Tianzhou (Charles) Ma’s journey to becoming a biostatistician began at the University of Pittsburgh, where he earned a doctoral degree in biostatistics in 2018. Before arriving at Pitt, Ma earned his bachelor’s degree in genetics from the University of Toronto and completed his master’s in biostatistics at Yale University. His master’s thesis focused on genome-wide association studies, which sparked his interest in statistical genetics and led him to George Tseng, a professor in the Department of Biostatistics and Health Data Science. Under Tseng’s mentorship, Ma found a perfect match for his research interests in statistical genomics and bioinformatics.
Reflecting on his decision to join Pitt, Ma shared, “I was introduced to Dr. Tseng by my master’s advisor and colleagues. His lab’s research focused on developing novel statistical and bioinformatic methods for transcriptomic meta-analysis and multi-omics data integration, which really fits my research interest and career path well.” This alignment of interests and the collaborative environment of the department proved to be crucial in Ma’s academic development. He appreciated emphasis on interdisciplinary collaboration, which allowed him to work with experts from various fields, including cancer, psychiatry and epidemiology. The supportive atmosphere created by faculty and friends encouraged him to explore innovative ideas and methodologies.
During his five years at Pitt, Ma immersed himself in that environment, working closely with leading scientists at UPMC. “From my first year, I started to work with Dr. Tseng on novel methodology projects, while at the same time, I worked closely with breast and ovarian cancer researchers to analyze data generated from their labs,” he explained. This hands-on experience not only honed his technical skills but also deepened his understanding of the clinical implications of his work. He often attended lab meetings and discussions, where he learned to communicate complex statistical concepts to non-statisticians, a vital skill for any researcher.
Ma’s dissertation, titled “Differential expression and feature selection in the analysis of multiple omics studies,” was a significant milestone in his academic journey. It comprised two main components: the development of Bayesian hierarchical models for differential expression analysis of multiple transcriptomic studies and a novel sure screening framework for feature selection in omics studies. “Differential expression analysis is a key tool in bioinformatics that helps identify biomarkers for specific diseases and understand the biological processes that underlie these conditions,” Ma noted. His contributions have been important in helping the understanding of gene expression in various diseases, particularly cancer. He often presented his findings at departmental seminars and events, receiving valuable feedback that further helped his research. The opportunity to present at conferences also allowed him to network with other researchers and gain insights into the latest advancements in the field.
Reflecting on the challenges he faced during his dissertation, Ma recalled, “The first challenge we faced was finding high-quality RNA-seq data for testing our methods. It took me several months of searching through databases before I found suitable examples.” This persistence paid off, as the data he eventually sourced became integral to his research and even contributed to his advisor’s successful grant proposal. He also learned the importance of networking with other researchers, which helped him gain access to valuable datasets. “I reached out to colleagues and attended workshops, which opened doors to collaborations that were crucial for my work,” he added.
Another challenge was the heavy computational demands of Bayesian models. “At that time, Bayesian methods were new to me and the group. We formed a Bayesian study group to review literature and discuss methodologies,” Ma explained. Through collaboration and shared learning, they overcame these obstacles and developed efficient methods for Markov Chain Monte Carlo (MCMC) sampling. This initiative not only enhanced his understanding of Bayesian statistics but also fostered a sense of community among his peers.
“Research is high risk, and failure is so common. We just need to be patient and keep doing the right thing.” His ability to navigate these challenges was reinforced by the support of his advisor and labmates, who shared their expertise and resources. He often participated in study groups and workshops, which provided him with additional perspectives and techniques that enriched his research.
“The study group was a great way to learn from each other and tackle complex problems together,” he said, emphasizing the importance of collaboration in research. "The collaborative spirit at Pitt was inspiring. It motivated me to push my boundaries and strive for excellence,” he reflected. “I learned how to manage my time effectively, balancing coursework, research, and other responsibilities.” The rigorous training he received at Pitt not only equipped him with technical skills but also instilled in him a strong work ethic and a passion for research. He often reminisces about the late nights spent in the lab, the lively discussions with peers, and the mentorship he received from faculty, all of which shaped his journey as a biostatistician.
After completing his PhD, Ma transitioned to a faculty position at the University of Maryland, joining the Department of Epidemiology and Biostatistics as assistant professor of biostatistics. He was recently promoted to tenured associate professor in the department and formed his own research lab focusing on developing timely statistical and machine learning methods for analyzing omics data and a number of "-omics" data integration problems to further our understanding of health and disease.
Ma's research focuses on developing novel, useful, and timely statistical methods and software in genetics and bioinformatics. His expertise lies in meta-analysis, data integration, Bayesian analysis, machine learning, and high-dimensional variable selection. His methods have wide application in neuroscience, cancer, and epidemiology fields. His experiences at Pitt laid a strong foundation for his current research endeavors, allowing him to approach complex problems with confidence and creativity.
When asked specifically what his plans for the future were, he stated, “To be honest, I do not have a very concrete goal for my academic career. As long as I can do the kind of research I like, which can also be valuable to society, help solve some pressing public health and medical issues in the real world, I feel pretty satisfactory. Also, instead of just being a statistician, I would hope I can gradually become a full scientist where I can solve a really tough scientific problem on my own instead of playing a supporting role for most of the time.”
Ma’s commitment to academia is driven by his desire to solve complex scientific problems and mentor the next generation of biostatisticians. He highlights the importance of developing grant writing skills, which he believes are crucial for success in academia. “In this big data and AI era, biostatisticians are playing a more critical role than ever before,” he noted, highlighting the evolving landscape of the field. He often shares his insights with students, encouraging them to embrace the challenges of research and to view setbacks as opportunities for growth.
Staying current with developments in biostatistics is important for Ma, who regularly engages with the latest literature, attends conferences, and collaborates with peers across the nation. “Biostatistics is a fast-evolving field, especially with the emergence of AI techniques. We need to stay updated and be willing to learn from various resources,” he advised. He encourages students to remain open-minded and explore various topics within the field, as this broad perspective can lead to unexpected opportunities and innovations. “Diversity in thought and approach is what drives progress in our field,” he emphasized.
Tianzhou Ma’s journey from the University of Pittsburgh to a prominent position in biostatistics displays the power of mentorship, collaboration, and resilience in our program. His experiences at Pitt not only laid the groundwork for his research but also inspired him to contribute to the field and mentor future generations. As he continues to navigate the complexities of biostatistics and public health, Ma remains committed to pushing the boundaries of knowledge and fostering a collaborative spirit in his work.
Calvin Dziewulski