Meet Daniel Rud, a 2025 PhD graduate developing models — and futures

“I always wanted to stay in California,” says Daniel Rud, a Southern Californian native who graduates with a Doctor of Philosophy in Biostatistics this spring from the Keck School of Medicine of USC.

After he graduated from California State University, Northridge with an undergraduate degree in applied mathematics and statistics, and a minor in computer science, Rud set his sights on USC. He decided to pursue a PhD in biostatistics, which he describes as a coalescence of his background in computer science and statistics.

To help him chart his course, he joined the first cohort of the Los Angeles Biostatistics and Data Science Summer Training Program (LA’s BeST), a six-week biostatistics research training program housed in the Division of Biostatistics in the Department of Population and Public Health Sciences at the Keck School of Medicine.

“LA’s BeST was pivotal for me because it helped me uncover my specific interests in biostatistics,” he reveals. “It was here that I learned about the different applications of statistics and became enamored with the idea of its use to tackle complex public health issues. This experience motivated me to apply my skillset toward making an impact—whether that was trying to understand people’s predisposition for disease, or developing cancer therapeutics, or even contributing to research that could inform public policy and promote health safety.”

After Rud completed the summer program, he applied to doctoral programs in California.

“USC was my top choice—I wanted to continue to work with the faculty I had trained with and thankfully, I was admitted to the university.”

Developing new statistical models

Rud’s dissertation centered on two distinct areas. The first topic focused on advancing pollutant mixture models to understand the effects of groups of pollutants on health outcomes. “One example of how this model could be used is to inform public health policy related to vehicle emissions and safety. Here, we would investigate the mixture of pollutants—such as particulate matter, nitrogen oxides, or carbon monoxide – from exhaust and brake wear emissions. This would help us understand which pollutant is the most vital to control for to lead to better health outcomes,” he explains.

“My first dissertation project was a big moment for me because of its potential impact. I hope to see widespread use of it,” says Rud, whose work is currently being disseminated among researchers in his field.

In his second project, Rud developed new statistical models to better understand gene-environment interactions, aiming to reveal how common environmental exposures such as tobacco smoking and red meat consumption influence disease risk across individuals with different genetic backgrounds. “These statistical models help us identify vulnerable subpopulations that face increased disease risk due to specific environmental or behavioral risk factors,” he says.

Through his doctoral training, Rud has grown into a researcher capable of tackling unfamiliar and complex problems with confidence and persistence. He credits his time at USC for equipping him with the skills and resilience needed to navigate technical challenges—a foundation he will carry with him throughout his career.

“I have come to trust my ability to handle challenges. When facing complicated situations, I have learned to take a step back, break them into smaller pieces, and work through each one. Those little wins add up, and before I know it, I’ve figured out the original problem.”

From trainee to mentor

During his time at USC, Rud returned to LA’s BeST, but this time as a teaching assistant, imparting his knowledge and experience to undergraduate students from across the country, who now looked up to him from a seat he once occupied.

“I helped them understand the possibilities of a future in biostatistics—whether that was pursuing higher education or going into industry. For those interested in applying to graduate programs, I informed them about the PhD experience and gave them advice on life as a doctoral student.”

“One of the most rewarding moments was when I wrote letters of recommendation for two students from the training program who were later admitted into doctoral programs. I remember one student in particular who was apprehensive about the application process, but I reassured her that she would be a strong candidate and encouraged her to apply—she was accepted into her program.”

Supportive academic leadership

There are many faculty members and mentors who have supported Rud in his academic journey.

“I am grateful to Dr. Juan Pablo Lewinger, my advisor, who supported me throughout my PhD. I always enjoyed talking to him, and when there were tough problems, he gave good suggestions and was enthusiastic about my progress. I never felt overlooked, and he was very considerate toward me—he never put me down and was very helpful. Similarly, Drs. Jim Gauderman and Rob McConnell supported me through my PhD as a T32 trainee—they were approachable and friendly, and provided great suggestions for my work. Finally, I’ve been with Dr. Kimberly Siegmund from the start at LA’s BeST. I’ve taken classes with her and conducted research with her — she’s probably been one of my biggest supporters from the beginning. I also cannot forget to shout out our awesome project administrator, Nicole Reyes, who has always been someone I could count on for support and a good chat. I will truly miss all the wonderful faculty I had the pleasure to work with!”

Beyond the PhD

“With the plethora of data available today—which continues to grow rapidly—I believe there is a great opportunity to push the use of machine learning for public health. By leveraging machine learning advancements in biostatistics, I believe that in the future, we’ll have a better understanding of disease etiology and determinants.”

After graduation, Rud will join Google as a software engineer working in machine learning and artificial intelligence. While his focus will be on developing machine learning solutions, he is passionate about how these skills can contribute to solving pressing challenges in healthcare.

“I want to continue to deepen my knowledge of statistical and machine learning methods to tackle complex problems. I really hope for the day when cancer will be as easily cured as a bacterial infection with a general course of antibiotics. By advancing the field of biostatistics, we’ll be able to develop more effective therapeutics so that people won’t suffer at the scale they do now—for me, that is a pressing problem for us in biostatistics and public health to work towards.”