[{"content":"Hlog: Hasan Algafri\u0026rsquo;s Blog I am a Master\u0026rsquo;s student in Artificial Intelligence \u0026amp; Data Science at Heinrich Heine University Düsseldorf (HHU). My research interests span Deep Learning, including but not limited to Representation Learning, Generative Models, and Reinforcement Learning.\nResearch \u0026amp; Interests Recently, my research has focused on developing efficient and innovative model architectures and training strategies. I also explore other fascinating topics such as the Theory of Deep Learning, Interpretability, and Multimodal Learning.\nWhat You\u0026rsquo;ll Find Here This blog serves as a platform to share my thoughts, research insights, and explorations in the field of AI. I write about various topics including:\nDeep Learning Research: Novel architectures, training methodologies, and theoretical insights Technical Tutorials: Practical guides and implementations of AI/ML concepts Research Notes: Summaries and analyses of interesting papers and trends in the field Get in Touch I\u0026rsquo;m always interested in discussing research ideas, potential collaborations, or just connecting with fellow researchers and enthusiasts in the AI community. Feel free to reach out through the contact information.\n📄 Download PDF Version of my CV for a complete overview of my academic and professional background.\n","permalink":"https://hasanoj.github.io/about/","summary":"\u003ch1 id=\"hlog-hasan-algafris-blog\"\u003eHlog: Hasan Algafri\u0026rsquo;s Blog\u003c/h1\u003e\n\u003cp\u003eI am a Master\u0026rsquo;s student in Artificial Intelligence \u0026amp; Data Science at Heinrich Heine University Düsseldorf (\u003ca href=\"https://www.heicad.hhu.de/lehre/masters-programme-ai-and-data-science\"\u003eHHU\u003c/a\u003e). My research interests span Deep Learning, including but not limited to Representation Learning, Generative Models, and Reinforcement Learning.\u003c/p\u003e\n\u003ch2 id=\"research--interests\"\u003eResearch \u0026amp; Interests\u003c/h2\u003e\n\u003cp\u003eRecently, my research has focused on developing efficient and innovative model architectures and training strategies. I also explore other fascinating topics such as the Theory of Deep Learning, Interpretability, and Multimodal Learning.\u003c/p\u003e","title":""},{"content":"📄 Download PDF Version\n🎓 Education Master of Science (M.Sc.) – Artificial Intelligence and Data Science\nHeinrich Heine University Düsseldorf, Germany\n10/2023 – Present\nhttps://www.hhu.de/\nBachelor of Science (B.Sc.) – Computer Science\nKing Fahd University of Petroleum \u0026amp; Minerals, Saudi Arabia\n09/2018 – 06/2023\nGPA: 3.94 / 4.00\nConcentration: Artificial Intelligence \u0026amp; Machine Learning\nKey Courses:\nDeep Learning Machine Learning Natural Language Processing Computer Vision https://www.kfupm.edu.sa/\n💼 Work Experience Undergraduate Research Assistant SDAIA-KFUPM Joint Research Center for Artificial Intelligence\n10/2022 – 06/2023\nConducted a comprehensive literature review on semi-supervised learning methods for sign language recognition Developed a semi-supervised model for sign language recognition using pose-based inputs and Transformer architecture Ran multiple experiments to evaluate the model performance on the WLASL dataset Collaborated with research team to publish findings in international venues Research Intern Undergraduate Research Office at KFUPM\n06/2022 – 08/2022\nEvaluated DarKnight, a privacy-preserving deep neural network training framework Conducted empirical analysis using TensorFlow-based models on various datasets Presented research findings at the SURE (Summer Undergraduate Research Experience) Exhibit Day Gained experience in privacy-preserving machine learning techniques 📚 Publications SSLR: A Semi-Supervised Learning Method for Isolated Sign Language Recognition\nHasan Algafri et al.\n2025 | arXiv:2504.16640\nDesigned a novel semi-supervised learning framework addressing the challenge of limited labeled data for sign language recognition Utilized pose-based inputs and Transformer backbone architecture for improved performance Demonstrated significant improvements over baseline methods on standard benchmarks Style Change Detection using Discourse Markers\nHasan Algafri et al.\n2022 | CEUR Workshop Proceedings\nDeveloped a classification algorithm to detect style changes in multi-authored documents Implemented discourse marker analysis for improved detection accuracy Published and presented at CLEF PAN 2022 conference Last updated: August 2025\n","permalink":"https://hasanoj.github.io/cv/","summary":"\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://drive.google.com/file/d/1Tcye0O9dpmQrjPzQGnHYmNFw2BOwGuB5/view?usp=sharing\"\u003e📄 Download PDF Version\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2 id=\"-education\"\u003e🎓 Education\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eMaster of Science (M.Sc.)\u003c/strong\u003e – Artificial Intelligence and Data Science\u003cbr\u003e\n\u003cem\u003eHeinrich Heine University Düsseldorf\u003c/em\u003e, Germany\u003cbr\u003e\n\u003cstrong\u003e10/2023 – Present\u003c/strong\u003e\u003cbr\u003e\n\u003ca href=\"https://www.hhu.de/\"\u003ehttps://www.hhu.de/\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cstrong\u003eBachelor of Science (B.Sc.)\u003c/strong\u003e – Computer Science\u003cbr\u003e\n\u003cem\u003eKing Fahd University of Petroleum \u0026amp; Minerals\u003c/em\u003e, Saudi Arabia\u003cbr\u003e\n\u003cstrong\u003e09/2018 – 06/2023\u003c/strong\u003e\u003cbr\u003e\n\u003cstrong\u003eGPA\u003c/strong\u003e: 3.94 / 4.00\u003cbr\u003e\n\u003cstrong\u003eConcentration\u003c/strong\u003e: Artificial Intelligence \u0026amp; Machine Learning\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey Courses\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDeep Learning\u003c/li\u003e\n\u003cli\u003eMachine Learning\u003c/li\u003e\n\u003cli\u003eNatural Language Processing\u003c/li\u003e\n\u003cli\u003eComputer Vision\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003ca href=\"https://www.kfupm.edu.sa/\"\u003ehttps://www.kfupm.edu.sa/\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2 id=\"-work-experience\"\u003e💼 Work Experience\u003c/h2\u003e\n\u003ch3 id=\"undergraduate-research-assistant\"\u003eUndergraduate Research Assistant\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eSDAIA-KFUPM Joint Research Center for Artificial Intelligence\u003c/strong\u003e\u003cbr\u003e\n\u003cstrong\u003e10/2022 – 06/2023\u003c/strong\u003e\u003c/p\u003e","title":"Curriculum Vitae"}]