I build research-driven machine learning systems - from datasets & explainable models to deployed applications. My work combines Computer Vision, Signal/Audio analysis, and classical ML for practical health & agriculture solutions.
- Research-first mindset: multiple conference papers on ML, explainability, and medical/chemical triaging.
- End-to-end implementation: dataset curation → model training → evaluation → deployment (MERN / Flask / Flutter).
- Leadership & collaboration: Founder & President at CollabCircle - coordinating ML research projects with peers.
- Languages: Bangla (native), English (fluent), German (beginner).
| Project | What I built | Tech | Outcome |
|---|---|---|---|
| Tomato Leaf Disease Detection | Large image dataset → trained/compared EfficientNetB3 & VGG16 → deployment-ready pipeline | Python, TensorFlow/Keras, OpenCV | Journal draft / Kaggle-ready dataset |
| Parkinson's Disease Detection (Voice) | Feature engineering + SVM/XGBoost ensemble for voice signals | Python, scikit-learn, librosa | Conference paper + poster |
| CollabCircle Official Website | Organization website for ML research collaboration | MERN (React, Node, Express, MongoDB) | Live / community onboarding |
| Heart Disease Detection | Multi-model pipeline combining clinical features with survival analysis; explainability added via SHAP | Python, scikit-learn, lifelines, SHAP, Pandas | Conference paper / explainable model for clinical research |
| Bengali Digit Recognition (Attention Network) | Custom attention mechanism applied to handwritten digit classification in Bengali | PyTorch, CNN + attention modules | Conference paper / improved recognition baseline |
| Vehicle Management System | Full-stack vehicle management web application for operations & record-keeping | MERN (React, Node, Express, MongoDB) | Deployed demo / admin + user dashboards |
Each project has a dedicated repo - see my repositories for code, datasets, and notebooks.
Machine Learning & Research:
Python · scikit-learn · PyTorch · TensorFlow · OpenCV · librosa · Pandas · NumPy · Matplotlib · Seaborn · EDA · Feature Engineering · PCA · Model Explainability (SHAP/LIME)
Web & Full Stack:
JavaScript · React · Node.js · Express · MongoDB · MERN · Flask · REST APIs · Tailwind CSS · Deployment (Render/ Heroku / Vercel / Docker)
Other Languages / Tools:
C++ · Dart / Flutter · Java · C# · SQL · Git · LaTeX · Google Colab · Kaggle
Soft / Research Skills:
Academic research, experimental design, technical writing, teamwork, project management, communication, time management, and emotional intelligence.
- Machine Learning & AI fundamentals (Bangladesh EDGE, IBM, Simplilearn, Kaggle certificates)
- Data Visualization & Computer Vision (Kaggle)
- Career Skills in Data Analytics (LinkedIn Learning)
- Email: smrizvi.i29@gmail.com
- LinkedIn: https://www.linkedin.com/in/smri29/
- Kaggle: https://www.kaggle.com/shahmohammadrizvi
- CollabCircle: collabcircle.official@gmail.com
Open to: research collaboration, internship opportunities, mentoring, and project contributions.
- 🔎 Check my repositories to see code + demos.
- 📩 Want to collaborate? Send a short email with “Collab_smri29” in the subject.
- 📜 Need my CV? DM for the latest copy.
Built with ☕ and a lot of curiosity | From Bangladesh to breakthroughs... | © Shah Mohammad Rizvi
