Parth Gupta

About Parth Gupta

I am a Master’s student in Computer Science at Ohio State University with a strong foundation in software development, machine learning, and distributed systems. Through internships at IIT and Webonex, I have worked on enhancing image recognition accuracy, optimizing backend operations, and developing scalable software solutions. I have also worked on projects spanning AI-driven health record retrieval, full stack web development, and data visualization, showcasing my ability to apply technical skills to real-world challenges. Beyond academics, I have demonstrated leadership through various co-curricular activities, collaborating on team projects, mentoring peers, and taking initiative in problem-solving tasks. I am always eager to explore innovative solutions and contribute to impactful technology driven initiatives.

Experience

1. Graduate Research Associate

   The Ohio State University College Of Engineering

   Aug 2024 — Present · OH, US

   As a Graduate Research Associate in the NOWLAB at The Ohio State University, mentored by Dr. Hari Subramoni and Dr. D.K. Panda on building AI-powered digital agriculture systems within the NSF-funded ICICLE AI Institute. My work focuses on transforming drone and field imagery into actionable insights for farmers by combining computer vision, geospatial analytics, and high‑performance computing to enable scalable, real-time decision‑making in the field.Key Contributions:1. ICICLE Digital Agriculture Pipeline & Award RecognitionLed development and maintenance of the ICICLE Digital Agriculture pipeline that integrates data collection, preprocessing, model inference, and map-based visualization for crop health and management, contributing to its recognition as a showcase system at the 2025 Ohio Farm Science Review.2. AI-Powered Crop Image Inference & Spray-Zone ShapefilesEngineered deep learning–based crop image inference and classification modules that automatically generate spray-zone shapefiles from drone imagery, enabling targeted field operations and improving field productivity by 66%.3. HPC Deployment with Tapis & SupercomputersContainerized the pipeline and integrated it with the Tapis platform for deployment on Ohio Supercomputer Center resources and San Diego Supercomputer, allowing users to run large-scale inference with a web-based React–Flask interface for real-time crop detection, classification, visualization, and secure downloads, easing farmers’ day-to-day operations.4. Inference Optimization on GPUsImplemented system-level optimizations in the PyTorch inference stack, including GPU memory monitoring and multi-kernel parallel execution strategies inspired by HPC best practices, achieving a 70% reduction in inference time (from 0.80 s to 0.24 s per image) to support near real-time decision-making in the field.

Education

• The Ohio State University

  Masters, Computer Science

• Medicaps University

  Bachelor of Technology - BTech, Computer Science