Prabha Sundaravadivel

Engineered three classes of NDAA-compliant drones for plant monitoring across greenhouse, row crop, and canopy environments, utilizing ROS2 for autonomy and vision-assisted navigation. Designed AI-enabled stand-alone intelligent monitoring devices for real-time quantification of plants, fish, and weeds using Python and ROS. Integrated multi-sensor data using microcontroller–GPU hybrid systems in drones and ground robots, enabling fully autonomous operation through ROS-based motion control and Python-driven planning. Developed a YOLO-based object tracking system for high-precision monitoring of tiny aquatic species in Petri dishes. For edge sensing applications, combined low-cost sensors and RGB cameras to curate spectral signatures for plant-specific identification and environmental monitoring.

• Secured over $800K in competitive funding through four USDA projects and two institutional initiatives, with additional support from federal agencies, industry partners, and private foundations via successful proposal development and cross-sector collaboration.
• Mentored over 500 students in the Internet of Medical Things (IoMT) through an NSF Cybertraining grant and supported more than 20 student entrepreneurs through a VentureWell Foundation grant, collectively securing $600K in funding.
• Trained a Deepseek model as a Virtual Tutor for Computer Engineering with over 90% accuracy.
• Achieved over 98% accuracy in object tracking and reduced the cost of cameras to $200.

Lead a research group focused on developing intelligent systems for real-time AI inferencing in modular aerial, ground, and underwater robots operating in resource-constrained environments. Design and deploy TinyML and EdgeAI solutions using low-cost, high-efficiency sensing frameworks that integrate FPGA, GPU, microcontrollers, and single-board computers to enable high-throughput environmental monitoring across smart healthcare, agriculture, and transportation domains. Champion academic excellence by mentoring students in integrative systems thinking, applied problem-solving, and scientific communication, while serving on university advisory boards, assessment panels, and academic support committees.

• Founding Director of the Center for Robotics and Intelligent Systems (CeRIS), securing over $1.7 million in competitive funding to advance research in drones, edge AI, and autonomous systems.
• Developed scalable, low-power monitoring platforms supporting compute-intensive applications such as AI-enabled weed detection, aquatic ecosystem tracking, and security surveillance leading to 3 patents.
• Authored over 80 peer-reviewed publications, and received 27 awards in recognition of innovation and impact.
• Graduated 7 MSEE thesis students and delivered 35 invited talks at national and international venues.

Designed and deployed low-power, cost-effective sensing platforms for real-time evapotranspiration modeling in a cross-disciplinary research initiative. Demonstrated innovation and technical leadership by independently solving complex research problems across multiple collaborative teams.

• Contributed to enhanced environmental modeling accuracy while enabling scalable deployment of edge-AI systems in resource-constrained settings.
• Engineered edge-optimized machine learning algorithms for real-time inference, incorporating multi-source data fusion and historical trend alignment.

Led Level 1 (L1) operations team, ensuring timely resolution of service issues and maintaining high system uptime across multiple financial platforms. Mentored junior team members and implemented cross-training initiatives to enhance team agility and operational coverage. Streamlined documentation and internal communication processes, contributing to improved incident response and knowledge transfer within the support team.

• Developed and deployed Perl automation scripts that significantly reduced false alarms by 30% and improved system reliability for mission-critical banking infrastructure.
• Received the “On the Spot Award” for effective response to critical system alerts during live market hours, ensuring uninterrupted operations in a high-stakes financial environment.