Applied AI for infrastructure, sensing, and scientific prediction
We build computer-vision, deep-learning, and forecasting systems that turn complex, real-world data into reliable decisions from infrastructure monitoring to biomedical and environmental analysis.
About
What we do ...
ATLAS AI is a United States based research and development company that designs and deploys artificial intelligence systems for demanding, data-rich problems. Our work spans computer vision, time-series forecasting, signal processing, and multimodal data fusion, built not as demonstrations, but as deployable pipelines validated through rigorous testing.
We focus on problems where accuracy and reliability matter: assessing infrastructure, interpreting sensor and imaging data, and predicting the behavior of complex physical and biological systems.
What we work on ...
Infrastructure monitoring and predictive maintenance
AI systems that detect damage, assess severity, and recommend maintenance actions for roadways, power transmission lines, and utility networks. Combining computer vision, geospatial analytics, and cost-aware decision support to help agencies prioritize work and reduce inspection costs.
Computer vision and remote sensing
Detection, classification, and condition assessment from imagery. Including aerial and drone-based inspection using RGB, thermal, and geospatial data.
Forecasting and scientific modeling
Physics-informed and data-driven models for spatiotemporal prediction, including environmental and oceanographic systems, field reconstruction from sparse data, and rigorous backtesting.
Biomedical and signal-processing AI
Deep-learning models for medical imaging, disease detection, biometrics, and bioacoustics. Applying transfer learning and careful validation to perform well with limited labeled data.
How we work ...
Our approach is grounded in research discipline and engineering practice. We build scalable AI pipelines for high dimensional, noisy, heterogeneous data; we validate models rigorously through systematic evaluation and backtesting; and we deliver reproducible, production oriented systems with proper documentation and post deployment monitoring. The goal is always the same: AI that holds up outside the lab.
Who we serve ...
We work with organizations that need trustworthy AI for high-stakes decisions: transportation and public-works agencies, utilities and infrastructure operators, research programs, and engineering teams that need applied AI translated into working tools.
Research & Credentials ...
Although, ATLAS AI has been established in 2025, but it is led by PhD-level researchers in electrical engineering, computer engineering, and computer science, who have more than a decade of experience translating advanced AI into deployable systems across computer vision, forecasting, biometrics, and bioacoustics. Our experience includes work supported by national research programs and federal agencies, spanning healthcare AI, environmental modeling, and sensing.
Contact
You can find us in ..
• Boca Raton, Florida, USA ... main office • Torrento, Canada
• London, UK • Sydney, Australia
• Baghdad, Iraq • Riyadh, Saudi Arabia
• Dubai, UAE • Beirut, Lebanon
• Istanbul, Turkey • Islamabad, Pakistan
• Belgrade, Serbia
We welcome the opportunity to work with you. To get started, please complete the contact form on this page or write to us directly at [email protected]. Let us know about your business and objectives, and we will provide a customized quote for your project.
Projects & Research
Active Projects
Automated Pothole Detection & Road Treatment Optimization
Computer Vision · Geospatial Analytics · Pavement Lifecycle Intelligence
An AI-driven roadway damage assessment framework that automates the full pipeline from defect detection to treatment recommendation. Computer vision models analyze road inspection imagery to detect and classify pavement distress; potholes, cracking, rutting, and delamination, and estimate severity from morphological, traffic load, and environmental features. A decision support engine then recommends the optimal treatment strategy for each segment; crack sealing, patching, resurfacing, or reconstruction, by balancing pavement condition indices, structural risk, and cost efficiency. The output is a prioritized, geospatially mapped maintenance plan ready for municipal and transportation agency use.
Power Line Drone Inspection System
Autonomous UAV · Thermal & RGB Imaging · Real-Time Fault Detection
An autonomous drone-based inspection system for high-voltage power transmission lines that replaces costly and hazardous manual surveys with intelligent UAV flights. Deep learning models fuse thermal imaging, RGB video, and onboard geospatial telemetry to detect and classify Electrical instrument and components, damages, faults, vegetation encroachment, corrosion, and structural degradation in real time. Detected faults are geolocated, severity-scored, and routed through an automated reporting pipeline that delivers prioritized maintenance actions directly to utility asset management teams.
AI Infrastructure Monitoring & Predictive Maintenance
Computer Vision · Time-Series Forecasting · Operational Analytics
An end-to-end platform that continuously monitors distributed utility assets, detects early degradation using computer vision and time-series forecasting, and delivers prescriptive maintenance recommendations before failures occur. The system integrates risk scoring, operational constraints, and cost efficiency modeling to prioritize technician dispatch, maintenance scheduling, and asset rehabilitation across large scale networks. Production grade ML and generative AI workflows handle automated reporting, post deployment monitoring, and maintenance copilot functions; reducing manual workload while improving decision speed and consistency.
Research Portfolio
Prior to founding ATLAS AI, our members led and contributed to the following federally funded and peer reviewed research programs at Florida Atlantic University.
Ocean Current Modeling & Prediction
Dynamic Modeling · Spatiotemporal Forecasting
Funded by the National Academies of Sciences, Gulf Research Program
Built automated deep learning systems for 3D ocean current prediction and bias correction of numerical oceanographic models. Developed modified architecture that retains the temporal and spatial evolution of model observation differences to produce correction weights extending up to one year beyond the observation period; achieving more than 85% relative accuracy improvement over baseline numerical models. A second effort applied Physics Informed Neural Networks (PINNs) to reconstruct full surface velocity fields from sparse drifter data, achieving a correlation coefficient above 0.91.Funded by the National Academies of Sciences — Gulf Research Program
Publications:
Bioacoustics Detection & Localization
Signal Processing · Acoustic Array Processing · Marine Species Monitoring
Funded by the Defense Advanced Research Projects Agency (DARPA)
Developed an end-to-end automated detection and localization system for Goliath grouper using low frequency pulse sounds recorded by a six element underwater hydrophone array. The two stage pipeline uses an adaptive matched filter to detect and time sound pulses, followed by a time difference of arrival (TDOA) localization algorithm that places the source within approximately 2 meters over a 50 meter array span; enabling precise fine scale behavioral tracking of marine species from passive acoustic recordings alone.
Publications:
Medical & Health AI
Transfer Learning · Medical Imaging · Disease Detection & Classification
A broad program of applied deep learning research across high impact healthcare domains. Work includes: Alzheimer's disease detection from FDG-PET imaging using transfer learning; diabetic retinopathy severity classification from retinal fundus images; COVID-19 classification using a two-stage deep learning approach; skin cancer detection; and emotion recognition from facial imagery. A consistent focus across all work is achieving strong performance under limited labeled data through transfer learning, cross-validation, and systematic model evaluation.
Publications:
Human Activity Recognition & Multimodal Fusion
Wearable Sensors · Multimodal Fusion
Designed multimodal deep learning architectures that jointly process wearable inertial sensor data and visual imagery for real-time human activity and health monitoring. Work spans feature engineering, cross-dataset generalization, and systematic fusion evaluation across multiple benchmark datasets.
Publications:
American Sign Language Recognition
Computer Vision · Vision Mamba · Accessibility AI · Real-Time Classification
Developed a series of increasingly capable systems for automated ASL recognition, progressing from CNN-based single-image classification to Vision Mamba architectures and multi-focus image fusion for robust real-world performance. The most recent work in this line achieves high classification accuracy across the full ASL alphabet and advances the state of the art in efficient sequence modeling for vision tasks. A subsequent effort developed a real-time ASL translation system using a wearable instrumented glove and mobile application, combining deep learning gesture recognition with keypoint tracking for accessibility applications.
Publications:
Biometrics — Finger Knuckle Print Authentication
Biometric Identification · Low-Complexity Classification
Developed classification systems for personal authentication based on finger knuckle print (FKP) biometrics, progressing from quantum-computing-based models to pretrained vision transformers and the Vision Mamba architecture. The Vision Mamba approach achieves high classification accuracy at significantly lower computational complexity than transformer baselines, making it well-suited for deployment on resource-constrained biometric devices.
Publications:
Marine Species Distribution — Protect Florida Whales
Spatial Analysis · Species Distribution Modeling · Ecological AI
Studied marine mammal population dynamics along Florida's Atlantic coast, developing analysis pipelines for spatial distribution assessment of whale species. Work contributed to conservation planning and behavioral ecology research by providing data-driven spatial models of species presence and movement relative to environmental conditions.