkle
Jose Lasso

Jose Lasso

Jose received his B.S.E in mechanical engineering and minor in computer science from the University of Michigan—Ann Arbor. His current research topic is in the mechanics of materials involving thermal protection for reentry vehicles. Specifically, he is working on carbon fiber nano structural modeling and understanding the fiber/matrix interface using tools such as KMC and LAMMPS. 

Faculty Advisor: Harley Johnson