- 💼 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.
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AI Developer | July 2024 - present | A-Tech Solution Co., Ltd., South Korea
- Development of LLM-based Applications
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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.
- Video Recommendation System
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Graduate Research Assistant | March 2022 - February 2024 | Information Security & Machine Learning Lab, Gachon University, South Korea
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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
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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
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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
- Landslide Monitoring and Alert System
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Languages 👉 Python (Proficient) | JavaScript/TypeScript (Intermediate) | C/C++ (Beginner)
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ML/DL Frameworks 👉 PyTorch | Keras | TensorFlow | scikit-learn
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LLM Frameworks 👉 LangChain | LangGraph
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Python Libraries 👉 NumPy | OpenCV | Matplotlib | Pandas | scikit-image | Tkinter | SQLAlchemy | threading
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Development Tools 👉 Visual Studio Code | Jupyter Notebook | Git | GitHub | GitLab | Docker | FastAPI | Pydantic
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AI Workflow Experience 👉 Model development | Model optimization | Dockerization | API development
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Operating Systems 👉 Ubuntu | Windows
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Soft Skills 👉 Communication | Teamwork | Problem-Solving | Critical Thinking
- 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.
- Let's connect on Linkedin: LinkedIn