Panel 34: Navigating your Tech Journey
Tech careers can take you in unexpected directions, and each of us—Clary, Anh, Melita, and Ramisa—has traveled a different path this summer. From optimizing large-scale systems and exploring scientific data storage, to applying machine learning and enhancing robotic privacy, our experiences have stretched across various tech domains. In this panel, we’ll talk about how we adapted to new environments, tackled real-world problems, and built skills that will shape our future careers. It’s not just about the technical work; it’s about the lessons, teamwork, and growth we experienced along the way. Whether you’re starting your journey or looking for insights, join us as we share what we learned and how you can apply it to your own path in tech. Also, the panel features students with diverse experiences in the tech field, with some focusing on industry internships and others on academic research. They will discuss how their respective experiences have influenced their understanding of technology and career aspirations. This session offers a well-rounded view of industry practices and research innovation.
Moderator: Isabelle Beaudry, Assistant Professor of Statistics
Maximizing Efficiency: Enhancing System Performance Through Database Query Optimization
Clary Nguyen ’25, Computer Science and Math double major
HPC and Large Data Storage
Anh Nguyen ’26, Computer Science and Statistics double major
Estimating COVID-19 Daily Infections Using Wastewater RNA Data and Artificial Neural Network
Melita Madhurza ’26, Computer Science and Statistics double major
Enhancing Privacy in Domestic Robotics
Ramisa Tahsin Rahman ’25, Computer Science major, Mathematics minor
Speaker name: Anh Nguyen
Title: HPC and Large Data Storage
This past summer, I was a software engineering intern at the NSF National Center for Atmospheric Research (NCAR) and a part of the SIParCS (Summer Internship in Parallel Computer Science) program. I worked on an application to store information about hundreds of millions of files and directories inside NCAR’s scientific data storage to facilitate searching for them in the future. Additionally, an interesting learning opportunity I got to experience was visiting NCAR’s Wyoming Supercomputing Center. In this presentation, I will talk about my application process, the highlights of my internship, and some valuable lessons I learned.
Speaker name: Ramisa Tahsin Rahman
Title: Enhancing Privacy in Domestic Robotics
“My DAAD RISE internship at the Human-Computer Interaction lab in Germany provided a unique opportunity to contribute to the growing field of privacy in domestic robotics. Working with a team of researchers, I helped design visual privacy indicators to increase user awareness of data collection in smart home environments.
In this presentation, I will reflect on the research process, including user studies that tested the effectiveness of these indicators. I will also explore the broader implications for privacy in human-robot interaction and how this project has shaped my understanding of ethical technology design.
Join me as I share insights from my summer experience and discuss the impact of this project on my professional development.”
Speaker name: Clary Nguyen
Title: Maximizing Efficiency: Enhancing System Performance Through Database Query Optimization
“During my software engineering internship at Rubrik in Palo Alto, CA, I worked on two significant projects using Go, SQL, Docker, and MySQL.
The first project involved migrating a library, reducing query response times by 50%, resolving customer issues, and addressing technical debt by refactoring over 10,000 lines of code and generating database schema migrations. In the second project, I optimized storage costs by deleting 98% of stale data.
I adhered to industry best practices like unit testing, code reviews, debugging, and cross-functional collaboration. My typical week included working from the office three days, with 30-minute stand-ups, lunches with the team, and remote work two days. I also participated in weekly intern events and explored various cities in the Bay Area, including San Francisco and San Jose.”
Speaker name: Melita Madhurza
Title: Estimating COVID-19 Daily Infections Using Wastewater RNA Data and Artificial Neural Network
My research at the UMass Math/Stat REU focused on using machine learning to estimate daily COVID-19 infection rates through wastewater data. Since many COVID-19 cases go unreported, wastewater RNA concentration offers a novel way to track the spread of the virus. By analyzing RNA data from sources like Biobot and the CDC, along with weather data, we developed a neural network model to predict true infection rates. The model’s input included wastewater RNA concentration, temperature, and precipitation, while the output predicted daily recovered case counts. This approach could inform future pandemic response efforts by providing a reliable, scalable method for monitoring infections in real-time. Despite data limitations, our model showed promising results, and future improvements will focus on increasing data accuracy and refining training criteria.