Jun 2023 – Sep 2023 · 3 mos · Riverside, California, United States · Hybrid

• Regularly checked and maintained sensor status, ensuring optimal performance and reliability in pest detection.
• Utilized SQL for effective database management, contributing to data organization and accessibility.
• Cross-referenced sensor data with weather station information to verify sensor accuracy, enhancing data reliability for pest prediction.
• Initiated the development of a tool for segmenting customer fields and assigning sensors to precise coordinates. This tool also aimed to provide real-time dashboard updates on sensor status, temperature, and insect counts
• Set up and conducted experiments for sensor improvement, focusing on enhancing detection capabilities for fruit flies
• Participated in field operations, including the repair and replacement of faulty sensors and validating insect counts against sticky trap data to ensure accuracy
• Contributed to the development of cutting-edge traps that classify pests in real-time, aiding farmers in making informed decisions about pesticide usage

Sep 2022 – Sep 2023 · 1 yr

• Created and tested crop-type prediction models using spatio-temporal satellite datasets, achieving
• enhanced accuracy in agricultural analysis
• Conducted comprehensive evaluations of Meta’s Segment Anything Model on RGB satellite imagery,
• focusing on zero-shot performance for precise crop-type prediction.
• Engaged in innovative efforts to refine and enhance an existing model for improved performance, gaining
• valuable insights into advanced modeling techniques and challenges.

Jun 2022 – Apr 2023 · 10 mos

• Provided specialized support to research groups requiring extensive computational resources, facilitating
• access to NSF-funded computational clusters.
• Collaborated closely with research teams to initiate and optimize their use of advanced GPU/CPU
• clusters, ensuring alignment with project needs.
• Engaged in hands-on code optimization and performance testing across various computing clusters,
• enhancing research efficacy and outcomes.

Feb 2022 – Sep 2022 · 7 mos

• Implemented a U-Net model from scratch for Multi-class Image Segmentation, conducting comprehensive experiments to assess its performance and capabilities.
• Reproduced and evaluated various models and techniques presented in academic research papers, applying theoretical knowledge to practical scenarios.
• Experimented with tensor decomposition methods to explore their potential in improving model
• efficiency and accuracy.
• Engaged in collaborative research projects, working closely with multiple research groups to broaden the
• scope and impact of the studies.

Aug 2019 – Jul 2022 · 2 yrs 11 mos · Fremont, California, United States

Jan 2019 – Mar 2019 · 2 mos · Fremont

• Food server.

Jul 2017 – Dec 2017 · 5 mos · Milpitas

Mar 2017 – Jun 2017 · 3 mos · United States