Hybrid-Neuro-Symbolic AI combines neural network learning with symbolic reasoning systems like knowledge graphs, rule engines, or logic programming. Neural components handle perception and pattern recognition, while symbolic components provide explainability and constraint enforcement.
Machine learning systems for optimizing power plant operations including combustion efficiency, heat rate optimization, steam turbine performance, and real-time monitoring.
Combines geoscience data (seismic, MT, well logs, remote sensing) with AI to identify and rank prospective geothermal resources.
AI-powered supply chain emissions tracking and Scope 3 carbon accounting
Black-box AI recommendations face low operator trust in safety-critical plants, and hidden sensor calibration issues can corrupt optimization decisions.
Evaluates solar and storage scenarios to quantify how technology choices and policy-sensitive inputs impact LCOE, generation mix, emissions, and storage economics for energy planning and procurement.
AI-assisted oncology trial matching that extracts biomarker and TNM staging data from unstructured charts and performs transparent criterion-level inclusion and exclusion eligibility assessment.
AI-powered clinical trial dose optimization for Phase II studies, extracting dosage evidence from trial text and identifying dose options that best balance safety and efficacy.
Analyzes errors in finance AI systems for scenario analysis, focusing on financial reasoning, calculations, and chart-based visual context to identify failure patterns and improve model reliability.
AI-powered code review and code understanding platform that orchestrates review, testing, security, and delivery workflows while explaining code behavior for faster onboarding and requirements analysis.
This application area focuses on optimizing production schedules in complex manufacturing environments while explicitly accounting for human workers, equipment health, and sustainability constraints. Instead of relying on static, rule‑based planning, these systems generate and continuously adjust detailed schedules across plants, lines, and shifts to balance throughput, due dates, energy use, and worker fatigue or well‑being. It matters because modern factories operate under tight delivery windows, labor shortages, strict safety requirements, and decarbonization targets that traditional scheduling tools cannot jointly optimize. By integrating real-time data on machine status, maintenance needs, worker conditions, and energy or emissions, these systems improve on-time delivery, reduce overtime and breakdowns, and support safer, more sustainable operations aligned with Industry 5.0 principles.
Natural-language assistant that helps residents explore FY2025 parking violation data by answering questions, filtering records, summarizing trends, and explaining results without requiring users to work directly with raw open datasets.