Research Publications

Yevheniy Chuba, ARESA — YoreAI / University of Pittsburgh

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.

Yevheniy Chuba, ARESA — YoreAI / University of Pittsburgh

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.

Yevheniy Chuba, ARESA — YoreAI / University of Pittsburgh

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.

Yevheniy Chuba, ARESA — YoreAI

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.

Yevheniy Chuba, ARESA — YoreAI

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.

Yevheniy Chuba, ARESA — YoreAI

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.

Yevheniy Chuba, ARESA — YoreAI / University of Pittsburgh

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.

Yevheniy Chuba, ARESA — YoreAI

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.

Yevheniy Chuba, ARESA — YoreAI

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.