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 (stat-msd@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 stat-msd@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.

Students will be evaluated by the faculty committee on the following: 

  • Achievement in core areas of statistical modeling, applied statistics and statistical computing;
  • Achievement in defining the ability to address and solve real-world problems with relevant statistical and computational methods;
  • Achievements in communicating in oral and written form with professional audience

Note that a student completing the MSS program have to satisfy all of the above 3 criteria at Satisfactory or Excellent level. A student will otherwise receive written feedback on those aspects marked Unsatisfactory, including comments on remedial paths recommended.

Select posters from Spring 2019 Portfolio Presentation

Portfolio Titles of the Spring 2019 Graduates

Exploring Bayesian TIme-Series Models with Financial Data
Effect of Democratic Campaign Spending on 2018 House Midterms
A Two -stage Labeling Framework for Effective Text Classification 
Extensions of Predictive Models
Bayesian Applications in Time Series 
Applied Machine Learning: Classification and Regression Examples
Comparing the Performance of DID and LDV in Different Scenarios
An R-based Prediction Tool for Optimizing Forecast
Applications of Sampling and Clustering Methods
Phase Transitions in Linear Models and DID Causal Inference Analysis
Community Detection Thresholds in Heterogeneous Graphs
Using Biclustering Methods to Classify High Dimensional Data
The Application of TVAR Method on Financial Data
Approaches to Data Visualization and Prediction: Healthcare to Art
Application of Statistical Methods on Financial and Medical Data
Machine Learning Models in Health Care
Time Series Model in Inventory Optimization Management
Unsupervised Exploratory Analysis Tool for Biclustering
The Yelp Restaurant Recommendation System
Prediction of Default Risks with Statistical Models
Machine Learning Application in Video Game Outcome Prediction

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)