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Work Experience

Ph.D. Candidate, Hoboken, NJ​

Jan 2019 - Dec 2022

Stevens Institute of Technology, Department of Electrical and Computer Engineering

  • Developed a neural model to identify timing jitter in QAM signal, resulted in a publication at the WOCC21 conference, and presented in an IEEE symposium
  • Modeled self-designed datasets for various research projects, which were adopted in IEEE publications
  • Created experiments and implementation in deep learning architectures, which resulted in 7 IEEE publications

Teacher Assistant in Engineering Programming C++, Hoboken, NJ​

Jan 2022 - Dec 2022

Stevens Institute of Technology

  • Initiated ten new weekly assignments for the course curriculum. And led weekly Q\&A sessions for 30+ students
  • Managed assignment submission logistics using GitHub classroom and Gtest tools to test C++ code

Teacher Assistant in Introduction to Communication Systems, Hoboken, NJ​

Aug 2021 - Dec 2021

Stevens Institute of Technology

  • Constructed five labs session to simulate communications system fundamentals using MATLAB, which is adapted in the course curriculum for future intakes
  • Mentored 20+ students and taught tutorial and exam review sessions by evaluating students' performance reports

Research Assistant, Hoboken, NJ Aug​

2018 - Jun 2019

Stevens Institute of Technology

  • Investigated ``Fast Detection of Brains With Abnormalities" using unsupervised learning that led to an accepted poster presentation at the Organization for Human Brain Mapping (OHBM) Italy 2019
  • Utilized a nilearn Python package to investigate hiding features in MRI scans and searched for the possibility of implementing a 3D model
  • As result applied: nilearn, PyTorch, ReNA algorithm, and traumatic brain injuries tracktbi pilot dataset (https://aalmarhabi.github.io/mri-rena)

Graduate Research, Hoboken, NJ​

Jan 2017 - Dec 2018

Stevens Institute of Technology

  • Built a prototype for different languages font Recognition (DLFR) to recognize English, Arabic, and Japanese languages with a Raspberry Pi camera. As a result applied: Keras, TensorFlow, OpenCV, and Raspbian operating system (https://sites.google.com/stevens.edu/font-recognition/)
  • Designed and optimized machine learning algorithms for Breast Cancer Diagnostic using the University of Wisconsin breast cancer dataset and improved models accuracy of KNN classifier to 96 % and SVMs to 97 %