I work on computer vision that holds up outside the lab: on crowded roads, in classrooms and in humanitarian settings.
I give what I learn away for free.
I am looking for a fully funded PhD in AI and for sponsors. Every sponsored project is released open for all.

• 🎯 Seeking: a fully funded PhD in AI, in computer vision and deep learning.
• 🔬 Research focus: robust, efficient and fair vision models that work outside benchmark conditions, for road safety, humanitarian settings and education.
• 📝 Research record:
  • 1 peer-reviewed paper, published by Springer Nature (2026): Emotion Detection at Lightning Speed: Leveraging YOLOv11 for Facial Expression Analysis, Lecture Notes in Networks and Systems (ICCTRDA 2025).
  • 1 paper under review: a three-seed benchmark of nano YOLO detectors for blood-cell detection, submitted to IEEE ISBI 2027 (with H. J. Shethil and J. A. Aranna).
  • 2 manuscripts in preparation: blood-cell classification, calibration and label audit (target IEEE EMBC 2027); people counting in 4K drone video.
  • 3 undergraduate theses: pedestrian risk, vehicle over-speed and brain-tumor classification.
• 💼 Now: Advanced ICT Officer at CNRS-UNHCR since October 2025. I teach students digital and ICT skills in a humanitarian setting.
• 🧪 Before: Research Assistant at Robo Tech Valley, Dhaka (March–July 2025), working on AI/ML and computer-vision projects.
• 🏛️ Education: B.Sc. in Computer Science & Engineering, Ahsanullah University of Science and Technology (AUST), 2025.
• 🌍 Open science:
  • 273 free written lectures and 110 free animated video lectures;
  • every sponsored project is released open for all.
• ⚡ Algorithms: 12+ years of competitive programming (since 2014).

My research interests centre on computer vision and deep learning for real-world safety and human understanding, particularly in developing countries such as Bangladesh.

My undergraduate research tackled pedestrian detection with risk-level classification for Bangladeshi road scenes, and vision-based vehicle over-speed detection. My paper on real-time facial-expression analysis with YOLOv11 was published by Springer Nature in 2026. My current work benchmarks YOLO detectors for blood-cell detection in microscopy images.

I want to develop robust, efficient and fair vision models that work reliably outside benchmark conditions, for road safety, humanitarian applications and education.

A. Giri, J. A. Apurba, A. Z. M. J. Uddin, S. I. Rizon, M. R. Begum, A. S. M. A. S. Akib, M. Giri, B. Shahi, M. Khadgi. “Emotion Detection at Lightning Speed: Leveraging YOLOv11 for Facial Expression Analysis.” In Proceedings of the International Conference on Computational Technologies for Research in Data Analytics (ICCTRDA 2025), Lecture Notes in Networks and Systems, Springer Nature, 2026, pp. 218–233. DOI: 10.1007/978-3-032-27666-7_19

🎓 Research profiles: Google Scholar · ORCID · ResearchGate · Academia.edu

1. Beyond the benchmark: How can detectors stay reliable on dense, unstructured roads, with rickshaws, buses and pedestrians sharing a lane in rain or at night, when they were trained on data from elsewhere?
2. Real-time on modest hardware: How far can YOLO-class models be compressed for edge devices without losing accuracy on the cases that matter most for safety?
3. Fair human understanding: Do expression and pose models work equally well across ages, skin tones and cultures? How do we measure the gaps and close them?
4. AI for humanitarian settings: How can vision and learning systems responsibly support education and services in refugee and low-resource contexts?

I build in the open, but compute, data and publishing cost real money.

My pledge: when a project is sponsored, I release it openly and free for anyone to learn from, use and build on. That includes:

• the source code;
• the trained model weights;
• the datasets, with personal data anonymised and consent respected;
• an open-access paper;
• lessons on my free teaching sites.

What sponsorship pays for

• 🖥️ GPU compute to train and evaluate vision models
• 🏷️ Collecting and annotating real-world data, such as Bangladeshi road scenes
• 📄 Open-access publication fees, so the papers are free to read
• 🎬 More free lectures and animated lessons
• ✈️ Travel to present the work at conferences

Projects open for sponsorship

Sponsors get:

• acknowledgement in the repository, papers and lectures;
• regular progress updates;
• a direct line to me throughout the project.

• The AI Lecture Hall · source
  • 273 university-level lectures in 10 tracks.
  • Covers AI foundations and mathematics through transformers, diffusion models, reinforcement learning, MLOps and AI ethics.
  • Each lecture includes derivations, code and exercises.
• AI in Motion · source
  • 110 animated video lectures: more than 8 hours, including 36 deep dives of 10+ minutes.
  • Narration, subtitles, chapters, quizzes and printable illustrated notes.
  • Every animation is computed live from the real algorithm: gradient descent really descends and Q-learning really learns.
• Teaching at CNRS-UNHCR: ICT and digital skills for students.
• YouTube: Study with Janin and Pomodoro Study with Janin.

• 12+ years of contest problem solving, since 2014, on Codeforces, CodeChef, HackerRank and other platforms.
• Recognised for outstanding performance in online programming contests.
• 📈 Profile: StopStalk
• 📂 Archives:
  • Competitive-programming resources collection
  • Codeforces solutions

Area	Tools
AI and machine learning
Algorithms and systems
Web and backend
Data
Tools

• Advanced Learning Algorithms · DeepLearning.AI & Stanford Online
• Unsupervised Learning, Recommenders, Reinforcement Learning · DeepLearning.AI & Stanford Online
• Divide and Conquer, Sorting and Searching, and Randomized Algorithms · Stanford University
• Advanced Algorithms and Complexity · UC San Diego
• Object-Oriented Data Structures in C++ · University of Illinois Urbana-Champaign
• Data Scientist Career Track · 365 Data Science: exams in ML, deep learning, SQL, mathematics, probability and statistics
• Convolutional Neural Networks with TensorFlow in Python · 365 Data Science
• MLOps Bootcamp · DataMites
• Introduction to DevOps · IBM

📁 All 39 certificates (Google Drive)

• 🥇 Talent Pool Scholarship (2008): ranked 4th in Kurigram District.
• 🏆 Competitive-programming awards for performance in online contests.
• 🧠 Microsoft MVP Program trainee, Dhaka: workshop on AI, computer vision and Microsoft Azure.
• 🔶 Google Developer Group TensorFlow workshop, Dhaka: AI, machine learning and data mining.

• 📧 Email: japurba97@gmail.com
• 🌐 Academic portfolio: janin-a-apurba.vercel.app
• 🎓 Google Scholar: Janin A Apurba · ORCID: 0009-0002-8576-284X
• 🔬 ResearchGate: Janin-Apurba · Academia.edu: JaninAApurba
• 📄 CV: Google Drive
• 👥 References: academic references from AUST CSE faculty are available on request.

If my work helped you, please follow and subscribe:

• Study with Janin: youtube.com/@studywithjanin
• Pomodoro Study with Janin: youtube.com/@pomodorostudywithjanin3326

_{© Janin A Apurba, CSE, AUST · Advanced ICT Officer, CNRS-UNHCR}