3. Data, Science & AI

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.

Enables querying the CZ CELLxGENE Census for single-cell expression data with biological filtering and integration to scanpy/PyTorch for population-scale analysis.

Accesses BRENDA enzyme database via SOAP API to retrieve kinetic parameters, reaction equations, and organism data for biochemical research and metabolic pathway analysis.

High-performance vector similarity search engine for RAG and semantic search, enabling fast nearest neighbor and hybrid search in production systems.

Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching for production API deployment.

Tracks ML experiments with automatic logging, real-time visualization, hyperparameter sweeps, and model registry management in a collaborative MLOps platform.

Platform for creating and deploying persistent autonomous AI agents that execute multi-step automation through visual workflows and continuous agent systems.

Programmatically accesses NIH Metabolomics Workbench database to query metabolite data, MS/NMR spectra, and study metadata for biomarker discovery and metabolomics research.