3. Data, Science & AI

AI agent for de novo antibody design and optimization using protein language models including RFdiffusion.

AI-powered tool for analyzing copy number variations in genomic sequencing data, supporting cancer genomics and constitutional variant detection.

Unified agent leveraging single-cell foundation models for cross-species annotation, perturbation prediction, and gene network inference in bioinformatics.

An LLM-powered agent for spatial transcriptomics research, enabling dynamic code generation, visual data analysis, and scientific literature retrieval.

Manages laboratory test results within healthcare systems for efficient clinical data processing and patient care workflows.

AI-optimized CAR-T cell design for solid tumors using antigen prioritization and exhaustion-resistant engineering to improve therapeutic efficacy.

AI agent interpreting spatial transcriptomics data to propose mechanistic hypotheses and analyze tissue organization.

AI-powered analysis of time-resolved cryo-EM data to study protein dynamics, drug-binding kinetics, and conformational transitions for drug discovery.

Optimizes small molecule structures (SMILES) for enhanced protein binding affinity using an LLM-powered genetic algorithm.

AI-driven analysis of microbiome-cancer interactions for tumor profiling, immunotherapy response prediction, and microbiome-targeted therapeutic opportunities.

AI-powered analysis of long-read genomic sequencing data for structural variants, isoforms, epigenetics, and de novo assembly.

AI-driven tool for ultra-sensitive minimal residual disease (MRD) detection in blood using deep learning, enabling early relapse prediction at sub-0.001% VAF.

AI-powered monitoring for MPN progression, treatment response, and transformation risk in PV, ET, and myelofibrosis.

Analyzes datasets (CSV, JSON, SQL) to extract insights, identify patterns, and generate visualizations using pandas for statistical analysis.

Creates and configures specialized AI subagents within Claude Code for delegated task execution with custom prompts and tool permissions.

Integrates Firebase AI (Gemini) for generative AI features, handling SDK setup, prompt engineering, and AI-powered app capabilities.

Manages persistent AI agent memory and context across sessions, repositories, and dates with conclusion storage and document search workflows.

Configures Looker Studio dashboards using BigQuery data sources, including connection setup, custom SQL, scheduled queries, and performance optimization for analytics.

Defines core principles for AI agent collaboration including decomposition, context management, and abstraction levels to optimize agent interactions.

Designs multi-agent workflows with validation gates for orchestrating AI models (Opus, Sonnet, Gemini, Codex) in autonomous development tasks using skill stacking and iterative improvement.

Automates prompt repetition to enhance LLM accuracy, achieving significant performance gains across 67% of benchmarks on lightweight models like Haiku and Flash.

Configures AI agents with security policies, project documentation standards, and team-shared workflows for Hooks, Skills, and Plugins.

Designs and implements evaluation systems for AI agents, covering grader types, benchmarks, and production integration for coding, conversational, and research agents.

Detects patterns, anomalies, and trends in code and data using regex, AST analysis, and statistical anomaly detection.