Statistics PhD candidate · UMass Amherst

Turning complex data into evidence people can use.

I develop statistical and machine-learning workflows for wearable sensors, population health, and high-dimensional systems—with equal attention to methodological rigor and practical impact.

665K
GPU job logs analyzed
7
Peer-reviewed publications
3.98
PhD GPA
Christopher Odoom
Current focus Deep-learning-based sedentary behavior estimation
Statistical inference Machine learning Wearable-sensor analytics Public health Complex survey analysis R · Python · SQL

01 / About

Statistics grounded in the real world.

My work sits at the intersection of statistical methodology, machine learning, and public-health research.

I am a Statistics PhD candidate at the University of Massachusetts Amherst and a research assistant with UC San Diego’s ADALab. There, I’m translating a deep-learning posture model from Python into R and testing it on NHANES accelerometer data—down to matching timestamps, labels, and predicted probabilities.

My current research uses deep learning and complex-survey methods to estimate sedentary behavior from NHANES wrist accelerometry. I also work on nonparametric derivative estimation with penalized B-splines and applied problems spanning health, insurance, and research computing.

Methods

GAMs, GLM/GLMM, survey inference, penalized splines, survival analysis

ML

Deep learning, XGBoost, LightGBM, CatBoost, cross-validation, Optuna

Toolkit

R, Python, SQL, Git, Slurm, Jupyter, R Markdown, LaTeX

02 / Selected work

From wearable data to GPU clusters.

All GitHub repositories
02
Python · HPC analytics2025

Unity GPU Efficiency Analytics

Led a four-person team and designed rate- and duration-normalized efficiency metrics across 665,000 cluster job logs to inform research-computing decisions.

03
Python · LightGBM · XGBoost2026

Policy Retention Modeling

An end-to-end insurance cancellation workflow combining policy history with FEMA, RUCA, and NIBRS context features, year-aware validation, and Optuna tuning.

04
R package · Data engineering2026

BikeRentalData

An R package for discovering, downloading, and standardizing official trip data across four U.S. bike-share systems, with calendar, weather, and infrastructure enrichment.

05
Flask · R · Statistical guidance2024—2025

Statistical Model Suggester

A web application that translates a user’s research problem into suitable model classes using structured, rule-based statistical guidance.

06
R · GBD 2023 · Forecasting2026

Diabetes Burden in Sub-Saharan Africa

Regional comparisons, burden decomposition, mortality-to-disability patterns, and forecasts through 2030 using the latest Global Burden of Disease data.

07
Python · Time series · Forecasting2026

TB in Ghana Forecasting

A forecasting pipeline comparing classical and machine-learning models to project tuberculosis incidence, mortality, and mortality-to-incidence ratios through 2030.

03 / Experience

Research, leadership, and teaching.

Building sound methods is only part of the job. I also care about clear communication, thoughtful collaboration, and tools that others can reuse.

Apr—Sep 2026

ADALab · UC San Diego

Research Assistant

Porting CNN/BLSTM posture-classification inference from Python to R and validating prediction equivalence on NHANES accelerometer data.

Fall 2026

UMass Amherst

Incoming Primary Instructor · STAT 111

Designing and teaching an introductory statistics course, including the full curriculum, assessments, office hours, and student feedback.

Jun—Aug 2025

Center for Data Science & AI · UMass Amherst

Project Manager

Led GPU-efficiency analytics from metric design through automated stakeholder reporting and research-computing recommendations.

2022—2026

Department of Mathematics & Statistics · UMass Amherst

Researcher, Statistical Consultant & R Tutor

Worked across nonparametric research, study design, reproducible modeling, technical recommendations, and R instruction.

05 / Connect

Have a question worth exploring?

I welcome conversations about research collaborations, statistical consulting, public-health analytics, and applied data-science work.

codoom@umass.edu