Looking for help with the data science, machine learning, artificial intelligence, or computing part of your research? Or looking for help with the social science and human subject data collection part of your research? An I-DISC Fellow can help!–offering expertise in programming, computation, data science and social science.

I-DISC Fellows are advanced doctoral students with expertise in programming, computation, data science or social science. The I-DISC Fellows program will now provide a part-time research assistant to help Lehigh faculty with the data science, programming, computational projects and social science aspects of their research.

What you get:
Time from a computer science doctoral student or a social psychology doctoral student, up to 4 weeks @ 10 hours/week effort. No need to advertise or interview candidates.

The cost:
NONE for Spring Semester 2024! 

How to apply:
Interested faculty should complete a REQUEST FORM and provide a brief summary of project, time needed and scheduling constraints. 
Now accepting applications for SPRING SEMESTER 2024.

Examples of previous I-DISC Fellow Projects

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 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.

Project: Using the Burrito Optimization Game for Education
PI: Larry Snyder (Industrial & Systems Engineering)

The Burrito Optimization Game is a free educational game that I built with the optimization software company Gurobi. The game introduces learners to basic concepts in optimization. With colleagues at Lehigh and JMU, we are writing a paper on the use of the game in classroom settings, collecting data about the effectiveness of the game in introductory optimization courses. Alex was instrumental in shepherding us through the IRB process, from drafting many of the IRB materials, to communicating with IRB on our behalf, to developing research protocols and scripts, to building the survey instruments. She educated us about best practices for human-subjects research, including common pitfalls that can invalidate results, and provided feedback on measures to best fit quantitative human subjects research. 

It is not an exaggeration to say that Alex saved us dozens of hours of work. Alex is an expert in human-subjects research and IRB approvals, things that we have little or no experience with. Having her help us with those aspects of the research freed us up to concentrate on the aspects of our project that are closer to our core research interests. This was immensely valuable.

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)

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.)

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.)

Computational software predicts 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: 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) Risk of behaviors exposing to Ebola spillover decision tree validation results

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)