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Data Science Alumna Brings Biology and Data Together in Research Innovation
Alanna Hazlett
Employment
Howard Hughes Medical Institute, Research Technician (Ashburn, VA)
Education
M.S. in Data Science 2025, University of Virginia
B.A. in Biology 2016, University of Northern Iowa
Alanna Hazlett’s journey into data science reflects the growing intersection between scientific research and advanced analytics. With a background in biology and a newly earned M.S. in Data Science (MSDS) from the University of Virginia, Hazlett is applying her skills at the Howard Hughes Medical Institute, where she works with cutting-edge imaging data and predictive models.
Her story highlights how the UVA School of Data Science equips students to bridge disciplines, expand career possibilities, and contribute to meaningful research. Below, Hazlett shares more about her experience, from her day-to-day work and favorite courses to the projects and people who shaped her path.
Q: What does a typical day in your role look like, and what drew you to this position within the data science field? How do you collaborate with other teams or departments?
I currently work in a dry lab at the Janelia Research Campus of Howard Hughes Medical Institute. I work with microscope imaging data, segmentation models, and synapse prediction models. My current role is hybrid, so much of my work is conducted virtually, utilizing Slack and Zoom as communication tools.
Q: What did you learn in the MSDS program that’s been most useful in your career so far, and is there anything you wish you’d spent more time on while in the program?
I now have a broader understanding of the intersection of data science and research. While AI is expanding rapidly right now, many researchers have been utilizing modeling for quite a while to make discoveries and advancements in research.
Q: Were there specific classes, projects, or professors that you found particularly influential in preparing you for your career?
As I am currently looking for a data scientist position within science research, biotech, pharma, or hospital systems, I think that DS5003: Healthcare for Data Science taught by Christian Wernz provided a solid foundation for healthcare data science. This class was an elective, so I had completed most of my graduate courses and was able to utilize that knowledge, expand upon those skills, and apply them to the healthcare setting. I utilized SQL to extract and manipulate healthcare data, Tableau for creating visualizations and dashboards, and learned about the ethical implications of utilizing AI and machine learning in the healthcare setting.
Additionally, DS6001: Practice and Application of Data Science, or the so-called “Pipeline Course,” covers a bit of many of the fundamentals of data science that are very practical. The course is very comprehensive, covering how to get data from files or from APIs, utilizing SQL and creating databases, data wrangling utilizing the Python library pandas, and visualization methods.
Q: How has the MSDS degree set you up for long-term success, positioning you for career growth and new opportunities?
Prior to my MSDS degree, I did not have much experience with coding, data science, and AI. This opportunity was a great fit for my analytical and detail-oriented nature. It has allowed me to expand my career options and career growth for the future by combining my background in biology with data science. I now have the skill set to explore and investigate all kinds of data and share those insights with stakeholders.
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Q: Was there a student experience or classmate/cohort interaction during your time at the School of Data Science that stands out as particularly memorable for you?
Yes! While I enjoyed working on group projects and with different people throughout the program, my favorite was working with Naomi Ohashi and Timothy Rodriguez. We worked together on our DS6050 Deep Learning project. We utilized convolutional neural networks to classify chest diseases in X-rays.
After training and validating our models, we achieved great accuracy, AUC, and F1-Score metrics for our classifications. We then wanted to utilize Grad-CAM, an algorithm that utilizes gradient-weighted class activation mapping, to display a heatmap over the X-ray images. This helps display why the model made its classification by highlighting the areas of interest. We all learned a lot during this project and were very excited by the success of our project.
Our professor Sodiq Adewole encouraged us to submit our project to arXiv, and our paper was published: Chest Disease Detection in X-Ray Images Using Deep Learning Classification Method.
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Q: As you continue your data science journey, what experiences have helped you explore the field and build your skills?
I recently participated in and won a hackathon hosted by Data Community DC, Inc. I was not entirely sure what to expect as this was my first-ever hackathon. The World Bank had several questions of interest, and teams were formed based on those questions. Prior to the event, I did not know any of my teammates. Our team was investigating how laws across pay equity, workplace protections, entrepreneurship, and parenthood policies influence women’s labor-force participation and economic outcomes. We had a few hours to load the data, create descriptive statistics and visualizations, and perform data modeling.
The event concluded with each group presenting to the hundreds of people in attendance. My group really emphasized storytelling and ensuring that the audience understood the impact the laws are currently making, what actions could be taken to improve women’s labor-force participation and economic outcomes, and what tools are available to assess the laws.
Q: How do you stay connected to UVA and the School of Data Science, and why does it matter to you?
I have enjoyed participating in the conferences hosted by the School of Data Science. I have attended the WiDS conference and Datapalooza. These events have been a great opportunity, both while I was in the program and after, to gather with members of my cohort and other students in person.
The School of Data Science provides opportunities specifically for current students and alumni to maintain and create connections. It is great to be able to see how members of my cohort are doing and see where their interests lie in the broad field of data science. Datapalooza has many different topics covered every year, and there is always something new to learn, either in the breakout sessions or during the keynote.
Learn more about the part-time, 100% online M.S. in Data Science at the University of Virginia. Request more information, connect with Admissions, or start your application today.
