Portfolio of Work

As part of the Completion Exercise for M.S. students, you may present and defend a Portfolio of Work that demonstrates mastery of statistical methods, application and computation.

THE PORTFOLIO PRESENTATIONS ARE SCHEDULED DURING THE LAST FRIDAY OF MARCH ANNUALLY FROM 2:00PM to 4:00PM.  THE PRESENTATIONS WILL BE FOLLOWED BY A RECEPTION. ALL MSS GRADUATE STUDENTS (FIRST AND SECOND YEAR) ARE INVITED TO THE RECEPTION. 

The Portfolio consists of:

  • Poster: Each student will create a Poster that they must present to a committee of three faculty members from the Department and includes material from two different projects (that may or may not be related);
  • A portfolio title that should be submitted to the MSD prior to your presentation (msd@stat.duke.edu)
  • A written description of one of the projects on your poster, including a discussion of how the experience relates to your field and a summary of what was learned (to MSD at msd@stat.duke.edu), along with copies of any non-proprietary documents or presentations you created during the internship period;
  • Any material you created as a research or teaching assistant;
  • Curriculum vitae (bring a current copy of your CV to your presentation and give it to your committee).

All students choosing a Portfolio of Work should follow the steps outlined in this document.

Select posters from Spring 2018 Portfolio Presentation

Example Poster 1

Example Poster 2

Example Poster 3

Portfolio Titles of the Spring 2018 Graduates

Statistical Modeling and Insights in Financial Industry
Trends in Balloon Catheter Dilation of Paranasal Sinuses
Inferring Drug Innovation with Adverse Events 
Machine Learning Methods for Spatial and Financial Applications
Applied Bayesian Methods for Text Mining
Dynamic Factor Analysis in Internet Search Volume and Stock Volatility 
Comparing  Model-based Ranking Methods to Evaluate Physicians and Hospitals
Prediction of Medication Non-adherence with Clinical Notes
Evaluating Performance of Hospitals and Physicians using a Binomial Generalized Linear Mixed Model 
Text Analysis and Other Exploration
Deep Learning for the Automatic Grading of Diabetic Retinopathy 
Modeling Economic and Political Dynamics in the Middle East.
Python Implementation of Bayesian Hierarchical Clustering
Implementation and Applications of Bayesian Hierarchical Clustering
Multi-Scale Topological Data Analysis to Identify Brain Fiber Connectivity for Biological Systems Applications
Bayesian Approach on Correcting Model Performance given Biased Estimates of Feature Values
Predicting Patient Admissions in the Medicare Shared Savings Program
Comparison of Machine Learning Methods in the Estimation of Housing Prices
Evaluating the Performance of a Generalized Recommendation Engine for the Financial Services Industry
Predictive Analytics in Healthcare and Medical Data Exploration
Establishing a Realistic Prior Model for Complex Geometrical Objects
Graph-Coupled HMMs and Deep Neural Network for Modeling Infection and Medical Diagnosis
Empirical Study of Topic Modeling in Movie Recommendation
Statistical Modeling and Traffic Violation Analysis
News' Predictive Power on St. Louis Fed Financial Stress Index
Application of Neural Networks with Joint Embedding for Medical Document Classification
Analysis and Implementation of Classification Algorithms (Kmeans + +, CONCOR)