Boris Resnick is a systems architect and technology leader with over two decades of experience designing complex, scalable software for mission-critical aviation and AI ecosystems. His expertise spans ground-based aviation navigation, 5G-enabled Unmanned Traffic Management (UTM), and AI-driven data fusion, with a focus on shaping regulatory frameworks and technical standards for next-generation airspace and telecommunications.
Leads the technical vision for cellular-based unmanned traffic services, building edge-based deconfliction software and robust simulation environments that rely on 5G infrastructure to enable dense, safe drone operations in urban environments.
A significant portion of Boris Resnick's work involves actively shaping the regulatory frameworks and technical standards for the next generation of airspace and telecommunications.
Technical partner for an innovative suite of plugins for OBS Studio. EMBYAD operates as a custom video encoder utilizing real-time AI to natively embed non-intrusive, ad-blocker-proof advertisements such as virtual in-game billboards or backgrounds directly into live video streams without interrupting the broadcast.
Played a key role in a multi-year project to modernize Changi Airport's runway throughput capabilities. Oversaw the deployment of specialized Doppler LIDAR infrastructure to perform "ground-truth" validation of aircraft wake vortex behavior in a tropical environment. This allowed the airport to shift from static safety categories to a data-driven, seven-group separation matrix, safely reducing separation gaps to 60-180 seconds and reclaiming lost runway capacity.
Contributed to the architecture and development of a software-defined precision landing infrastructure, designed as the primary alternative to the traditional Instrument Landing System (ILS). The system utilizes real-time differential GNSS corrections. Focused heavily on the complex Executive Monitoring (EXM) logic for real-time fault isolation, ensuring the software met the most stringent aerospace certification standards (RTCA DO-178C DAL A) for Category I, II, and III precision approaches.
Designed a high-performance, real-time intelligence module for Advanced Air Mobility that dynamically ingests, correlates, and filters fragmented air traffic data from multiple providers into a single, unified operational picture. Includes an AI-driven behavior analysis layer to instantly distinguish between legitimate operations and potentially unauthorized rogue activities.
Developed an AWS-hosted verification facade designed to analyze flight log statistics and verify Command and Control (C2) signal recordings against strict operational communication standards for unmanned aerial systems. The tool provides geospatial signal mapping to visualize flight paths overlaid with signal quality metrics, ensuring regulatory compliance.
Designed an enterprise-grade automated data pipeline that drastically accelerates the training of object detection models. Leveraged advanced Vision Language Models (VLMs) to automatically annotate images and prepare datasets, removing the traditional bottleneck of human labeling and turning AI model development into a highly scalable software factory.
A Git-native Requirements Management System (RMS) that treats product specifications as code. Integrates directly with CI/CD pipelines and features an AI-powered Model Context Protocol (MCP) server, allowing Large Language Models to instantly query, read, and understand exact project requirements.
Co-authored peer-reviewed research on utilizing Convolutional Neural Networks (CNNs) to detect aircraft wake vortices based on scanning Doppler LIDAR measurements. Specialist in Computer Science from the Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University (MSU).
For inquiries, partnerships, or collaborations, contact Boris Resnick.