Research Publications & Contributions
As a Staff Gen AI/ML researcher and former CTO, I bridge the gap between cutting-edge research and production systems. My work focuses on making advanced AI accessible and efficient for real-world applications.
Published Research
RWKV: Reinventing RNNs for the Transformer Era
Linear Transformers Can Do Real-Time Language Modeling
Paper: arXiv:2305.13048
My Contributions: Core contributor studying infinite context length capabilities and efficient linear attention mechanisms.
Impact: This groundbreaking work enables language models with theoretically unlimited context while maintaining O(n) complexity, making it 10-100x more efficient than traditional transformers for long sequences.
Read Detailed AnalysisResearch Interests
Efficient Language Models
- Linear attention mechanisms and state-space models
- Model compression and quantization techniques
- Edge deployment optimization
- Resource-constrained AI systems
Production AI Systems
- Scalable inference architectures
- Real-time model serving
- Cost-efficient deployment strategies
- Multi-model orchestration
Generative AI Applications
- Multi-agent systems and coordination
- RAG (Retrieval-Augmented Generation) optimization
- Long-context processing
- Voice synthesis and storytelling AI
Industry Impact
My research contributions have directly influenced:
- FakeYou.com: Advanced voice synthesis using efficient models
- Open Source: Democratizing access to efficient AI through RWKV
Collaborations & Speaking
I’m open to research collaborations, speaking engagements, and consulting on:
- Efficient transformer architectures
- Production AI system design
- Small language model deployment
- Real-world AI applications