Westwood High School
Hi there! My name is Uma Sthanu, and I am a student with a passion for Computer Science and Business. My goal is to help bridge the gaps in education and healthcare globally, and I strongly believe in the potential of technology to bring this change. With this goal in mind, I have incubated 2 companies even as a pre-teen that I am still actively working on: T2L Academy, an Ed-tech startup, to provide education for all, removing economic barriers. Making resources accessible to all through videos delivered online & creating a learning hub to help K-8 students, I hope to increase participation and exposure for the underprivileged to expand their learning beyond the school curriculum. As someone who had the fortune to be able to participate in various elementary, middle, and high school school competitions in Science, Math, Arts & Literature, I believe that encouraging participation in these competitions will make learning more fun for all. Sayffer is a Health-tech startup that provides a suite of applications to help improve healthcare on the global scale. Our first project, building the Sayffer mobile application, worked to create a secure & compliant health data sharing application for K-12 staff, teachers & parents. The mission is to digitize the manual communication and collaboration that exists in K-12 around health data. The application is accessible to all and available on all mobile platforms, built to ensure HIPAA & FERPA compliance. Our most recent project is TBDetect (tbdetect.sayffer.com). I recognized the potential of technology of AI & Machine Learning to solve problems at a global scale and decided to use Machine Learning to solve the ongoing Tuberculosis pandemic, which, despite being centuries old, continues to kill over a million a year, especially in Africa and South Asia. I hope that TBDetect can help many nonprofit organizations and smaller hospitals battling Tuberculosis around the world and struggling with the lack of sufficient qualified medical resources. In the past, I have completed a certification on Machine Learning for Healthcare from Stanford University, and on Design Thinking for Innovation and Business Strategy from the Darden School of Business at the University of Virginia. I have also obtained other marketing and data science certifications from Google, IBM and Hubspot, attended a Girls in Engineering summer camp at UC Berkeley, and is part of the Google CodeNext Class of 2023. I am also a 7-time Science Fair winner at the regional, state, and national levels – finishing State 1st in the Software & Systems category in 2022, being a Broadcom MASTERS semifinalist (Top 300 in the Nation) in 2021, winning the Broadcom Coding with Commitment Award & the Society of Women in Engineering Award for Excellence in Science, Engineering and Mathematics for 6-8th graders in 2020, the UT Austin Dell Medical School Future Health Leader Award in 2018 for her earlier projects in science, technology, and healthcare, the Michael & Susan Dell Healthy Living Center Health Innovation award in 2023, and the BioAustin Health Innovation award that same year. In addition, I have participated in business competitions such as DECA and FBLA, and will go on to represent the State of Texas at the International Career Development Conference for the former in April 2023.
Tuberculosis is a major global health issue that affects millions of people around the world, particularly in areas with limited access to medical professionals. Each year, TB causes around 1.5 million deaths, making it the world’s top infectious killer. Early detection is crucial for effective treatment, but in many cases, individuals may not have access to proper diagnostic tools. To address this problem, I developed a new method for analyzing diagnostic tests with 97% accuracy using a machine learning model and an accompanying iOS app. The app (TBDetect) is designed to detect Tuberculosis in individuals by analyzing images of sputum smears. TBDetect has the potential to make a significant impact in areas where access to medical professionals and diagnostic tools is limited. It provides a simple and convenient way for small hospitals and volunteer organizations to screen patients for Tuberculosis and seek treatment if necessary. In the app, a microscope slide with a sputum smear is photographed (under a low-cost, portable microscope) with a smartphone camera and uploaded to the machine learning model, which has been trained on a large dataset of sputum smear images labeled with the presence or absence of Tuberculosis. The model then returns a prediction of whether or not the individual has the disease. Overall, this project highlights the importance of utilizing technology to address global health issues, particularly in areas where healthcare access is limited. The development of TBDetect demonstrates the potential for using AI to improve healthcare outcomes and save lives.
Currently, communication in schools when it comes to student allergies, cases of the flu, and COVID-19 outbreaks is done manually, and can be time-consuming and inefficient - we saw this during the COVID-19 pandemic, with manually-updated dashboards to keep the community informed. When it comes to food allergies, in elementary and preschools it is very important that allergies of younger students are monitored and students in that class avoid bringing or sharing food with that allergen in it - but the current paper letters that are sent home with students are prone to getting lost or accidentally thrown away. Sayffer is a mobile app, available on both iOS and Android, that can help make administrative tasks related to student health easier and more secure. It uses a secure database to store important information and only allow the registered and verified nurse for an educational institution to be able to access it when necessary, while automatically sending anonymous alerts and non-personally identifiable information to parents. The app is HIPPA and FERPA compliant, so that communities can have a convenient, automated system without having concerns about privacy and security. To learn more and watch a demonstration video of the app, visit sayffer.com.
EcoSnap is a prototype for a mobile application that serves as an all-in-one toolkit to help the environment, including a "News" feature for fact-checked environmental news from reliable sources, a "Scan" feature that uses a machine learning algorithm to identify recyclable materials (including non-traditional ones, such as Ridwell or Terracycle, independent companies that recycle traditionally non-recyclable items) from an image, and a "Map" feature displaying local opportunities for volunteering that can help the environment. This application can help raise awareness regarding environmental issues, and encourage more people to help the environment by showing them small steps they can take to make an impact. In 2022, this project was recognized as a winner by the Coolest Projects Showcase.