Why did you choose the Yale School of Public Health?
I chose YSPH because it is an incredibly resourceful, transparent, and pioneering institution, especially where students can engage in paradigm-shifting work and collaborate to shape the future of public health. This future involves integrating frontier trends such as artificial intelligence, digitalization, and the automation of civilization. More importantly, YSPH offers the opportunity to work with faculty who perfectly align with my research interests in causal inference, enabling me to cultivate my research mindset. It fosters my growth both as a professional bioscientist with strong interdisciplinary collaboration skills and as a methodology researcher, advancing innovative methods to promote health research framework.
What were you doing before enrolling at YSPH?
Before joining YSPH, my undergraduate research primarily focused on the philosophy of science, statistical inference, and computational cognitive science, where I explored the causal inference paradigm that underlies the framing of knowledge and decision-making processes through both statistical and philosophical perspectives. My blueprint was centered on revolutionizing the foundation of civilization by reverse-engineering human cognition and incorporating systematic biases related to the physiological structure.
Beyond academia, I founded my own research institute and affiliated think tank to rapidly build a network across various industries and develop a talent framework. This initiative helped me strategically integrate resources from academia, industry, and research, forming a solid foundation for my early career and supporting my professional development at YSPH.
One of my favorite aspects of the YSPH academic program is the outstanding faculty, who are pioneering leaders in their respective fields.
Yukang Zeng
What are your favorite aspects of the YSPH academic program?
One of my favorite aspects of the YSPH academic program is the outstanding faculty, who are pioneering leaders in their respective fields. They not only bring the latest industry insights into the class but are also very open and willing to involve students in their research, encouraging rapid exploration and trial-and-error to help students find their career paths.
Another key aspect is the program’s three distinct pathways, which align with future job market trends in public health. These include the foundational Implementation Science, the innovative Data Science pathway that integrates cutting-edge data science methods, and the Standard Pathway, which offers students the freedom to explore interdisciplinary directions. These pathways provide a solid foundation for any career in the healthcare sector, allowing students to select courses that best support their career development.
Finally, the program features an excellent teaching assistant system, where senior master’s and PhD students provide first-year students with valuable opportunities to engage with them. This creates a highly supportive system not only for academic collaboration, but also for career development, giving students access to valuable mentorship and guidance from more experienced peers.
What was your most impactful experience outside of class?
One of the most impactful experiences outside of class has been my academic collaboration with Professor Fan Li and Professor Guangyu Tong in the field of Causal Inference. Their mentorship allowed me to engage deeply with both methodological innovation and practical applications, training me not only in statistical methodologies, but also in the ability to abstract problems from real-world data. This collaboration sharpened my insights into implementation science and problem discovery, helping me cultivate my academic mindset and research taste early on.
More recently, in collaboration with Professor Guangyu Tong at the Cardiovascular Medicine Analytics Center (CMAC), we have explored the application of Bayesian Learning to detect heterogeneous treatment effects in heart failure through various innovative approaches, like integrating with mediation causal inference methods to uncover causal patterns. This precious interdisciplinary research experience has had a profound impact on my academic development and enhanced my ability to innovate in applied research.
Do you have a favorite Yale place or New Haven food?
My favorite place at Yale is Tsai City, the university’s innovation and entrepreneurship center. It’s a dynamic space where students from diverse professional backgrounds come together with their academic innovations or entrepreneurial ideas. The environment is full of opportunities for pitching ideas, interdisciplinary collaboration with co-founders, product-oriented research, and accessing highly supportive mentorship. Tsai City also hosts many innovative and critical thinking lectures, which constantly push the boundaries of understanding and encourage creative problem-solving. Students rapidly iterate their products and research outcomes, making the environment both fast-paced and deeply enriching.
What do you hope to do after graduation?
After graduation, I will follow my blueprint and pursue a PhD to continue focusing on my research interest in the foundations of human cognition and the systematic biases that shape our understanding of the world. The interdisciplinary, multi-omics, and multi-systemic research skills I’ve gained at YSPH, combined with the methodology approaches I learned through collaborating with Fan and Guangyu on causal inference research, have provided me with the perfect foundation. These experiences will enable me to push forward my lifelong research goals and bring my blueprint to life by addressing some of the most challenging questions about human cognition and its impact on scientific discovery.