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Dara McNally
Junior Computer Vision Researcher specializing in deep learning and object detection
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About
Robotics engineer who built and scaled a distributed perception stack on a Unitree Go1 quadruped, coordinating 5 Jetson Nanos and a Raspberry Pi to capture, aggregate, and stream multi-camera video in real time via UDP/GStreamer and custom ROS nodes. Also implemented a YOLOv9-based detection pipeline enhanced with Grad-CAM-driven selective image enhancement (e.g., MIRNet/UFormer) to improve real-time detections and robot reactions to visual stimuli.
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Key Strengths
- Built distributed multi-camera sensing pipeline across 5 Jetson Nanos plus a central Raspberry Pi
- Implemented real-time video capture, streaming, and recording using UDP + GStreamer
- Developed ROS nodes to aggregate and stream all onboard camera feeds (beyond default single-stream capability)
- Designed real-time robot visual-reactivity behavior (turn-away response to detected stimulus)
- Integrated YOLOv9 with a multi-stage enhancement pipeline using Grad-CAM saliency masks and models like MIRNet/UFormer
- Performance-focused real-time optimization (sensor gating, frame-rate monitoring to manage latency)
- Self-directed ramp-up on a new robotics platform and transferred knowledge to teammates
- Emphasis on production reliability via documentation, rigorous testing, and CI/CD without breaking stable releases
Reference Highlights
- high ability in complex robotics/real-time systems
- strong interest and engagement with robotics work
- thorough testing practices
- strong debugging skills (catches uncaught errors)
- code runs reliably in field testing without errors
- extremely hard worker
- stays late to meet deliverables
- meets deadlines/deliverables on time
- asks for help appropriately
- delegates when needed
- handles ambiguity well in changing research environments
- shows initiative by proposing and designing new research projects
- effective cross-disciplinary collaborator (ML and robotics)
- Very strong in real-time systems
- Strong on the software side of complex robotics
- Produces clear, readable Python code
- Writes maintainable code (clear variable naming and comments)
- Code is easy for others to understand later
- Takes initiative through independent research
- Contributes to increasing paper publications
- Collaborates smoothly with other groups (including hardware-focused teams)
- Positive feedback on team progress speed
- Very capable at implementing real-time computer-vision systems
- Strong at refactoring and improving codebases
- Strong organizational skills in team development
- Proficient in Python (syntax, built-ins, best practices)
- Pragmatic about leveraging existing packages for speed
- Strong ownership in ambiguous environments (PhD-level independence)
- Strong work ethic and initiative
- Keeps up with fast-moving computer-vision research
- Able to build hardware-compatible, efficient models
- Capable of interfacing with various hardware/edge devices
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