Career Choices : Richness of Opportunities

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