Building self-improving, self-evaluating AI systems that advance STEM research autonomously.
As AI capabilities advance with world models and cutting-edge research, humans are becoming the bottleneck of research progress. ARESA is building the scaffolding for scientifically controlled, empirically proven autonomous research—starting with human-in-the-loop collaboration and evolving toward independent discovery. Every proof, architecture, and method we develop is validated and shared openly with the world.
We introduce GeoAI Agentic Flow, a novel architecture that synthesizes coordinate embedding, spatial neural networks, and multi-agent collaboration to achieve state-of-the-art performance in fire hazard risk assessment. Our contributions include the Coordinate Embedding Framework (CEF) with proven bi-Lipschitz properties, a Spatial Neural Network (SNN) with graph-based attention, and a Multi-Agent Collaboration Protocol (MACP) with convergence guarantees. Evaluation on 546,000+ California addresses demonstrates 89.7% accuracy with sub-100ms latency.
We present the theoretical foundations of the Coordinate Embedding Framework (CEF), proving that the mapping from geographic coordinates to semantic vectors satisfies key mathematical properties including bi-Lipschitz distance preservation, feature reconstruction bounds, and stage independence. These theoretical results provide rigorous guarantees for geospatial machine learning applications.
We formalize the Multi-Agent Collaboration Protocol (MACP) for geospatial risk assessment, proving convergence guarantees, Byzantine fault tolerance bounds, and communication efficiency theorems. A 128-agent system organized into specialized pools achieves weighted consensus with provable optimality and tolerates up to 10 Byzantine failures.
An interactive dashboard analyzing 550,000+ fire department dispatch records from 2014-2025. Explore temporal patterns, geographic hotspots, false alarm trends, and priority distributions through dynamic visualizations with real-time filtering.
AresaDB is a unified multi-model database combining SQL, vector search, and RAG capabilities for healthcare machine learning research. Featuring sub-millisecond query latency, semantic similarity search with cosine/euclidean metrics, and retrieval-augmented generation pipelines. Pre-loaded with 287K+ healthcare records including drug reviews, medical transcriptions, and PubMed abstracts.
A unified command-line interface and web UI for querying 8+ database types with a single tool. Supports PostgreSQL, MySQL, SQLite, ClickHouse, BigQuery, DuckDB, Snowflake, and Databricks. Features schema exploration, query history, and connection management with hot-reloading configuration.
AresaDB is a high-performance, multi-model database engine written in Rust that unifies key-value, graph, and relational data paradigms under a single property graph foundation. Achieves sub-millisecond point lookups while supporting complex graph traversals, relational queries, and vector search for RAG applications. Benchmarks demonstrate 22,000+ inserts/second and competitive query latencies against SQLite, DuckDB, and Pandas.
A transformer-based architecture for autonomous clinical documentation, achieving 94.2% F1-score on medical entity extraction, 89.7% accuracy on clinical relationship mapping, and 87.3% clinician acceptance rate for diagnostic suggestions. Processes 4,999 clinical notes with sub-200ms latency for real-time decision support.
A graph neural network architecture for healthcare knowledge graph reasoning, achieving 87.4% accuracy on drug interaction prediction and 84.2% on adverse effect forecasting. Constructs a 847K-node, 2.3M-edge knowledge graph from drug reviews, PubMed, and FDA databases, identifying 23 novel drug repurposing candidates.
by Yevheniy Chuba • 2024
A comprehensive reference guiding you through core mathematical domains (calculus, linear algebra, probability, statistics) with applications from classical physics to modern machine learning. Integrates Python programming to visualize concepts and demonstrate how mathematics powers real-world AI systems.
by Yevheniy Chuba • 2024
50 comprehensive projects spanning healthcare, robotics, environmental science, finance, and cutting-edge AI applications. Demonstrates how to apply mathematical foundations to solve complex, real-world problems using state-of-the-art machine learning techniques.