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| title: Organization Card | |
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| # Articulated Research Institute for Scientific Excellence | |
| **A.R.i.S.E.** is an applied machine learning research lab based in Bangladesh. We work wherever rigorous ML methodology meets real-world complexity — from financial markets and satellite imagery to regional languages, medical imaging, explainability, and beyond. | |
| Our research spans the full arc from early-stage ideas to published results. Some projects are foundational explorations; others carry peer-reviewed contributions and public datasets. What ties them together is a commitment to methodologically sound, reproducible science — with a focus on problems that matter in South Asian and low-resource contexts. | |
| We are affiliated with the **Department of Computer Science and Engineering, Southeast University, Bangladesh**. | |
| 🌐 [ariserl.org](https://ariserl.org) · 💼 [LinkedIn](https://bd.linkedin.com/company/ariserl24) · 📘 [Facebook](https://www.facebook.com/ariserl24) | |
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| ## What We Work On | |
| Our portfolio is intentionally broad. Past and ongoing work has touched: | |
| - **Financial ML** — structural break detection, coverage bias, regime analysis, and time series forecasting in emerging markets | |
| - **Remote Sensing** — satellite and aerial image analysis | |
| - **Regional Language NLP** — dialect-aware datasets and models for Bengali and related varieties | |
| - **Medical & Biomedical Imaging** — retinal disease detection, ocular health, and physics-informed vision | |
| - **Explainable AI** — gradient-based and attention-based interpretability on image models | |
| - **Multimodal & Fusion Architectures** — combining heterogeneous inputs for robust prediction | |
| This list grows. If something is in our pipeline, it will eventually surface here. | |
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| ## What You'll Find Here | |
| - **Datasets** — annotated and benchmark datasets released alongside our publications | |
| - **Model Checkpoints** — fine-tuned and adapter-based models from our research pipelines | |
| - **Reproducibility Artifacts** — configs, notebooks, and scripts to reproduce key results | |
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| ## Get in Touch | |
| We welcome collaboration with researchers working on applied ML problems, particularly in low-resource, domain-specific, or underrepresented settings. | |
| Reach us at [ariserl.org](https://ariserl.org) or connect on [LinkedIn](https://bd.linkedin.com/company/ariserl24). |