Explore our peer-reviewed papers, technical reports, and open-source contributions pushing the boundaries of machine learning, NLP, and responsible AI.
We introduce Nexus-SMoE, a routing mechanism that dynamically activates only 12% of parameters per token while maintaining 98.5% of dense model performance. Benchmarked on MMLU and HumanEval.
Addressing the gap in self-supervised methods for structured data, we propose TabCon, achieving state-of-the-art results on 14 UCI benchmarks with zero labeled data required during pretraining.
A novel vision transformer architecture optimized for edge deployment, achieving 45 FPS on Jetson Orin while maintaining 94% mAP on dark-condition datasets.
We present a post-processing calibration framework that reduces disparate impact by 67% without significant accuracy trade-offs, validated across three major financial datasets.
An automated framework for statistical process control in real-time inference streams, reducing model degradation incidents by 82% across enterprise deployments.
Combining vector search with structured entity relationships to reduce hallucination rates by 41% in domain-specific QA systems for healthcare and legal tech.
A fully open-source 7B parameter model trained on 1.2T tokens of text, code, and scientific literature. Achieves top-3 performance on BIG-Bench Hard while requiring 40% less compute than comparable models.
Our interdisciplinary team combines expertise in theoretical computer science, cognitive psychology, and distributed systems.
Head of NLP & Architecture
Lead ML Researcher
Computer Vision Director
AI Ethics & Safety
MLOps & Systems