🚨 We are looking for motivated PhD, master’s and undergraduate students to work on statistical and machine learning methods for analyzing data from wearables ⌚️, smartphones 📱, and contextual location information 📍.
Our work sits at the intersection of statistical theory, computation, and real health problems. A typical project starts from a concrete question — does outdoor temperature shift mood differently across mood-disorder subtypes? can we recover shared rhythms from a smartwatch, a sleep diary, and a glucose monitor at once? — and ends with a new model, a proof of why it works, software that others can use, and an answer for the clinicians and epidemiologists we work with.
Prospective PhD students should apply to the Texas A&M Statistics PhD program and mention an interest in the CADENCE Lab in the statement of purpose. Current Texas A&M Statistics PhD students who would like to explore research with the group are welcome to e-mail Debangan directly with a CV and a short note about what interests you.
We look for strong foundations in probability and statistical inference, comfort with programming (R and/or Python; Julia or C++ are a plus), and curiosity about how people’s health unfolds over days, weeks and seasons. Background in functional data analysis, spatial statistics, copulas, Bayesian computation, or mobile-health data is helpful but not required.
Texas A&M undergraduate and master’s students interested in statistics, data science, or public health can join the lab for course credit, as part of a research experience, or for a master’s project or thesis. Projects typically involve cleaning and visualizing wearable and survey data, building reproducible analysis pipelines in R, and contributing to the lab’s open-source packages (M²FPCA, SGCTools). E-mail Debangan with your transcript, a CV, and the semester you would like to start.
Debangan Dey · debangan@tamu.edu Department of Statistics, Texas A&M University, College Station, TX