Gajanan Kothawade

Machine learning engineer and applied AI researcher building computer vision systems from prototype through production.

Experience spans training and benchmarking deep learning models (segmentation, object detection, classification) on real-world sensor, spectral, and geospatial data, and shipping a computer vision tool now in commercial use. Strong statistical foundation for rigorously validating model behavior, including designing custom protocols to catch distribution shifts and confounding that standard validation misses. Comfortable moving quickly across new data types and problem domains, building production-grade pipelines and decision-support tools, and translating technical work for both technical and non-technical audiences.

• Machine Learning Researcher - The Conservation Fund Freshwater Institute - Shepherdstown, WV (2024-08 - 2026-08) -
  • Computer vision pipeline, prototype to production: Led end-to-end development of a hyperspectral computer-vision pipeline (Python/OpenCV) for biological classification and automated ROI segmentation, achieving 81.8% classification accuracy and 98.8% mAP@50 on real-world production data.

  • Edge and cloud model benchmarking: Designed and executed a systematic benchmark of 12 object detection model variants (YOLOv5u, YOLOv8, YOLO11, YOLO26) across 7 training dataset sizes, evaluating accuracy, data efficiency, and inference latency on GPU (A100) and edge (Raspberry Pi 5) hardware.

  • Model validation and robustness: Designed a leave-one-date-out validation protocol that exposed session-specific distribution shift and confounding invisible to standard cross-validation, directly testing whether a model's predictions held up outside their training conditions.

  • Production deployment: Contributed to prototyping FilletCam 2.0 from early development, including hardware assembly, a YOLOv8-based computer vision tool for automated quality and defect assessment, now commercially deployed and in active use by industry customers.

  • Applied geospatial ML: Built a Google Earth Engine and Python monitoring workflow fusing satellite imagery with in-situ sensor data across 20 sites, delivering a decision-support tool for non-technical stakeholders.

• Graduate Research Assistant (PhD Dissertation) - Washington State University - Pullman, WA (2019-08 - 2024-07)
  • Sensor-to-image modeling: Applied PyTorch to reformulate high-dimensional sensor spectra as 2D image data for CNN-based classification of plant disease biomarkers, validated against molecular (qPCR) ground truth at 88% accuracy.

  • Statistical model benchmarking: Benchmarked 9 classifiers (Random Forest, SVM, Logistic Regression, Gradient Boosting, LDA/QDA, k-NN, Naive Bayes) via PCA and 5-fold cross-validation, with PLS-VIP, ANOVA, and Tukey HSD statistical analysis.

  • Geometric and 3D signal processing: Built UAV-derived 3D point-cloud pipelines (Python, Open3D: convex hull, alpha-shape, voxel-grid algorithms) for canopy segmentation and volume estimation from multi-view field imagery, published first-author at IEEE MetroAgriFor 2021.

  • Field data collection and calibration: Planned and flew multispectral and thermal UAV imaging missions as part of a DOE/PNNL project, radiometrically calibrated imagery against reflectance panels, and used RTK ground control for georeferencing across repeated acquisitions.

  • Quantified systematic bias between satellite and UAV vegetation-index measurements across three growing seasons, a data-quality finding that changed how the team interpreted downstream results.

  • Teaching Assistant for two graduate courses, Instrumentation and Measurements (BSYSE 541) and Unmanned Aerial Systems in Agriculture (BSYSE 552).

  • Mentored undergraduate interns and junior lab members on experimental methods and data analysis.

• Data Scientist, Geospatial ML - Quantela Inc. - Bangalore, India (2017-06 - 2019-07)
  • Decision-support dashboards: Built land-use classification and flood-risk mapping models from large-scale satellite imagery, translating raw sensor data into risk assessments and dashboards that public-sector stakeholders used directly for decision-making.

  • Data infrastructure: Architected AWS-based pipelines for large-scale satellite data ingestion and feature engineering, building a reproducible methodology applied consistently across new regions and datasets.

• Research Intern - Indian Institute of Tropical Meteorology (IITM) - Pune, India (2017-01 - 2017-06)
  • Atmospheric spectral analysis: Analyzed 26 years (1979-2004) of ERA reanalysis and LMDZ4 climate model data to characterize atmospheric variability in the Karakoram and Central Himalaya, isolating synoptic-scale signals via Lanczos high-pass filtering on HPC.

  • Periodicity discovery: Applied power spectral density analysis to the filtered data, identifying a dominant 3 to 8 day periodicity in Western Disturbance activity, consistent across both reanalysis and model datasets.

• Earlier Research Roles - TCS, KisanHub, IIRS/ISRO, India - India (2013 - 2016)
  • Mapped spatio-temporal rice cultivation across 330,000+ hectares using Landsat 8 spectral indices and ensemble ML, achieving 87.99% accuracy for land-use monitoring (TCS Innovation Labs).

  • Automated FAO-56 evapotranspiration estimation for 500+ farm parcels and built satellite-data processing workflows with unit testing and QA (KisanHub).

  • Mapped land degradation (waterlogging, soil salinity) across 1,868 km² in Haryana using Landsat-8 and GIS, validating results against lab-tested soil pH, electrical conductivity, and moisture content (IIRS/ISRO).

Ph.D. - Biological and Agricultural Systems Engineering - Washington State University, USA (2019 - 2024)

M.Tech. - Modeling and Simulation (Machine Learning) - University of Pune, India (2015 - 2017)

B.Tech. - Agricultural Engineering - Mahatma Phule Agricultural University, India (2010 - 2014)