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About Me

I am a computational biologist/agronomist passionate about using data science and imaging data to translate biological and agronomic sciences into digital solutions that better understand, diagnose, and treat various tissue-based diseases or increase the sustainability and profitability of agricultural systems. I obtained my Ph.D. degree in Agronomy from Kansas State University where I used optical sensors to assess and monitor crop and soil conditions.

Research Interests

  • Highly multiplexed imaging
  • Spatial Omics
  • Tumor biology
  • Remote sensing application in Precision Agriculture
  • Crop physiology

Work History

Computational Biologist (2021 May - Current)\ Spatial Technology Platform, Broad Institute of MIT and Harvard, Cambridge, MA.

  • Designed various pipelines to process and analyze highly multiplexed imagery data on the Google Cloud Platform.
  • Led the effort on processing and analyzing CODEX/CyCIF/mIF/MERFISH/Xenium/CosMX data on multiple projects.
  • Compared protein-based spatial biology methods across tissues.
  • Conducted the systematic benchmarking of imaging spatial transcriptomics platforms in FFPE human samples across healthy and cancerous tissues.

Data Scientist (2018 March - 2021 Jan)\ Geoinnovation, Indigo Agriculture, Charlestown, MA.

  • Designed on-farm experiments to test the effect of beneficial microbes coated on the seeds on the grain yield of Corn, Soy, Cotton, and Rice.
  • Applied traditional statistical methods and machine learning techniques to derive insights from the data and made actionable recommendations to our consumers.
  • Designed and implemented a pipeline for imagery data management (transfer, storing, logging, and quality control).
  • Developed protocols for UAV imagery data collection implemented in10 states in the US and South America.
  • Developed a pipeline to count individual corn plants, built an internal tool to direct soil sampling in farming fields, using Hi-Res RGB images.
  • Built UAV imagery-based crop condition reports as an alert and scouting tool for farmers to do in-season crop management.
  • Build modules within the “machine data factory” to clean all types of machine data automatically including as-planted, as-applied, and as-harvested data.

Digital Agronomist (Intern) (2017 March - 2017 Aug)\ The Climate Corporation, St Louis, MO.

  • Characterized soil properties using high-definition bare soil aerial imagery including color infrared and thermal data.
  • Evaluated the relationship between imagery-based soil features and in-season crop response on corn and soybean.
  • Developed new indices for quantifying the sub-field temperature variation and a new method for creating management zones using bare soil imagery.