3. Data, Science & AI

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.

Enables Bayesian inference with PyMC, including hierarchical models, MCMC (NUTS), and model comparison techniques for probabilistic programming.

Comprehensive citation management tool for academic research, enabling search, metadata extraction, citation validation, and BibTeX generation from sources like Google Scholar and PubMed.

Visualizes ML training metrics, model graphs, and performance for debugging and experiment comparison using Google's TensorBoard.

Unifies distributed training frameworks for PyTorch with minimal code changes, automatic device placement, and mixed precision support.

Simplifies RDKit for drug discovery tasks including SMILES parsing, molecular descriptors, and 3D conformer generation.

Compresses large language models via knowledge distillation, retaining performance while reducing inference costs. Supports soft targets and logit distillation techniques.

Analyzes Neuropixels neural recordings including preprocessing, spike sorting, and AI-assisted quality assessment for extracellular electrophysiology data.

Integrates with protocols.io API to manage scientific protocols, including creation, updates, collaboration, and documentation for lab and research workflows.

Toolkit for processing whole slide pathology images, including tile extraction, tissue segmentation, and dataset preparation for deep learning in computational pathology.

Manages biological datasets with FAIR principles, ensuring traceability, reproducibility, and integration with scientific workflows and MLOps platforms.

Enables probabilistic modeling and analysis of single-cell omics data, including scRNA-seq, scATAC-seq, and spatial transcriptomics, for tasks like batch correction and cell type annotation.

Python interface for mass spectrometry data analysis, enabling proteomics and metabolomics workflows with file handling and quantitative processing.

High-level PyTorch framework simplifying training loops with distributed computing, callbacks, and minimal boilerplate for scalable machine learning development.

Comprehensive toolkit for processing and analyzing physiological signals including EEG, ECG, and EDA for scientific research and clinical applications.

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware using GGUF quantization for efficiency.

Provides rapid bioinformatics queries via CLI and Python, accessing multiple biological databases for sequence analysis and research.

Expert guidance for PyTorch FSDP training with parameter sharding, mixed precision, and CPU offloading in distributed deep learning.