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.
Portfolio Contents
- 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 the Portfolio Presentation Process 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
- 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
- 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)