RESEARCH &
DOCUMENTATION

Pruning, quantization, and knowledge distillation for efficient transformers. Reducing model size without sacrificing performance.

Privacy-preserving ML across distributed networks. Byzantine-robust aggregation for trustless environments.

Training on multiple data types simultaneously. Vision-language models and cross-modal alignment techniques.

Real-time ML on edge devices. Hardware-aware neural architecture search and optimization strategies.

Sequential task learning without catastrophic forgetting. Memory replay and regularization methods.

Diffusion models and advanced generative systems. Text-to-image synthesis and content generation at scale.

Hybrid quantum-classical algorithms for optimization. QAOA and VQE implementations with error mitigation.

Surface codes and topological error correction. Fault-tolerant quantum computing pathways and overhead analysis.

Quantum kernels and neural networks. Demonstrating quantum advantage for classification and pattern recognition.

Gate-level optimization and circuit compilation. Depth reduction for NISQ devices with hardware constraints.

Advanced consensus protocols for high-throughput systems. Practical BFT implementations and optimizations.

Layer-2 solutions and sharding protocols. Cross-chain bridges and interoperability mechanisms.

zk-SNARK and zk-STARK implementations. Privacy-preserving verification and scalable proof systems.

Proof-of-Stake, Proof-of-Work variants, and alternative consensus mechanisms. Energy efficiency and security tradeoffs.

NeRF and neural radiance fields. Real-time rendering of complex scenes using learned representations.

High-performance 3D graphics pipelines. Ray tracing and path tracing optimization techniques.