Experience

 
 
 
 
 

Summer Internship

Livermore Software Technology Corporation (LSTC)

Jun 2019 – Sep 2019 Livermore, CA
I am working with the computational and multi-scale mechanics group and the LS-OPT software team on some projects.
 
 
 
 
 

Visiting Scholar

Ford Motor Company

Jul 2018 – Sep 2018 Dearborn, MI
I worked in the materials and manufacturing group in the Research and Innovation Center at Ford, mainly on developing methods for the design of carbon fiber reinforced polymer(CFRP) composite materials and products.
 
 
 
 
 

Research Assistant

Northwestern University

Sep 2015 – Present Evanston, IL
Various projects on fiber and nanoparticle composite materials design, uncertainty quantification and propagation, surrogate modeling of PDE-based engineering systems, etc. (see below)
 
 
 
 
 

Teaching Assistant/Grader

Northwestern University

Sep 2015 – Present Evanston, IL

Courses include (200+: undergraduate, 300+: undergraduate/graduate, 400+: graduate):

  • ME341 Computational Methods for Engineering Design

  • ME441 Engineering Optimization

  • ME220 Thermodynamics

  • ME340-1 Manufacturing Processes

 
 
 
 
 

Summer Session

University of California, Berkeley

Jul 2013 – Aug 2013 Berkeley, CA

Projects

Bike sharing data analysis

(STAT352: Nonparametric statistical methods). Nonparametric regression of bike sharing time and count data along with other environment variables (weather, temperature, etc.).

Financial time series data prediction with TensorFlow

(EECS495: Deep neural networks). Recurrent neural network (RNN) modeling of the S&P 500 index with the forward chaining validation method for time series.

Metamodel based design optimization of nanocomposites

Multi-response Gaussian process modeling of molecular dynamics (MD) simulation data for a nanocomposite system. Statistical sensitivity analysis for dimension reduction. Optimal design for strength and toughness obtained for the material.

Smartphone-based recognition of human activities and postural transitions

(EECS349: Machine learning). Machine learning predictions of human activities based on smartphone sensor data in both time and frequency domain.

Computational design of carbon fiber reinforced polymer (CFRP)

Stochastic reconstruction, uncertainty quantification, and surrogate modeling of the CFRP material system. Design of multi-component CFRP parts including geometry, material and process selection.

Presentations

Multiscale and Multidimensional Quantification and Propagation of Manufacturing Induced Uncertainty in Fiber Reinforced Composites

Poster presentation

Microstructure Reconstruction of Sheet Molding Composite Using a Random Chips Packing Algorithm

Awards

Student Travel Award

2019 USACM Conference on Uncertainty Quantification in Computational Solid and Structural Materials Modeling

Predictive Science & Engineering Design Interdisciplinary Cluster Fellowship

Awarded to support an interdisciplinary team project on uncertainty quantification in multi-scale modeling for engineering design

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