I-DISC Fellow Projects

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I-DISC launched the I-DISC Fellows Program at the beginning of 2021. I-DISC Fellows are advanced doctoral students with expertise in data science, computation, and machine learning.  From bugs to stars and healthcare to linguistics, I-DISC Fellows have worked on a diverse range of projects with faculty from across the Lehigh community.


The I-DISC Fellow was very thorough in the information she provided regarding my request. She reached out to several people/resources to fully understand how I might tackle this study.
Bethlehem Farmer's Market vendor engaging with Lehigh University students

Project: Enjoyment and Willingness-to-Pay: Campus Field Study
PI: Daniel Zane (Marketing)

The PI wanted to learn about the feasibility of conducting a field study on campus or at the Bethlehem's Farmers Market, which is held on campus. The I-DISC Fellow was very thorough in the information she provided regarding my request. She reached out to several people/resources to fully understand how how I might tackle this study. 

Photo Credit: Matt Stanley (LU Media Library)

Insect populations are in steep decline

Project: What Does it Take to Love a Bug? The Role of Causal Knowledge in Caring
PI: Barbara Malt (Psychology)

This project conducts sentiment analysis on terms related to insects from public media such as newspapers, books, and films. The Fellow provided assistance with sentiment analysis software, wrote a script to scrape data from publicly available sources online, and helped set up the management of the data collected. 

(Photo Credit: Chameleon bee by Anderson Mancini under the Creative Commons Attribution 2.0 Generic license.)

…[an I-DISC Fellow] was able to complete within hours what it would have taken me WEEKS to complete, if I was able to do it at all. This was an invaluable service.
Trillions of dollars worth of goods are traded globally every year.

Project: International Trade Flows
PI: Mary Anne Madeira (International Relations)

This project required the collection and analysis of a very large amount of international trade data from the World Bank. The Fellow wrote code to download and post-process this data, and also helped train the PI on the relevant data-science concepts and implementation.

(Photo Credit: Container Ship MARUS by W. Bulach under the Creative Commons Attribution-Share Alike 4.0 International license.)

I learned how to process a large corpus of documents (800k) in a much shorter period of time than I [had] done previously.
Computational software to predict shear-induced red blood cell damage in blood-wetting devices.

Project: Development of a Cloud-based Software for Hemolysis Prediction
PI: Yaling Liu (Mechanical Engineering and Mechanics / BioEngineering)

This project develops computational software for hemolysis evaluation in medical devices. The Fellow proposed ways to speed up the inference stage of the machine learning algorithm, proposed a virtual environment to make the hardware setup more flexible, and helped design the logic for image segmentation.

(Image credit: Y. Liu)

Project: Machine Learning for Automated Radiographic Scoring
PI: Hannah Dailey (Mechanical Engineering)

This project uses machine learning to score radiographic images—for example, to identify bone fractures in X-ray images. The Fellow helped to assess the feasibility of the use of deep learning models for such scoring. X-rays (as depicted in the image on the right) are commonly used to examine the healing progress of a broken tibia (shinbone) and fibula. Machine learning techniques are a promising tool for automating the evaluation of medical images like these.

(Image Credit: H. Daley)

The discussion helped clarify an ambiguous question about optimal research directions and analysis tools available.
Risk of behaviors exposing to Ebola spillover decision tree validation results

Project: Social, Demographic, and Environmental Influence on Ebola Spillover
PI: Paolo Bocchini (Civil and Environmental Engineering)

This project uses social, demographic, and behavioral data to try to estimate a person's likelihood to engage in habits and practices that may lead to Ebola spillover. The PI and his team created preliminary regression and machine learning models, and the Fellow investigated the possibility of applying more sophisticated data-driven models.

(Image Credit: Paolo Bocchini)

Here are some other examples of the diverse range of projects our I-DISC Fellows have worked on since the start of the Fellows Program in 2021:

  • Measurement/protocol development for implicit and explicit assessment of cognition and behavior and evaluation of human applications and potential consequences of technologies (Electrical and Computer Engineering)
  • Machine Learning for Microfluidic Device Design (BioE/MEM)
  • Use Machine Learning to classify variable stars in astronomical surveys (Physics)
  • Scrape social media posts to identify consumer sentiment (Marketing)
  • Create a searchable database of children’s spontaneous stories (Psychology)
  • Write Python function to extract text from SEC forms (Accounting)