Experience

RTHM

• Technology, Information and Internet

• Lead Software Engineer

  • Aug 2023 - Present

  Redwood City, California, United States

• Machine Learning Engineer

  • Aug 2022 - Aug 2023

  Redwood City, California, United States Figuring out new ways for a more efficient healthcare system



UMC Utrecht

• Hospitals and Health Care
• 1 - 100 Employee

• Artificial Intelligence Intern

  • Mar 2021 - Jul 2021

  Utrecht, Netherlands • Explored 70+ features of hematology data. • Created embeddings with UMAP to reduce dimensions. • Used different unsupervised clustering techniques to find novel groups in the data with merit in clinical research. • Identified several novel groups and laid down the groundwork for future research to investigate these groups.



Infosys

• India
• IT System Custom Software Development
• 1 - 100 Employee

• Artificial Intelligence Intern

  • Jul 2020 - Sep 2020

  Amsterdam, North Holland, Netherlands Create a computer vision algorithm to classify stroke types in ordinary broadcast video tennis matches. • Adapt YOLO v3 to track players in tennis with 99% accuracy. • Create an algorithm to properly track the lines of the tennis field with 95% accuracy. • Use TrackNet to track the high-speed small tennis ball with 99.8% precision. • Use this information to determine what kind of stroke a player performs, with 90% precision and 95% recall.



Connected Circles

• Netherlands
• Public Relations and Communications Services
• 1 - 100 Employee

• Artificial Intelligence Intern

  • Jun 2020 - Jul 2020

  Amsterdam, North Holland, Netherlands • Used NLP techniques to classify LinkedIn posts with their corresponding topic with 95% accuracy. • Build engagement measures to determine what LinkedIn posts receive the most traction. • Visualized this information along in PowerBI into a dashboard to easily find trends in the data.



CTcue

• Netherlands
• Hospitals and Health Care
• 1 - 100 Employee

• Artificial Intelligence Intern

  • Jan 2020 - Feb 2020

  Amsterdam, North Holland, Netherlands • Reducing the medical ontology SNOMED's size by finding base concepts. • Applying different AI techniques to large data sets (Neural nets, Random forests). • Applying Natural Language Processing to identify keywords.