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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
portfolio
Bioacoustic Pest Detection System
Published:
A contact-microphone acquisition and classification pipeline achieving 96% precision on faint in-plant pest signals. Read more
Automated Experimental Platform
Published:
A modular device-control, video-acquisition, telemetry, and metadata platform for long-duration operation. Read more
Computer-Vision Data Pipeline
Published:
A repeatable image-acquisition, quality-control, and data-ingestion pipeline that produced 5,000+ curated images. Read more
Scalable Visual Representation Learning
Published:
DINOv2, diffusion, and VQ-VAE benchmarking on 1.5 million images with 18% higher AUROC and 54% lower training cost. Read more
Robot-Assisted Sensor Calibration
Published:
An automated ROS, Gazebo, and UR5 extrinsic-calibration pipeline validated through sub-2 mm 3D reconstruction residuals. Read more
publications
Geometric Calibration of Single-Pixel Distance Sensors
Published in IEEE Robotics and Automation Letters, 2022
A geometric calibration method for single-pixel distance sensors, validated through precise 3D reconstruction. Read more
Recommended citation: Carter Silverman, Dev Mehrotra, Mohit Gupta, Michael Gleicher. (2022). "Geometric Calibration of Single-Pixel Distance Sensors." IEEE Robotics and Automation Letters. 7(3).
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Eavesdropping on Herbivores: Using Contact Microphones to Quantify Plant-Insect Interactions
Published in Preprint, 2024
Contact microphones reveal vibrational signals that quantify interactions between plants and herbivorous insects. Read more
Recommended citation: Dev Mehrotra, Laurence Still, Vidit Agrawal, Kimberly Gibson, James D. Crall, Emily N. Bick. (2024). "Eavesdropping on Herbivores: Using Contact Microphones to Quantify Plant-Insect Interactions." Preprint.
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talks
Insect Eavesdropper: Digital Monitoring of Crop Pests Via Vibrational Signals
Published:
Presented the Insect Eavesdropper, an end-to-end bioacoustic pipeline combining contact-microphone acquisition, signal preprocessing, self-supervised feature extraction, and classification. The system achieved 96% precision on in-plant pest detection; the pitch won first place and a $5,000 award. Read more
Falling Walls Competition
Published:
Presented research on machine-learning-enabled sensing for biological and agricultural systems as part of the 2024 Falling Walls Competition. Read more
World AgriTech Featured Showcase: Insect Eavesdropper
Published:
Showcased the Insect Eavesdropper and its approach to non-invasive, ML-enabled crop-pest monitoring. The project received $7,200 in support for the featured showcase. Read more
teaching
Technical Mentoring — Vidit Agrawal
Technical Mentoring, University of Wisconsin–Madison, 2023
Mentored development of unsupervised-learning pipelines for unlabeled acoustic data, focusing on model design, evaluation, and interpretation. Read more
Technical Mentoring — Rishit Malpani
Technical Mentoring, University of Wisconsin–Madison, 2024
Mentored development across OpenCV analysis and acoustic sensor integration, connecting computer-vision and audio pipelines with experimental sensing hardware. Read more
Technical Mentoring — Nachiket Kerai
Technical Mentoring, University of Wisconsin–Madison, 2024
Mentored development across LiDAR sensing and NeRF workflows, including sensor integration and neural 3D reconstruction. Read more
Technical Mentoring — Alex Arovas
Technical Mentoring, University of Wisconsin–Madison, 2024
Mentored development of embedded sensing systems and data-acquisition workflows, emphasizing reliable hardware integration and structured data capture. Read more
Technical Mentoring — Huangjing Qin
Technical Mentoring, Aarhus University, 2025
Mentored development of computer-vision pipelines and reliable datasets, including standardized imaging, quality control, and structured metadata. Read more