Join Po-Ling Loh, an American theoretical statistician at the University of Cambridge, as she shares her fascinating journey from childhood in Wisconsin to mastering higher dimensional statistics and differential privacy. She delves into the playful influence of her math-loving family and the challenges faced during her PhD at Berkeley. Loh provides insights on the cultural differences in academia between the US and UK while emphasizing the importance of mentorship in statistics. Plus, she shares humorous tales from her transition to life in the UK.
Po-Ling Loh's childhood, immersed in a mathematically rich environment, significantly influenced her eventual career in theoretical statistics.
Her transition from pure mathematics to statistics involved grappling with the dynamic nature of statistical problems and their relevance to real-world data privacy.
Deep dives
Early Life and Family Influence
Po Ling Lo shares her upbringing in a family deeply rooted in mathematics, with both parents holding degrees in the field. Residing in Madison, Wisconsin, her home was structured around a strong emphasis on math learning, evidenced by her mother's zealous efforts to ensure her children excelled. Childhood moments included playful activities like counting crayons and practicing money exchange to instill a foundational understanding of numbers. This nurturing environment cultivated a comfort with mathematics that significantly shaped her future academic endeavors.
Academic Journey and Evolving Interests
Initially exploring interests beyond math, Lo briefly flirted with biology and American history during her middle school and high school years. However, her older brother's passion for competitive math drew her back to the subject, ultimately guiding her towards a focus on mathematics in college. At Caltech, she found herself excelling in mathematics but experienced a turning point when faced with the challenges of advanced abstract algebra. This hurdle redirected her academic path towards statistics, aligning her coursework with her growing recognition of her true strengths.
Transition to Statistics and Its Nuances
During her transition from pure mathematics to statistics, Lo encountered a significant adjustment period, particularly in defining the right questions and handling statistical ambiguity. Unlike pure math, statistics involves a dynamic process of refining problems and adapting methodologies, which was initially challenging for her to grasp. Her unique background in rigorous mathematics provided her with a distinctive perspective, enabling her to navigate this shift more effectively. Through mentoring students, she emphasizes that understanding the evolving nature of statistical questions is crucial for success in the field.
Current Research and Interests
Now focused on advanced topics like high-dimensional statistics and differential privacy, Lo's research addresses challenges posed by massive datasets with many variables. She highlights the significance of differential privacy for ensuring individuals' data protection while enabling data analysis, an approach adopted by major tech companies. The connection between robust statistics and differential privacy piqued her interest, guiding her to explore how the methods used in robust statistics could enhance privacy measures. This intersection of theory and application continues to inspire her work, stimulating further inquiries in modern statistical practices.
Po-Ling Loh is an American theoretical statistician based at the University of Cambridge. She discusses her childhood, choosing a university, and her path to higher dimensional statistics and differential privacy.
She also compares life as an academic in the US and UK.
This episode was made possible by the Leverhulme Trust, a UK-based organisation which funds ambitious blue skies research across various disciplines - https://www.leverhulme.ac.uk/