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yasirali0/README.md

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👀 Check out my GitHub repositories:


▶️ About Me

  • 💼 Currently, I am working as an AI Developer at A-Tech Solution Co., Ltd..
    • This role is related to the development of LLM-based applications.
  • 💼 Previously, I worked as an AI Developer intern at HAMA Lab Co., Ltd. for two months.
    • My focus was on video recommendation systems where I performed tasks ranging from data analysis, deep learning-based model development and its dockerization up to Flask-based API development.
  • 👋 I did my Masters degree in Computer Engineering at Gachon University and worked as a Graduate Research Assistant at ISML Lab, Gachon University.
  • 🔭 My area of research in master's was federated learning, and my research topic was detection of poisoning attacks in federated learning.
  • 💻 I've been interested in programming since the very first time I took C++ course in my undergraduate degree.
    • I have programmed in various languages such as C++, C, JavaScript, and MATLAB, at a basic level.
    • I am proficient in Python and use it for research and development in machine learning and deep learning.

▶️ Experience

  • AI Developer | July 2024 - present | A-Tech Solution Co., Ltd., South Korea

    • Development of LLM-based Applications
  • AI Developer Intern | 03 March 2024 - 30 April 2024 | HAMA Lab Co., Ltd., South Korea

    • Video Recommendation System
      • Performed data analysis on video and user data within the database to formulate the research objectives.
      • Researched deep learning-based recommendation systems to select appropriate models and strategies tailored to our data.
      • Implemented data and machine learning pipelines and developed training and inference APIs using the Flask package.
      • Incorporated multi-threading strategy within the inference API to efficiently manage user requests and AI model inference simultaneously.
      • Utilized Docker for containerizing the recommendation system, ensuring portability and scalability of the solution.
  • Graduate Research Assistant | March 2022 - February 2024 | Information Security & Machine Learning Lab, Gachon University, South Korea

    • Research on Federated Learning

      • Conducted research in federated learning, focusing on the detection of poisoning attacks within the federated learning paradigm
      • Developed a federated learning framework using Python, PyTorch, and threading
      • Implemented deep learning models such as AlexNet, VGG16, and ResNet18 as the base models for the federated learning environment, and evaluated them on datasets such as MNIST, CIFAR-10, and CIFAR-100
      • Simulated poisoning attacks and analyzed their impact on the accuracy 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
    • Research on Tracing Attackers Over Overlay Networks

      • Collaborated with a colleague on this research project aimed at reducing the execution time and memory consumption of deep learning-based correlation attacks against Tor 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 two critical issues, high memory consumption and execution time
      • Successfully mitigated memory consumption challenge, reducing consumption from 133GB to 70GB through effective memory deallocation and proactive garbage collection strategies
      • Achieved a seven times reduction in execution time by leveraging GPU processing, facilitated by PyCUDA library.
      • Co-authored 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

    • Landslide Monitoring and Alert System
      • 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

▶️ Tools & Skills

  • Languages 👉 Python (Proficient) | JavaScript/TypeScript (Intermediate) | C/C++ (Beginner)

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

  • LLM Frameworks 👉 LangChain | LangGraph

  • Python Libraries 👉 NumPy | OpenCV | Matplotlib | Pandas | scikit-image | Tkinter | SQLAlchemy | threading

  • Development Tools 👉 Visual Studio Code | Jupyter Notebook | Git | GitHub | GitLab | Docker | FastAPI | Pydantic

  • AI Workflow Experience 👉 Model development | Model optimization | Dockerization | API development

  • Operating Systems 👉 Ubuntu | Windows

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


▶️ Research Publications

  • M. A. Hafeez, Y. Ali, K. H. Han and S. O. Hwang, "GPU-Accelerated Deep Learning-Based Correlation Attack on Tor Networks," in IEEE Access, vol. 11, pp. 124139-124149, 2023, doi:10.1109/ACCESS.2023.3330208. (Impact Factor: 3.9)
    • Code is available here.

🔗 Contact

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  1. APoT-Quant-for-MNIST APoT-Quant-for-MNIST Public

    Pytorch implementation of the Additive Powers of Two Quantization technique for deep learning models

    Python 4 1

  2. fast_correlation_attack_on_tor fast_correlation_attack_on_tor Public

    Official Implementation of "GPU-Accelerated Deep Learning-Based Correlation Attack on Tor Networks"

    Python 1

  3. abnormality_prediction abnormality_prediction Public

    Abnormality prediction for industrial air compressors

    Jupyter Notebook

  4. end-to-end-ml-project end-to-end-ml-project Public

    Jupyter Notebook

  5. image_to_audio_encryption image_to_audio_encryption Public

    This GUI Python program encrypts an image as audio

    Python 2

  6. image_segmentation image_segmentation Public

    Image Segmentation Using Pre-trained U-Net Model

    Jupyter Notebook