Developing Artificial Intelligence Imaging Tools for Material Design

energic phase .

Seeking undergraduate researcher to work on an NSF-funded project on computational imaging and artificial intelligence supporting the design of advanced material. Experience in Python programming is required.

Project description

This project aims to develop codes supporting the optimal design of material microstructure patterns using advanced imaging and data science, and uncertainty quantification methods.
Students will work on:

  •  Implementing novel neural networks training methods based on Bayesian inference
  • Training generative neural networks using imaging data of microstructure patterns
  • Integrating the trained network with other libraries for physics-based modeling, simulation, and optimization

Project outcome

The specific outcomes of this project will be identified by the faculty mentor at the beginning of your collaboration. 

Project details

Timing, eligibility and other details
Length of commitment Less thana  semester (about 2 mos)
Start time Summer (May/June of 2022)
In-person, remote, or hybrid? Hybrid Project 
Level of collaboration Individual student project 
Benefits Salary/Stipend
Who is eligible Juniors and Seniors with experience in python programming; neural network; GPU 

Project mentor

Danial Faghihi

Undergraduate Research Assistant

Mechanical and Aerospace Engineering

Phone: 716) 645-1450

Email: danialfa@buffalo.edu

Start the project

  1. Email the project mentor using the contact information above to express your interest and get approval to work on the project. (Here are helpful tips on how to contact a project mentor.)
  2. After you receive approval from the mentor to start this project, click the button to start the digital badge. (Learn more about ELN's digital badge options.) 

Preparation activities

Once you begin the digital badge series, you will have access to all the necessary activities and instructions. Your mentor has indicated they would like you to also complete the specific preparation activities below. Please reference this when you get to Step 2 of the Preparation Phase. 

  • Implementing novel neural networks training methods based on Bayesian inference
  • Training generative neural networks using imaging data of microstructure patterns
  •  Integrating the trained network with other libraries for physics-based modeling, simulation, and optimization