Posts by Collection

projects

Astro-Turfing Review System

  • The search for applying machine learning to real world problems led me to work in the area of Astro-turfing. This is a filtering model with machine learning algorithm to filter-out the fake reviews and hence the fake reviewers from committing review frauds. I used amazon reviews data sets to built a stochastic model on top of it.

Astro-Turfing Review System

  • The search for applying machine learning to real world problems led me to work in the area of Astro-turfing. This is a filtering model with machine learning algorithm to filter-out the fake reviews and hence the fake reviewers from committing review frauds. I used amazon reviews data sets to built a stochastic model on top of it.

Facial Recognition and Verification System

  • Working with the accuracies and flaw removal strategies with re-implementation of Open-Face/Googles Face-Net, for improving the range of applications in the domain of Security.
  • Resolved the false positive 2-D inputs by introducing more features in Stage 1 (face detection) as a.) Orientation Normalization b.) 3D surface representation.

Facial Recognition and Verification System

  • Working with the accuracies and flaw removal strategies with re-implementation of Open-Face/Googles Face-Net, for improving the range of applications in the domain of Security.
  • Resolved the false positive 2-D inputs by introducing more features in Stage 1 (face detection) as a.) Orientation Normalization b.) 3D surface representation.

Memory Defense: More Robust Classification via a Memory-Masking Autoencoder

  • We developed a robust autoencoder with one-hot memory masking to mitigate adversarial attacks.
  • The proximity approximation model can retrieve an image’s relevant memory features and reconstruct it with a repaired label.
  • The enhanced deep neural architecture significantly improved the robustness of DNN for an image classification task.

Memory Defense: More Robust Classification via a Memory-Masking Autoencoder

  • We developed a robust autoencoder with one-hot memory masking to mitigate adversarial attacks.
  • The proximity approximation model can retrieve an image’s relevant memory features and reconstruct it with a repaired label.
  • The enhanced deep neural architecture significantly improved the robustness of DNN for an image classification task.

Face-Mask Detection on real-world Webcam dataset

  • Successfully collected more than 1000 Gb of public webcam data, by capturing image frames periodically from over 75 webcams across United States.
  • Applied Coco-annotation semi-automated labelling to develop ground-truth labels.
  • Re-implemented 13 state-of-the-art face detection algorithms for face detection & face mask detection to analyze their effectiveness in real-world dataset.
  • Reported face mask usage across United States from Jun’ 2020 to Mar’ 2021.

Face-Mask Detection on real-world Webcam dataset

  • Successfully collected more than 1000 Gb of public webcam data, by capturing image frames periodically from over 75 webcams across United States.
  • Applied Coco-annotation semi-automated labelling to develop ground-truth labels.
  • Re-implemented 13 state-of-the-art face detection algorithms for face detection & face mask detection to analyze their effectiveness in real-world dataset.
  • Reported face mask usage across United States from Jun’ 2020 to Mar’ 2021.

publications

talks

teaching

CSE 017 - Programming and Data Structures (Fall 2019)

Undergraduate Course - Teaching Assistant, Lehigh University, Department of Computer Science & Engineering, 2019

  • Instructors -
    • Prof. Arielle Carr - CSE017(010), CSE017(011)
    • Prof. Houria Oudghiri - CSE017(012)

This course will cover the design and implementation of algorithms using Java. It assumes that students have had prior experience using conditional statements, loops, arrays, etc., in Java, and will build on this knowledge to develop a full understanding of proper object-oriented programming, algorithmic techniques (e.g., divide-and-conquer, recursion), and the design of data structures (e.g., queues, stacks, trees).

CSE 017 - Programming and Data Structures (Fall 2020)

Undergraduate Course - Teaching Assistant, Lehigh University, Department of Computer Science & Engineering, 2020

  • Instructors -
    • Prof. Houria Oudghiri

This course will cover the design and implementation of algorithms using Java. It assumes that students have had prior experience using conditional statements, loops, arrays, etc., in Java, and will build on this knowledge to develop a full understanding of proper object-oriented programming, algorithmic techniques (e.g., divide-and-conquer, recursion), and the design of data structures (e.g., queues, stacks, trees).

work