Pushing the boundaries of artificial intelligence through fundamental research in neural architectures, quantum-inspired computing, and distributed systems
Novel approach to conditional computation using dynamic routing networks with O(log n) scaling.
Revolutionary approach to sequence modeling achieving O(1) attention complexity.
Bridging quantum computing principles with classical neural networks.
Dynamic sparsity patterns enable efficient lifelong learning without catastrophic forgetting in neural networks.
Introducing graph embeddings for causality inference across multimodal datasets, including text, images, and time series.
Neuromorphic-inspired architectures achieving millisecond-level decision-making with ultra-low energy consumption.
Decentralized training ensuring complete data privacy using cryptographic zero-knowledge verification.
Generative AI integrated with physical constraints to design novel superconducting materials.
A hybrid model combining Gaussian processes with recurrent networks to predict non-stationary time series.
Leveraging quantum annealing to solve coordination problems in large-scale multi-agent systems.
Introducing topological features into graph networks for enhanced understanding of relational data.
Combining neural networks with symbolic logic for interpretable discoveries in high-dimensional datasets.
Developing modular AI components for dynamic microservice deployment and scaling in real-time environments.
Working with leading universities and research institutions to advance the field of AI