Computing[MTU] Showcase Poster Session Winners

Thank you to all for participating in the Computing[MTU] Poster Session on October 10, 2022. Congratulations to our winners! The research posters were evaluated using the following criteria: 1)clear discussion of background and hypothesis or objective; 2) relevance and significance: Is this novel research? Is the research timely and addressing a current need? 3) methods are clear and appropriately linked to hypothesis/objective; 4) results and conclusions are clear and address the objective; 5) good overall organization; and 6) clear and logical oral presentation.

View poster abstracts here.

View photos from the Poster Session here.


  • 1st: Dominika Bobik “An Educational Modeling Software Tool That Teaches Computational Thinking Skills”
  • 2nd: Niccolo Jeanetta-Wark “Performance Measurement of Trajectory Tracking Controllers for Wheeled Mobile Robots”
  • 3rd: Kristoffer Larsen “A machine learning-based method for cardiac resynchronization therapy decision support”


  • 1st: Shashank Pathrudkar “Interpretable machine learning model for the deformation of multiwalled carbon nanotubes”
  • 2nd: Nicholas Hamilton “Enhancing Visualization and Explainability of Computer Vision Models with Local Interpretable Model-Agnostic Explanations (LIME)
  • 3rd (Tie):
    • Zonghan Lyu “Automated Image Segmentation for Computational Analysis of Patients with Abdominal Aortic Aneurysms”
    • Tauseef Mamun “When to be Aware of your Self-Driving Vehicle: Use of Social Media Posts to Understand Problems and Misconceptions about Tesla’s Full Self-Driving Mode”

Honorable Mentions

  • Dharmendra Pant, “DFT-aided Machine Learning-based discovery of Magnetism in Fe-based Bimetallic Chalcogenides”
  • Chen Zhao, “Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images”
  • Abel A. Reyes-Angulo, “GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification”
  • Suresh Pokharel, “Improving Protein Succinylation Sites Prediction Using Embeddings from Protein Language Model”