3. Data, Science & AI

Accelerates LLM inference with RadixAttention prefix caching for structured JSON/regex outputs, constrained decoding, and agentic workflows.

High-performance data manipulation library using Apache Arrow for efficient filtering, grouping, and I/O operations in data analysis workflows.

High-performance RLHF framework for training large language models (7B-70B+) using Ray, vLLM, and ZeRO-3, supporting PPO, DPO, and distributed training.

Provides activation-aware weight quantization for 4-bit LLM compression, enabling 3x speedup with minimal accuracy loss on limited GPU memory.

Provides an RNN-Transformer hybrid architecture for efficient AI inference with linear time complexity, infinite context, and production-ready large language models.

Comprehensive Python toolkit for creating, analyzing, and visualizing complex networks and graphs with graph algorithms and community detection capabilities.

Provides constitutional AI training for aligning language models with human values using self-critique and AI feedback to reduce harmful outputs without human labels.

Provides expert guidance for fine-tuning language models using GRPO and RL with the TRL library, focusing on reasoning and task-specific training.

Enables image understanding, visual question answering, and multi-turn image-based conversations using CLIP vision encoder and LLaMA language model.

Accelerates LLM inference with NVIDIA TensorRT for high throughput, low latency, and quantization support on NVIDIA GPUs.

Analyzes Google Analytics data to provide insights on website performance, traffic patterns, and conversion rates, enabling data-driven optimization strategies.

Accelerates LLM inference with speculative decoding, Medusa, and lookahead techniques, achieving 1.5-3.6x speedup for real-time applications and constrained deployments.

Reduces chain-of-thought token usage 27-51% in reasoning models (QwQ, DeepSeek-R1) while preserving accuracy via NOWAIT technique.

Provides expert guidance for fine-tuning LLMs using Axolotl with YAML configurations and LoRA/QLoRA techniques.

Manages end-to-end machine learning lifecycle including experiment tracking, model versioning, and production deployment.

Expert guidance for distributed training with DeepSpeed, covering ZeRO optimization, pipeline parallelism, and mixed precision techniques.

Enables 4-bit quantization for LLMs, reducing memory usage by 4x and accelerating inference 3-4x on consumer GPUs with minimal accuracy loss.

Integrates with Benchling R&D platform to automate lab data management, including DNA/protein registries, inventory, and ELN entries via API.

Comprehensive toolkit for building, optimizing, and executing quantum circuits, supporting algorithms, simulations, and hardware execution for scientific and AI applications.

Enables neural network interpretability experiments for large models (70B+) using nnsight with remote execution, eliminating local GPU requirements.

Python toolkit for reading, writing, and processing genomic data formats including SAM/BAM, VCF, and FASTA/FASTQ in next-generation sequencing workflows.

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with tensor, pipeline, and expert parallelism for maximum GPU efficiency.

Provides a unified Python API for accessing and analyzing biological data across 40+ bioinformatics databases including UniProt, KEGG, and PubChem.

Guidance controls LLM outputs using regex and grammars to guarantee valid JSON, XML, and code generation, enforcing structured formats and enabling multi-step workflows.