Data Standards, Ethics, and Governance: A High Schoolers Take Away from IAFP

Shanmukh Kottapalli, a rising senior in high school (yes!), spent a summer interning for Food Safety Strategy, focusing on data analysis, and was able to join the team at IAFP. Here's what stood out to him.

This summer, I attended the International Association for Food Protection (IAFP) Annual Meeting for the first time. As a high school senior focused on data science, I am interested in how data and technology are being used to improve different industries, such as food safety. After the conference, I left with a much stronger understanding of how science, technology, and collaboration are coming together to help solve food safety-related issues.

One of the first things I explored was the poster sessions. These were research projects presented by graduate and PhD students, and many of them were directly connected to industry challenges. One that stood out to me focused on plasma-activated microbubble water. It uses plasma-charged bubbles to clean food surfaces and kill bacteria without relying on strong chemicals. It was a smart, innovative approach to reducing contamination more safely and sustainably.

Many of the sessions I attended focused on artificial intelligence and machine learning. I was especially interested in the risk component and how these tools are already being used to detect risks before they happen. For example, companies are using AI to combine lab test results, environmental sensor data, and supply chain information to predict when and where contamination might occur. This allows them to act early, instead of just responding after the fact. These predictive tools are already improving how inspections are prioritized and how outbreaks are tracked.

However, I also heard over and over again about the obstacle of inconsistent data. Many companies and labs collect food safety data using different devices, units, and formats. That makes it hard to combine or compare results across systems. This slows down progress, limits the accuracy of AI models, and makes collaboration harder. Experts at the conference are working to create common standards so the data is clean, structured, and easy to share. Before attending IAFP, I didn’t realize how important a data standard is.

Ethics was another central theme. Several speakers talked about the importance of building systems that are fair, transparent, and respectful of privacy. These tools might eventually influence major decisions about food recalls, regulations, or inspections. That means companies need to be clear about how decisions are made and avoid bias in the data.

Another highlight for me was learning how DNA sequencing is being used to trace contamination more precisely. DNA sequencing tools can give us better insight to potential sources of contamination and help to narrow in on sanitation program efficacy. What stood out was how this technology is no longer limited to government labs. It is becoming faster and cheaper, which means even smaller food companies are starting to use it.

Overall, IAFP showed me how broad and connected this field really is. Food safety is not just about inspections or lab results. It is also about systems, such as data pipelines, sensors, predictive models, sanitation methods, and DNA analysis, and more importantly, using these tools to proactively prevent food safety issues rather than just reacting to results after something goes wrong. The one session I attended on data sharing talked about how the data governance piece is as crucial as the data collection itself. If people aren't on the same page regarding data usage, shared goals, and a structured process for data sharing, the efforts are likely to fail. One of the biggest takeaways was that being able to tell a good story with data is just as important as running the analysis. Whether you’re explaining a poster, presenting AI findings, or sharing results with decision-makers, how you communicate the message matters.

Attending IAFP helped me connect what I’ve learned in data science to real-world applications in public health. It also made me more aware of the challenges, from standardizing data to keeping AI accountable. I left the conference with a stronger sense of direction and a better understanding of where my skills can make an impact. I am grateful for the opportunity to learn from numerous researchers, scientists, and industry leaders, and I am excited to continue exploring how data science can be applied to solve meaningful problems. I’m currently exploring college programs in data science, applied mathematics, and statistics. If you're interested in connecting or following my journey, feel free to reach out on LinkedIn.