Workshop Tracks

Track 1: ML research addressing challenges faced by Muslim communities and research related to Islamic contexts

This track is open to all researchers, regardless of religious or ethnic background, and encourages submissions that promote the use of ML to support and empower Muslim communities globally, including in Muslim-majority countries. Published and/or In-progress work is welcome. Areas of interest include but are not limited to:

Growth of Muslims in ML: Initiatives that expand educational and professional opportunities for Muslims in ML, promote access, and encourage representation in the field.
ML for Social Good: Using ML/AI to engage Muslim youth, promote community outreach, enhance societal well-being, and improve governance, healthcare, or education in Muslim communities.
Language and Speech Technologies: Advancements in tools for languages spoken in Muslim communities, such as Arabic, Urdu, and Persian.
Computational Analysis of Islamic Scriptures: Digital humanities research, including computational studies of Hadith or Quranic texts.
Data Collection and Representation: Rich datasets from diverse Muslim communities.
Mitigating Algorithmic Bias and Discrimination: Research exploring strategies that mitigate algorithmic bias and related topics that impact Muslim individuals and communities.

Track 2: Machine learning research by Muslim authors

This track invites submissions from researchers who self-identify as Muslim. Published and/or In-progress work is welcome. We encourage work that explores the frontiers of machine learning and AI, including but not limited to:

LLMs, MLLMs, Generative AI: Research focused on large language models, generative AI systems, cutting-edge vision models, self-supervised learning, and advancements in multilingual capabilities, especially for languages spoken by Muslim communities.
Applications of ML: In vision, language, speech, and audio.
Machine Learning for Society: For example, in education, health, climate, and social sciences.
Probabilistic Methods: Deep learning, reinforcement learning, and optimization.
Social and Economic Aspects of ML: Fairness, interpretability, human-AI interaction, privacy, safety, and strategic behavior.
Federated Learning and AI-driven Innovations: For healthcare and smart cities and public services such as governance, energy, and traffic management.
Evaluation Methodologies and Scalable Infrastructures
Robotics and NeuroAI: Including research at the intersection of neuroscience, AI, and cognitive science.
AI Ethics and Safety: Data privacy, interpretable AI, green AI, red teaming in AI, etc.