Pioneering Accessible AI Research.
We are decentralizing intelligence by focusing on offline models and small language models (SLMs) that empower individuals and local institutions, reducing dependency on big tech infrastructure.
Research Pillars
Offline-First Architectures
Developing models that operate entirely without internet connectivity, ensuring data sovereignty and reliability in remote environments.
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Optimizing massive LLMs into agile, highly efficient Small Language Models (SLMs) that run on local consumer hardware.
Embedded AI
Our research bridges the gap between software optimization and hardware constraints, tailoring AI for the next generation of RISC-V and ARM edge devices.
AI Optimization
Advancing techniques like quantization, knowledge distillation, and efficient fine-tuning to run powerful models on resource-constrained devices without sacrificing accuracy.
Rigorous Inquiry, Real Impact
Our multidisciplinary team combines academic depth with pragmatic engineering to solve the AI accessibility gap.
Quantization Strategies for Edge LLMs
Evaluating 4-bit and 2-bit quantization on low-power ARM architectures for real-time natural language processing tasks.
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Decentralized Intelligence & Data Privacy
A study on how local model deployment fundamentally shifts the power dynamic between users and monolithic tech corporations.
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Democratizing AI Literacy through SLMs
How affordable, offline models are transforming educational outcomes in bandwidth-limited regions around the globe.
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Join our multidisciplinary team of scientists and engineers. Apply to work on cutting-edge offline models, embedded AI, and ethical tech.