3. Data, Science & AI

Provides scikit-learn compatible APIs for time series machine learning tasks including forecasting, anomaly detection, and clustering on temporal data.

Serverless GPU platform for deploying ML models as APIs and running scalable batch jobs without infrastructure management.

Primary Python toolkit for molecular biology data processing, including sequence manipulation, file parsing, and BLAST workflows.

Trains Mixture of Experts (MoE) models efficiently using DeepSpeed or HuggingFace, reducing compute costs while enabling sparse architectures for large-scale AI systems.

High-performance Rust toolkit with Python bindings for genomic interval analysis, BED file processing, and ML tokenization in computational genomics.

Framework for building AI systems with declarative programming, automatic prompt optimization, and modular RAG systems using DSPy.

Provides a vision-language pre-training framework for image captioning, visual question answering, and multimodal chat with zero-shot capabilities.

GPU-accelerated data curation tool for preparing high-quality training datasets for LLMs, featuring deduplication, quality filtering, and content safety checks.

Framework for Python-based computational fluid dynamics simulations, supporting Navier-Stokes equations, turbulence analysis, and HPC with FFT methods.

Fine-tunes LLMs using RLHF techniques (SFT, DPO, PPO) with HuggingFace Transformers for preference alignment and reward optimization.

Guides training and analysis of Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features for model analysis.

Accelerates transformer training/inference with 2-4x speedup and 10-20x memory reduction using Flash Attention for long sequences.

Provides tools for building and training Graph Neural Networks (GNNs) for node classification, link prediction, and molecular property prediction using PyTorch Geometric.

Python library for quantum machine learning, quantum circuit design, and hybrid quantum-classical model training with automatic differentiation and PyTorch integration.

Predicts protein-ligand binding poses and confidence scores using diffusion models, supporting PDB and SMILES inputs for structure-based drug design.

Provides zero-shot image segmentation using points, boxes, or masks as prompts, or automatically generates all object masks in an image.

Automates exploratory data analysis for scientific datasets, detecting file types and generating structured reports with quality metrics and recommendations.

Provides access to and analysis of comprehensive drug data from DrugBank, including properties, interactions, targets, and pharmacology for research and discovery.

Enables machine learning analysis of genomic regions using BED files, including region embeddings and scATAC-seq processing.

Accesses ZINC database for drug discovery, enabling compound searches by ID, SMILES, similarity, and 3D structure analysis for virtual screening.

Enables constraint-based metabolic modeling with FBA, FVA, gene knockouts, and SBML support for systems biology and metabolic engineering analysis.

Provides advanced prompt engineering for LLM optimization, RAG, agent design, and structured outputs to enhance AI product performance.

Efficiently search bioRxiv preprints by keywords, authors, or date ranges, retrieving metadata and PDFs for scientific literature reviews.

Provides direct REST API access for protein data retrieval from UniProt, including searches, FASTA sequences, and ID mapping for bioinformatics workflows.