3. Data, Science & AI

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.

Builds scalable data pipelines, ETL/ELT systems, and data infrastructure using Spark, Airflow, and dbt for data modeling and orchestration.

Extends transformer model context windows using RoPE, YaRN, ALiBi, and position interpolation for processing long documents (32k-128k+ tokens).

Provides a library for training and experimenting with reinforcement learning agents using algorithms like PPO and SAC in Gym environments.

Framework for building, simulating, and executing quantum circuits, supporting quantum algorithms and hardware integration.

Provides production-grade PDF processing for forms, tables, OCR, and batch operations with robust validation and error handling.

Comprehensive toolkit for building AI data pipelines including ETL workflows, vector embeddings, and knowledge graphs via CocoIndex library.

Provides genomic data analysis and visualization for NGS workflows, including BAM to bigWig conversion, QC metrics, and heatmaps for ChIP-seq, RNA-seq, and ATAC-seq.