esm

Simplifies materials science computational workflows by providing advanced tools for crystal structure generation, analysis, and simulation using Python.

Retrieves, queries, and analyzes European Nucleotide Archive (ENA) genomic data with advanced filtering and research-grade processing capabilities

Generates precise scientific timelines and chronological data models for historical, archaeological, and research-based temporal analysis and visualization.

Performs probabilistic programming and Bayesian inference using PyMC, enabling complex statistical modeling with MCMC sampling and posterior analysis.

Explains machine learning model predictions using SHAP values, helping data scientists interpret feature importance and model behavior

Streamlines data flow management by providing intuitive input/output processing, transformation, and routing for complex scientific and engineering workflows.

Parallelize and scale data processing and machine learning workflows across distributed computing environments using Dask's flexible parallel computing framework.

Implements reinforcement learning algorithms and training pipelines using Stable Baselines3 for OpenAI Gym environments and custom RL projects.

Generates interactive, publication-quality scientific visualizations and dashboards using Python's Plotly library for data exploration and presentation

Streamlines genetic design and research workflows by interfacing with Benchling's API for sequence management, annotation, and collaborative molecular biology tasks