James C. Spall
Principal Professional Staff
(JHU Applied Physics Laboratory)
Research
Professor
(JHU Department of Applied Mathematics and
Statistics)
Chair,
Applied and Computational Mathematics Program
(JHU Engineering for Professionals)
Co-Chair,
Data Science Program
(JHU Engineering for Professionals)
Offices
The
Applied
Physics Laboratory
11100
Johns Hopkins Rd.
Laurel,
Department
of Applied Mathematics and Statistics
The
Whitehead
Hall
Email: James Spall
Telephone: 443-778-4960 (at
JHU/APL)
Research
Interests and Professional Biography
Stochastic
systems; parameter estimation; stochastic optimization; Monte Carlo methods and
simulation; neural networks; control systems; mathematical modeling; system
identification and Kalman filtering; mathematical
statistics; optimization theory; uncertainty calculation; reliability analysis.
- Curriculum Vitae
(including list of publications)
- Article “James Spall: Finding Order
in the Random” that appears in profile of selected staff at JHU/APL
public website (June 2012).
Teaching
I teach
courses in the Department of Applied Mathematics and Statistics (AMS), the
Applied and Computational Mathematics (ACM) Program (within JHU Engineering for
Professionals), and the JHU/APL Strategic Education Program (non-credit courses
for JHU/APL staff members). Selected recent credit courses in AMS are listed
below:
- 553.633,
Monte Carlo Methods
- 550.636,
System Identification and Likelihood Methods
- 550.663,
Stochastic Search and Optimization
- 550.664,
Modeling, Simulation, and Monte Carlo
- 550.690,
Neural Networks and Feedback Control Systems
Books
and Related Web Sites
Books
Spall, J. C. (2003), Introduction to
Stochastic Search and Optimization: Estimation, Simulation, and Control,
Wiley,
Spall, J. C. (2017), “Solutions Manual
for Introduction to Stochastic Search and Optimization: Estimation, Simulation,
and Control ” (235 pages).
Spall, J. C. (editor and
coauthor) (1988), Bayesian Analysis of Time Series and Dynamic Models,
Marcel Dekker, New York (576 pages).
Web site for simultaneous perturbation
stochastic approximation (SPSA) algorithm: