1. Career Choices : Richness of Opportunities Nell Sedransk National Institute of Statistical Sciences North Carolina State University

2. Background  Academia – Mathematics, Statistics & BioEngineering Departments, Medicine and Cancer Centers  Industry – Engineering, R&D Training  Research Administration – NSF, NIST, NISS  Government Agency – BLS, NCES, NIH&NCI  Panels to Assess Research Directions &Proposals  - NIH, NIDR, NCI, NSF, BiNational RF

3. Contexts for Statistics  Academia – Teaching, Research, Collaboration; Self- selecting & Self-defining, Dissemination/Publication  Industry – Hard Problems in Design & in Analysis; Results Required, Implementation  Research Administration – Weighing Impact of Results, Weighing Impact of Application, Publication and/or Deliverables for Implementation  Government Agency – BLS, NCES, NIH&NCI, Relevance to Policy  Panels to Assess Research Directions &Proposals  - NIH, NIDR, NCI, NSF, BiNational RF, Technical Assessment of Potential for Significant Advances

4. Aspects of Statistics  Academia – Classrooms of Students, Self-defined Research Objectives  Industry – Applications needing Experiments – Designs, Analysis of Data (designed and observational), Algorithm construction and data representation  Research Administration – Large-scale projects, Opportunities to define statistical approaches  Government Agency – Data & Questions to answer from Data Bases, New Methodology for acquiring and organizing data (surveys) or modeling potential data  Research Enterprises – Survey construction and development, medical/laboratory record analysis

5. Personal Motivation  Statistical/Mathematical Impetus  Theoretical (abstract) Insight  Application requiring mathematical interpretation  Social science theory for relationships in data  Potential Impact of (applied field) results  Fascination with patterns in data  Computational tools  Ingenuity with data/relationship representations  Working Style  Individual  Collaborative

6. Example: New Kinds of Data  Trajectories: Speech Production Analysis –“Lights” affixed to Reference Points on Jaw –Repeated Syllables (consonant-vowel patterns; accented / unaccented) –Degree of Lisp  Light Tracings –Multiple Cameras and Sensors on Hand –Subject in Wheel-Chair –Extent of Controlled Motion

7. Example: Image Data  Pressure Patterns for Seated Wheel-Chair –Grid of Sensors on Wheel-Chair Pad (Multiple Properties) –Serial Multivariate Readings –Non-informative Regions –Detection of Acute Pressure Areas –Measures of Variability within Session  Averaging and Differencing Images –Distinguishing Variability from Significant Change –Characterizing Precursors to Deleterious Change

8. Criteria for Problems  Abstract View –Higher Dimensions, Fewer Constraints –Smallest Problem without Solution –Generalization of Solutions to Smaller Problems –New Context for Fundamental Formulation –Proof of Importance  Illustrative Example(s)  Comparison Studies (often Simulations)

9. Criteria for Problems  Concrete View –Motivating Context or Potential Impact - Example(s) –Comprehensive Problem Statement –Guarantee of Solution –Promise of Implementation –Match of Example with Statistical Formulation –Proof of Importance  Impact of Single-Case Solution  Transfer of Methodology for Repeated Application

10. Criteria for Solutions  Abstract View –Model Failures or Constraints –New Kinds of Data –Interweaving Deterministic and Stochastic Models –Communication for Complex or High Dimensional Models –Distances: Statistical Formulation to Application; Efficiency for Approximation –Proof of Importance  Example

11. Criteria for Solutions  Concrete View –Motivating Example(s) or Data Set(s) –Information (Precision) Requirement for Solution –Time Constraint for Solution –Cost Constraint for Solution –New Context for Fundamental Formulation –Proof of Importance  Impact of Solution to Scientific Problem  Transfer of Methodology for Repeated Application