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) |