README.md

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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).