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About


https://www.linkedin.com/in/umasthanu/

Uma Sthanu is a purpose-driven innovator at the intersection of science, technology, and business. Leveraging computational tools and entrepreneurial thinking to drive better healthcare outcomes, protect the environment, and bridge educational gaps.

Research & STEM:

Exploring neurodegenerative diseases through computational biology

  • Winner of 27 Science Research awards & 44 STEM awards, including:
    • ISEF 2025: Top Award (Mary Sue Coleman Award for Life Sciences Innovation & Impact)
    • ISEF 2024: Grand Award
    • Texas State Science & Engineering Fair: First Place (2022, 2025)
    • Greater Austin Regional Science & Engineering Fair: 18 awards from 2017-2025
  • MIT RSI Scholar ’25
  • Bioinformatics research at Harvard
  • Computational Biology research at Yale and Stanford
  • Neurobiology research at UT Austin
  • Biostatistics research at UAB
  • Hackathon wins in sustainability, AI, and innovation (WaffleHacks ’23, alGIRLrithm ’25)

Business Acumen & Communication:

Pairing research with strategy to maximize real-world impact

  • 14 Business awards, including:
    • 3× DECA ICDC Awards: 2 Place Glass Award in Professional Selling, Top Test Scores, and Finalist in Business Administration, Financial Operations & Professional Selling
    • Top 8 in U.S. – National Finalist, Digital Presentation Skills Challenge ’24
    • Featured Speaker in CEE Lyceums and other STEM organizations
  • Top 25 Worldwide – Runner-up, New York Times Global STEM Writing Contest

Leadership & Entrepreneurship:

Driving innovation through collaboration, problem-solving, and mission-driven ventures

  • 5 Leadership awards to date
  • Leadership roles in DECA, NTHS, Academy Ambassadorships, Student Council, and Spanish Honor Society

Founder of three startups:

  • Sayffer – HealthTech incubator (projects: Sayffer & TBDetect)
  • EcoSnap – GreenTech app powered by AI
  • T2L Academy – EdTech platform supporting non-curricular competition prep

TBDetect

Working to use the power of Neural Networks and Machine Learning to provide accurate Tuberculosis diagnosis to all.


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