Hanxi (Mark) Chen
2024 Toffler Scholar | Master’s Student in Biostatistics, University of California, Los Angeles
Biography
Hanxi Chen’s interest in science began not just with equations or code, but with a genuine desire to make a social impact. Born in a small town in China and raised across multiple cities, Chen grew up observing how social exclusion, access to healthcare, and structural inequality shaped lives. His early awareness aims to inspire respect for his commitment to social change.
“My motivation started with a desire to help people who are vulnerable,” Chen says. “I saw early on how much circumstances can shape health.”
His mother, a teacher, played a formative role in nurturing that awareness. She modeled empathy, responsibility, and attentiveness to others, instilling in Chen a sense that learning carried social purpose. At the same time, Chen developed a fascination with healthcare systems themselves. As a middle school student, he volunteered at a hospital in China. This experience lasted more than three years through high school.
Inside the hospital, Chen saw order and precision. Processes moved efficiently. Data flowed continuously. He noticed that clinical decisions depended not only on physicians but also on information systems that organized patient histories, test results, and outcomes.
“That was when data started to feel real to me,” he explains. “It wasn’t abstract. It represented people’s lives.”
Biography
Hanxi Chen’s interest in science began not just with equations or code, but with a genuine desire to make a social impact. Born in a small town in China and raised across multiple cities, Chen grew up observing how social exclusion, access to healthcare, and structural inequality shaped lives. His early awareness aims to inspire respect for his commitment to social change.
“My motivation started with a desire to help people who are vulnerable,” Chen says. “I saw early on how much circumstances can shape health.”
His mother, a teacher, played a formative role in nurturing that awareness. She modeled empathy, responsibility, and attentiveness to others, instilling in Chen a sense that learning carried social purpose. At the same time, Chen developed a fascination with healthcare systems themselves. As a middle school student, he volunteered at a hospital in China. This experience lasted more than three years through high school.
Inside the hospital, Chen saw order and precision. Processes moved efficiently. Data flowed continuously. He noticed that clinical decisions depended not only on physicians but also on information systems that organized patient histories, test results, and outcomes.
“That was when data started to feel real to me,” he explains. “It wasn’t abstract. It represented people’s lives.”
Research Focus
During those volunteer years, Chen began to understand that data could serve as a bridge between lived experience and better decision-making. That realization shaped his academic path. Before beginning undergraduate study, he spoke with professors about preparing for a future in biostatistics. Their advice was consistent. Strong foundations in mathematics and statistics would be essential.
Chen entered the University of British Columbia in Canada to pursue a bachelor’s degree in statistics. Moving across continents alone at a young age required adaptability, but his upbringing had prepared him well. He had already learned how to adjust to new cities, cultures, and expectations.
At UBC, Chen explored diverse biomedical data applications, notably contributing to a project analyzing schizophrenia data with multivariate methods. His work aimed to uncover hidden brain-behavior relationships, inspiring confidence in his research potential.
Chen contributed by translating existing MATLAB code into Python and implementing the full analytical pipeline. Through that work, he gained practical experience transforming theoretical methods into usable tools. He saw how statistical structure could illuminate complex neuropsychiatric phenomena.
“Schizophrenia is still poorly understood. Data-driven approaches can help us see patterns that aren’t obvious otherwise.”
- Hanxi (Mark) Chen
The experience reinforced his interest in neuroscience and mental health, while also shaping his methodological identity. He became drawn to problems where heterogeneity and complexity challenge traditional analytical approaches.
After completing his undergraduate degree, Chen moved to Los Angeles to begin a master’s program in biostatistics at UCLA. There, he focused on coursework while refining his research interests and developing a clearer sense of direction. The program allowed him to strengthen his statistical foundations while exploring applications in neurological and neurodevelopmental conditions.
As a first-year master’s student, Chen began working on a research project centered on autism spectrum disorder. Autism affects approximately one in 36 children in the United States and presents with extraordinary heterogeneity. Individuals differ widely in communication, sensory processing, behavior, and functional needs. Despite its prevalence, there are no FDA-approved pharmacological treatments for its core symptoms to date.
Chen’s work focuses on understanding heterogeneity using objective data. He analyzes electroencephalography recordings to examine how patterns of brain activity relate to behavioral characteristics across individuals with autism. Rather than treating autism as a single condition, his approach aims to identify meaningful subgroups within the spectrum.
“Autism is not one thing,” Chen explains. “If we want precision in treatment, we need precision in understanding.”
His methodology combines semiparametric functional-on-scalar regression with functional principal component analysis. This framework allows him to model how EEG signals vary across frequency and individuals, identify structured patterns, and explore which aspects of brain activity existing behavioral measures explain and which remain unexplained.
That distinction matters. Patterns not explained by current behavioral classifications could eventually point to new biological subtypes or overlooked dimensions of neurodevelopment.
Chen works with EEG datasets that include both neural signals and behavioral assessments. These datasets pose significant challenges. Pediatric EEG recordings often contain motion artifacts and noise. Cleaning and processing them require specialized tools, computational expertise, and, sometimes, collaboration with external laboratories.
The Toffler Scholar Award plays a critical role at this stage. The funding supports Chen’s access to specialized resources, facilitates collaboration, and enables follow-up analyses when early results reveal unexplained structure.
“This support allows me to keep going when the data gets hard,” Chen says. “It lets me ask the next question instead of stopping at the first result.”
Looking ahead, Chen plans to pursue a PhD in biostatistics to develop methods that better capture complexity in neurological and neurodevelopmental data, especially in conditions like autism, schizophrenia, and other brain disorders.
He sees methodological rigor as inseparable from human impact. Better statistical tools ensure that healthcare reflects diverse experiences rather than averaging them away, leading to fairer and more effective care.
For Chen, the work remains grounded in purpose. It reflects a journey shaped by empathy, persistence, and a belief that data, when handled thoughtfully, can help bridge gaps in understanding and care.
“If we want to help people,” he says, “we have to see them clearly first.”