Projects | Jared Macshane

MANTIS Neural Compression

Wed, 01 Jan 2025 00:00:00 +0000

MANTIS is a task-informed split-computing system for UAV perception. It compresses visual evidence before transmission by estimating task relevance on the client, modulating the neural analysis transform, entropy-coding the resulting latent, and routing the compact representation to task-specific edge heads.

The system is evaluated across UAV-relevant workloads:

UAVid semantic segmentation for urban scene parsing
WAID wildlife detection for aerial environmental monitoring
Boreal Fire smoke detection for wildfire intelligence

Compared with JPEG, WebP, LADON, learned image codecs, and non-conditioned ablations, MANTIS improves the low- and mid-rate task-utility frontier. The paper reports up to 62.2% bitrate reduction at matched downstream accuracy and up to 9.3% average normalized task-accuracy improvement at matched bitrate.

SHIELD Digital Twin Project

Mon, 01 Jan 2024 00:00:00 +0000

SHIELD is a community-scale digital twin project for disaster resilience. The system connects sensing, simulation, and edge intelligence so resource-constrained perception models can support operational decision-making.

Lead development of a large-scale digital twin platform for disaster resilience
Design progressive inference models for resource-constrained environments
Coordinate cross-functional work across multiple institutions
Build real-time data processing and visualization capabilities

Wildfire Intelligence

Sun, 01 Jan 2023 00:00:00 +0000

Wildfire intelligence work connects perception models with constrained deployment settings where compute, bandwidth, and response time matter.

Developed a generative wildfire-spread model using conditional variational autoencoders
Designed supervised image-compression models that reduced inputs to 4.8 KB while preserving 72.9% wildfire-detection accuracy
Built an edge-computing framework for distributed wildfire detection using early-exit neural networks
Integrated wildfire perception work with broader digital twin and disaster-resilience systems

Geospatial Trail Mapping

Sat, 01 Jan 2022 00:00:00 +0000

This project developed a machine-learning pipeline for large-scale geospatial data processing and automated trail detection from noisy GPS traces.

Designed a trail-network construction algorithm using growing self-organizing maps
Processed noisy public GPS traces into cleaner map structure
Implemented efficient data structures to improve computation performance
Received the IEEE SIEDS Best Paper Award for the resulting publication

Hands-On VR Labs

Fri, 01 Jan 2021 00:00:00 +0000

This work supported scalable virtual and mixed-reality science labs by improving spatial tracking and validating the student experience.

Developed a computer-vision tracking system using ArUco markers for precise spatial tracking
Tested custom active-tracking hardware and 3D-printed form factors
Led user-experience studies to validate system effectiveness and improve interaction design
Integrated tracking tools into interactive virtual laboratory exercises