End-to-end research facility design and operationalization delivered for a defense client — covering site architecture, RF instrumentation deployment, network and computing infrastructure buildout, and regulatory certification. Specialized test equipment specified, procured, and integrated across the facility to support live signal collection, spectrum analysis, and experimental RF operations. Workforce development program designed and delivered to bring a multidisciplinary team — including engineers, intelligence analysts, and military operators — to operational proficiency on advanced RF systems and experimental methodologies. Ongoing technical training conducted for NSA analysts, Special Forces operators, and USAF/Marine Corps personnel on system operation and experimental protocols.

ML-driven signal identification, AoA/TDoA localization, and automated alert systems for contested spectrum environments. Deployed with sensor networks for persistent monitoring and geolocation across multiple operational zones.

EMI/EMC compliance completed for a military program aircraft per DO-160D, MIL-HDBK-516C, and MIL-STD-461 — enabling international flight clearance. EMIT cosite analysis automated for HALE vehicle antenna systems. EM emission and susceptibility test procedures drafted across multiple LRUs.

Full RF environment survey conducted across a high-value estate perimeter and surrounding airspace to establish a spectrum baseline. RF-based drone detection system designed with direction finding, signal classification, and a real-time alerting layer delivering SMS, push notification, and email alerts to estate security staff with bearing, signal classification, and confidence score. Delivered sensor placement plan, coverage map, and owner-operated monitoring specification. System accounts for both fixed-wing and multi-rotor drone profiles at varying altitudes and approach vectors.

Spectrum survey conducted across a multi-building commercial campus to identify unauthorized device activity, rogue access points, and interference sources. Layered RF sensing architecture designed across perimeter, parking, and rooftop exposure zones with automated anomaly detection and tiered alert routing — immediate push and SMS notifications for active intrusion events, daily digest emails for low-priority anomalies, and a security dashboard with event history and classification logs. Delivered documented findings, vendor-agnostic system specification, and phased deployment roadmap for the facilities and security teams.

Replaced disconnected spreadsheets, email chains, and manual approvals with an integrated AI-driven workflow covering job estimating, subcontractor scheduling, purchase orders, and client invoicing. Document processing automation handled proposal generation and change order tracking. Reduced administrative overhead significantly and eliminated billing delays that had been averaging two weeks post-job completion.

ML-driven demand forecasting system deployed to replace manual planning cycles — integrated with existing ERP infrastructure, reducing overstock and stockout events significantly. Automated customer profile extraction pipeline built from transaction history and CRM data, enabling targeted replenishment and outreach. Intelligent inventory visibility dashboard developed with low-stock alerting, supplier lead-time modeling, and automated reorder workflows.

Customer throughput and billing accuracy constrained by manual intake, phone-based scheduling, and disconnected verification across twelve service locations. End-to-end automation delivered covering customer intake, appointment scheduling, and verification workflows — reducing front-desk overhead and eliminating manual handoffs. AI-assisted documentation pipeline deployed to convert service notes into structured billing codes, improving invoicing accuracy and reducing processing time.

Marketing spend undirected and customer retention unmeasured across 12 locations — no behavioral segmentation, no visibility into high-value customer patterns, no mechanism for targeted outreach. ML-driven customer segmentation pipeline built from POS and loyalty data, enabling personalized promotions, high-value customer identification, and churn prediction. Automated marketing sequences deployed based on purchase behavior and segment membership, with weekly segment movement reporting delivered to operations and marketing teams.

Business development constrained by manual prospecting — time-intensive contact research, inconsistent outreach, and no systematic qualification or follow-up. End-to-end automated pipeline delivered covering contact discovery, data enrichment, qualification scoring, and personalized outreach sequencing across multiple channels. AI-generated messaging tailored to industry, role, and engagement signals, with full CRM synchronization and reply tracking. Manual BD overhead reduced substantially while qualified meeting conversion improved.

PSO-based coordination algorithm developed and implemented for autonomous multi-drone fleet navigation — enabling decentralized swarm behavior for area coverage, target search, and dynamic obstacle avoidance without centralized control. Each agent updated its trajectory using local best and global best position heuristics across configurable swarm topologies. System validated through simulation and live field tests with a distributed drone fleet, demonstrating convergent coverage patterns and adaptive formation behavior under dynamic conditions.

Full grant proposal development delivered across multiple federal funding mechanisms — including technical narratives, budget justifications, statements of work, and evaluation criteria alignment. Concurrent technical review conducted across an active R&D portfolio: assessing feasibility of proposed approaches, identifying gaps in technical methodology, and providing written feedback to internal research teams. Due diligence support provided on two partnership agreements, including IP assessment and scope-of-work analysis.

Technical assessment performed on a proposed government contract vehicle — reviewing statement of work, deliverable definitions, feasibility of technical claims, and risk surface across hardware and software components. Independent evaluation of the company's core IP provided for investor and partner use. Contract language reviewed for technical accuracy and scope alignment. Written findings delivered to executive and legal teams with recommendations on negotiation posture and technical risk mitigation.

Field operations constrained by manual scheduling, paper-based job tracking, and disconnected billing — leaving dispatchers without real-time visibility and delaying invoicing by weeks. Custom full-stack platform delivered consolidating dispatching, job scheduling, technician assignment, parts tracking, and client invoicing into a single web application with mobile access for field staff. Real-time job status visibility restored dispatcher control and eliminated the billing backlog.

Custom embedded sensor nodes designed and deployed across a mid-size logistics fleet to capture location, temperature, door state, and vibration data in real time. Firmware developed for low-power operation with cellular and WiFi connectivity; cloud ingestion pipeline built for continuous telemetry. Web dashboard delivered for operations staff with configurable alerting on threshold violations — SMS and email notifications for temperature exceedances, geofence departures, and door-open events, plus an in-app alert feed with timestamp and vehicle ID for dispatcher review.

Load assignment managed by phone and spreadsheet — no real-time vehicle visibility, no systematic load matching, and no driver-facing tool for status updates or document capture. End-to-end web application delivered for load assignment, GPS-based vehicle tracking, and ML-driven dispatch optimization. Driver mobile interface built for load acceptance, status updates, and document capture. ETA prediction and at-risk delivery flagging delivered to dispatchers, improving on-time performance and reducing coordination overhead.

Full-stack savings management application built for a community credit union to help members set savings goals, track progress, and receive personalized guidance based on spending patterns. Rules-based automation deployed for recurring transfers and round-up contributions. Dashboard delivered with visualized goal timelines, projected completion dates, and nudge notifications for members falling behind their targets. Integrated with the credit union's existing core banking system via API to reflect real-time account balances without storing sensitive financial data in the application layer.

Full-stack web document scanning application developed and deployed — accessible directly from a mobile browser with no app installation required. Custom scanning algorithm built to detect document edges, apply perspective correction, and enhance contrast in real time using the device camera. ML-based OCR pipeline integrated for text extraction from scanned images, with structured output for PDF generation and searchable document export. Entire processing pipeline runs client-side to avoid transmitting sensitive document content to a server, enabling deployment for legal, financial, and medical document use cases.

Mechanical and electrical hardening performed on wideband sensing system enclosures for long-duration outdoor deployment — addressing RF transparency, thermal management, moisture ingress, and connector weatherization. Enclosure designs evaluated and modified for environmental exposure across temperature cycling, humidity, and wind load conditions. RF performance validated post-hardening to confirm minimal impact on antenna coupling and signal path integrity.

End-to-end architecture design, development, and field deployment of a distributed environmental sensing network operating across geographically separated remote sites. Sensor node hardware selected and configured for low-power operation with local data buffering; communication infrastructure designed for intermittent connectivity environments. Backend ingestion pipeline developed to aggregate, validate, and store multi-node telemetry streams. Alerting system delivered with configurable per-node thresholds, automated SMS and email notifications on exceedance events, and a remote monitoring dashboard with per-site status, recent readings, and alert history for field supervisors.

End-to-end SDR calibration methodology developed covering gain flatness, IQ imbalance correction, DC offset compensation, and noise floor characterization across multiple hardware platforms. Calibration procedures documented as repeatable SOPs for lab use. Hands-on training curriculum designed and delivered to a team of engineers and analysts — covering SDR fundamentals, real-time signal capture, spectrum analysis, and integration with signal processing pipelines in both GNU Radio and Python environments.

Passive radar system designed and prototyped using ambient FM and digital TV broadcast signals as illuminators — eliminating the need for a dedicated transmitter. Signal processing chain developed for direct-path interference cancellation, Doppler-range processing, and target track extraction from bistatic geometries. System validated against airborne targets in controlled test environments. Approach documented for integration into larger multi-sensor architectures.

Machine learning pipeline developed to automatically classify and characterize signal types from large-volume spectrum survey datasets — treating spectral observations as structured sequences for pattern-based identification. Feature extraction developed from time-frequency representations; classifier trained to distinguish modulation types, protocol signatures, and anomalous emissions across a wide frequency range. Output integrated into a monitoring dashboard with confidence scores and flagging for human analyst review.

FMCW W-band and SFCW K-band radar systems designed, simulated, fabricated, and characterized in LTCC. Novel power amplifier assembly method patented, enabling measurable radar range extension. K-Band antenna designed in 9K7 LTCC with improved fabrication procedures. Results published across peer-reviewed conferences. Multi-year consortium program delivered with biannual on-site reporting to government leadership.

Full architectural design and development of Tiffany — a central telemetry and analytics platform serving as the health and performance hub for a distributed ecosystem of 20+ interconnected applications and simulation services. Platform collects real-time metrics from all services via a dedicated protobuf-based TCP ingestion layer, aggregates health state, error rates, and simulation activity, and exposes a unified analytics dashboard for operators. Designed as the first service initialized in the ecosystem startup sequence — all downstream services register with Tiffany before becoming operational. Integrates with Google Analytics, Firebase, and Cloudflare to consolidate web, infrastructure, and application-layer telemetry into a single reporting surface.

LTCC-based beamforming antenna structures designed and fabricated for mmWave radar applications with improved radiation efficiency and pattern control. LTCC rework procedures developed to improve yield and repair existing assemblies without full re-fabrication — reducing cost and turnaround on hardware iterations. Wire and ribbon bonding, die assembly, and flying probe characterization performed across multiple device generations.

Full architectural design and development of Boltzmann — a web-accessible multiphysics simulation platform built for high-frequency device characterization, EM field simulation, and inverse design workflows. Simulation engine architected around a ZMQ-based real-time messaging layer with separate publish and request-reply channels for live result streaming to connected frontends. Computational backend designed to support physics-based solvers with ML surrogate acceleration, enabling rapid parameter sweeps across device geometry and material configurations. Deployed as a containerized service stack with dedicated engine dashboard, intelligence dashboard, and orchestration layer.

Double Gunn Diode devices theorized, designed, simulated, and patented — achieving expanded tunable frequency range. Computational models developed integrating numerical EM solvers with deep learning surrogate models (CNNs, GNNs) for accelerated device characterization. Reinforcement learning optimization applied to geometry and doping parameter exploration under RF performance constraints.