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• After that, I have written codes in other programming languages at some basic level, such as JavaScript, MATLAB, and C.
• Now, I mostly code in Python because it is more suitable for research and development in AI, machine learning, and deep learning.

Graduate Research Assistant | March 2022 - Present | Information Security & Machine Learning Lab, Gachon University, South Korea

• Research on Federated Learning

  • Developed a federated learning framework using Python, PyTorch, and threading
  • Implemented and evaluated the performance of various deep learning models e.g., AlexNet, VGG16, and ResNet18 within my federated learning codebase
  • Implemented and analyzed the impact of poisoning attacks on the performance of federated learning
  • Integrated state-of-the-art poisoning attack defense methods into the codebase for benchmarking purposes
  • Proposed a novel defense method that outperformed the state-of-the-art in terms of poisoning attack detection accuracy
  • Authored a research article currently under review in an IEEE journal
  • Currently surveying defense methods against poisoning attacks in asynchronous federated learning

• Research on Tracing Attackers Over Overlay Networks

  • Conducted a thorough survey on deanonymization attacks targeting the Tor overlay network, with a specific focus on deep learning-based correlation attacks
  • Performed an in-depth analysis of the prominent deep learning-based correlation attack, DeepCoFFEA identifying critical issues such as high memory consumption and correlation time
  • Successfully mitigated memory-related challenges, reducing consumption from 133GB to 70GB through effective memory deallocation and proactive garbage collection strategies
  • Achieved a seven times reduction in correlation time by leveraging GPU processing, facilitated by PyCUDA library.
  • Published a research article in IEEE Access journal, outlining the findings and implemented solutions

Intern | February 2021 - April 2021 | National Center of Artificial Intelligence at UET Peshawar, Pakistan

• Contributed to the Landslide Monitoring and Alert System Project
• Collected landslide videos to form a dataset for input into deep learning models
• Segmented and annotated videos into pre-landslide, landslide, and post-landslide phases by utilizing a custom Python script

Languages 👉 Python | C/C++ | JavaScript

ML/DL Frameworks 👉 PyTorch | Keras | TensorFlow | scikit-learn

Python Libraries 👉 NumPy | OpenCV | Matplotlib | Pandas | scikit-image | Tkinter | sqlite3 | PyCUDA | threading

Development Tools 👉 Visual Studio Code | Jupyter Notebook | Git | GitHub | Docker

Operating Systems 👉 Ubuntu | Windows

Soft Skills 👉 Communication | Teamwork | Problem-Solving | Critical Thinking

• Code is available here.