Image
Bridging the Gap Between AI and Medicine, One Dataset at a Time
Naomi Ohashi
Employment
National Cancer Center Japan (Kashiwa, Japan)
Education
M.S. in Data Science 2025, University of Virginia
M.P.A. in Public Administration 2002, New York University
Naomi Ohashi was a project manager for a premier biomedical research institution in Maryland, and she was already using data science to improve access to innovative cancer therapies. But Ohashi knew that there were gaps in her knowledge, and wanted to gain deep expertise to position herself in leadership roles. It was this desire that led her to apply for the M.S. in Data Science (MSDS) online program from the University of Virginia.
We spoke with Ohashi to learn how a master's in data science at UVA took her career to the next level, opening new opportunities to tackle unexplored approaches in cancer treatment and biomedical research.
Q: How has the MSDS degree accelerated your career or opened opportunities that wouldn’t have been possible otherwise?
Before I applied to UVA's master's program, I was working as a technical project manager at Frederick National Laboratory for Cancer Research, managing AI-driven drug discovery projects with consortium scientists. Joining the consortium was genuinely eye-opening. I got to see firsthand how machine learning and deep learning could accelerate the drug discovery process, ultimately helping get cancer treatments to patients faster, at lower costs, and with more options available. That curiosity about the mechanics under the hood is what motivated me to start the MSDS program.
I came in with gaps in my knowledge and left with skills I hadn't imagined I'd have. The MSDS program gave me a complete picture of the full-stack workflow, from data collection all the way to deploying a working application. The program made me a fundamentally better, more hands-on project manager.
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?
Statistical Learning, Practice and Application of Data Science, and Ethics connected directly to my work in some way. Deep Learning opened the door to exploring AI, including computer vision to detect lung diseases on chest X-ray images and building generative molecular design using large language models for novel properties. These courses gave me a grounded understanding of both what AI can do and where its limits lie. Data Engineering and Foundation of Computer Science built my confidence in full-stack software development. They were practical courses where the learning immediately translated to real impact.
Our capstone project focused on detecting bias in existing models. Our team evaluated bias detection tools to assess how accurately they identify biases in facial images. I wish we had more time to continue the research — to develop tools that evaluate bias before models are released and standardize their use in responsible AI development.
Q: How has the MSDS degree set you up for long-term success, positioning you for career growth and new opportunities?
As AI tools become more accessible to non-coders, the demand for people who truly understand the technology grows. That’s where I see my edge as a data scientist. I can speak to both the scientific and operational dimensions of AI in ways that many generalist users can’t. That kind of deep expertise opens doors to broader opportunities and positions me well for growth into leadership and management roles down the road.
Recently, I received an offer to join another cancer research organization in Japan. I look forward to continuing to apply my knowledge and experience in the biomedical research field, where there are still many unexplored approaches to drug development, diagnostics, and operational improvements.
Q: What’s exciting to you within the industry right now? What trends, innovations, or breakthroughs in industry are you paying attention to?
Professor Sodiq Adewole told us to stay current in the rapidly advancing field and suggested bookmarking the HuggingFace Daily Papers. So, staying current is something I try consistently. Now, it is a part of my job to share new ideas and techniques with my team. I’m excited to witness the advancement of medicine, and applications of personalized medicine or digital twins in cancer treatment may not be too far from becoming a reality.
I keep pushing myself to learn new things. Recently, I earned my Microsoft AI-900 certification for Azure cloud and plan to take online courses on Model Context Protocol and OpenClaw for building agentic models. I have also been connected with local AI interest groups and attending networking events, both to keep learning and to stay connected with what’s happening across the field.
Q: Can you share a specific project or problem at work where you directly applied skills from the MSDS program?
In our AI-driven drug discovery project, we used machine learning to predict molecular properties using large datasets from chemical libraries. As I moved through each course, I would regularly bring my “aha” moments back to my team at work. That led to some real, tangible improvements: We started pulling in big data via APIs in cloud environment, adopted GitHub Actions to track code iterations, and automate our continuous integration and continuous deployment pipelines.
Q: What made UVA’s MSDS program stand out to you? Were there specific classes, projects, or professors that were especially impactful?
The program struck the right balance between theory and real-world application, but what truly set it apart was the caliber of the people, both faculty and fellow students. One of the most impactful projects was the deep learning project. I was grateful to be working with Alanna Hazlett and Timothy Rodriguez who were professional and fully committed despite our challenges of work-school-life balance.
We experimented with different convolutional neural network models to detect lung diseases from X-ray images, and our best model achieved nearly 98% accuracy. Under professor Adewole’s guidance, we went through the entire research cycle, from collecting data and building models to analyzing the results and writing a scientific paper as a team. That end-to-end experience was genuinely invaluable, and it’s something I point to on my resume.
Q: How do you stay connected to UVA and the School of Data Science, and why does it matter to you?
I attended Datapalooza and Women in Data Science, both organized by the School of Data Science. They were wonderful opportunities to reconnect with professors and catch up with my classmates. I am about to start a new position in Japan and I’m already looking forward to connecting with the UVA Club of Japan through UVA Engagement. The Hoo community really does follow you wherever you go, and that means everything. Wahoowa!
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.
