2020-2021 Graduate Catalog [ARCHIVED CATALOG]
Statistics (STAT)
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BISWAS, ANIMIKH, Department Chair
HUANG, YI, Graduate Program Director, Statistics
M.S., Ph.D. (Degree Types )
Faculty
Professors
ARMSTRONG, THOMAS E., Ph.D., Princeton University; Functional analysis and measure theory, probability, mathematical economics
GOBBERT, MATTHIAS K., Ph.D., Arizona State University; Computational and industrial mathematics, scientific computing
GOWDA, MUDDAPPA S., Ph.D., University of Wisconsin; Applied analysis, optimization
GULER, OSMAN K., Ph.D., University of Chicago; Convex programming, computational complexity, interior point methods
HOFFMAN, KATHLEEN, Ph.D., University of Maryland, College Park; Calculus of variations, differential equations, mathematical biology, coupled oscillator theory
KOGAN, JACOB, Ph.D., Weizmann Institute, Israel; Calculus of variations, optimal control theory, optimization
LO, JAMES T., Ph.D., University of Southern California; Computational intelligence, intelligent systems, neural networks approach to systems control and signal processing, stochastic systems
LYNN, YEN-MOW, Ph.D., California Institute of Technology; Fluid dynamics, mathematical physics
MATHEW, THOMAS, Ph.D., Indian Statistical Institute, India; Inference in linear models and variance component models, design of experiments
NEERCHAL, NAGARAJ K., Ph.D., Iowa State University; Time series analysis, over-dispersion models, environmental statistics, data analysis
POTRA, FLORIAN, Ph.D., University of Bucharest, Romania; Numerical optimization, mathematical modeling, parallel computing
RATHINAM, MURUHAN, Ph.D., California Institute of Technology; Stochastic dynamics, non-linear dynamics, geometric control theory
ROSTAMIAN, ROUBEN, Ph.D., Brown University; Differential equations, mathematical modeling, mechanics
ROY, ANINDYA, Ph.D., Iowa State University; Bayesian Analysis, Bioinformatics, Brain Imaging, Sequential Design, Time Series and Signal Processing
SEIDMAN, THOMAS I., Ph.D., New York University; Control theory, non-linear partial differential equations, inverse problems
SINHA, BIMAL K., Ph.D., University of Calcutta, India; Multi-variate analysis, statistical inference, linear models, decision theory, robustness, asymptotic theory
SURI, MANIL, Ph.D., Carnegie Mellon University; Numerical analysis, partial differential equations
Associate Professors
BISWAS, ANIMIKH, Ph.D., Indiana University; Nonlinear PDE and Fluid Dynamics, Functional Analysis and Applications
DRAGANESCU, ANDREI, Ph.D., University of Chicago; Numerical analysis of partial differential equations, multi-level algorithms for inverse problems
PARK, JUNYONG, Ph.D., Purdue University; High-dimensional data analysis, classification, asymptotic theory
PEERCY, BRAD, Ph.D., University of Utah; Mathematical biology, partial differential equations, bifurcation theory, dynamical systems.
SHEN, JINGLAI, Ph.D., University of Michigan; Differential equations and dynamical systems; dynamic optimization; control theory; variational integrators with applications in operations research, mechanics and engineering
Assistant Professors
ADRAGNI, KOFI, Ph.D., University of Minnesota; Reduction of dimensionality, regression modeling and diagnosis, multivariate analysis, computational statistics, bioinformatics
HUANG, YI, Ph.D., Johns Hopkins University; Average treatment/exposure effect evaluation, biostatistical methods and applications in biomedical, public health and policy research
KANG, HYE-WON, PH.D., University of Wisconsin, Madison; Stochastic modeling, analysis, and simulation of biological systems, biochemical networks, and cancer signaling pathways
KANG, WEINING, PH.D., University of California, San Diego; Probability theory, stochastic processes, and their applications.
MALINOVSKY, YAAKOV, Ph.D., Hebrew University of Jerusalem; Design theory, stochastic ordering, sampling, group testing, nonparametric method
PARK, DOHWAN, Ph.D. University of Missouri, Columbia; Longitudinal data analysis, survival analysis, and biostatistics.
SOUSEDÍK, BEDŘICH, Ph.D., Czech Technical University in Prague, Ph.D., University of Colorado Denver: Computational mathematics, numerical analysis, scientific computing, uncertainty quantification, stochastic finite elements, domain decomposition, multigrid
Professors Emeritus
AZIZ, A. KADIR, Ph.D., University of Maryland, College Park; Functional and numerical analysis, control theory for partial differential equations
BELL, JONATHAN, Ph.D., University of California, Los Angeles; mathematical biology, mathematical finance, partial differential equations, applied math.
BHATIA, NAM, Ph.D., Technische Universität Dresden; Dynamical systems, chaos, bifurcation, mathematics education
GROSS, FRED, Ph.D., University of California, Los Angeles; Functional equations, complex function theory, meromorphic functions
PITTENGER, ARTHUR, Ph.D., Stanford University; General Markov processes, probability theory, quantum computational algorithms
RUKHIN, ANDREW, Ph.D., Steklov Institute of Mathematics; Statistical decision theory, adaptation, admissibility, Bayesian analysis, change-point estimation
Program Description
The Department of Mathematics and Statistics offers graduate programs leading to the M.S. and Ph.D. degrees in both applied mathematics and statistics. The department has had an active graduate program in applied mathematics since 1970. It expanded to include a full graduate program in statistics in 1984. The strength of these programs lies in its graduate faculty, who are actively engaged in research in applications of mathematics and statistics in a wide variety of real-world problems, as well as in investigations of fundamental and theoretical questions. The faculty designs and implements courses and curricula with emphasis on innovative research directed toward practical applications, as mandated by the charter from the University System of Maryland Board of Regents.
Both the applied mathematics and statistics programs are intended for those students who are interested in pursuing an advanced degree and who have earned the equivalent of a bachelor’s or master’s degree in mathematics, statistics or in other mathematically oriented disciplines. Students who already hold a master’s degree may apply and enter the doctoral program directly. The doctoral programs provide training suitable for employment in academia, industrial research and development organizations, as well as research-oriented government agencies. The master’s degree programs provide training in applications of mathematics and statistics in areas suitable for employment in industry or government agencies. They also can serve as preparatory steps toward advancing to a doctoral program.
To serve students’ varying range of backgrounds and goals, the department has instituted several tracks within its master’s degree programs. Each track defines a set of well-focused graduation requirements. Students who intend to continue to the doctoral programs should consider the traditional tracks in applied mathematics or statistics. A student whose final goal is a master’s degree should consider the industrial track in applied mathematics or the applications-oriented track in statistics. Most graduate courses are offered in the late afternoon or in the evening to enable the participation of those who hold full-time employment.
Individuals wishing to benefit from the department’s course offerings without enrolling as degree-seeking students may do so by filing a non-degree seeking student application form. For students who do not already hold an undergraduate degree, a combined B.S./M.S. program leading to a master’s degree in either applied mathematics or statistics is also offered by the department.
The department offers doctoral study in a broad spectrum of both classical and modern applied mathematics and statistics. Admission to this program presupposes a strong background in mathematics and/or statistics. Doctoral students continue with advanced study and dissertation research, with specialization in any of the departmental fields or in an interdisciplinary area. Particular emphasis is given to the following areas in statistics: Bayesian analysis, biostatistics, data mining, design of experiments, environmental statistics, nonparametric statistics, reliability, spatial statistics and image analysis, statistical decision theory and inference, survival analysis, time series analysis.
Program Admission Requirements
Applications for the master’s and the doctoral programs must be made on prescribed forms, obtainable from the Graduate School’s Web site, www.gradschool.umbc.edu/admissions. Admission to the two programs, applied mathematics and statistics, are made separately and are not interchangeable. The completed application packet must include: (a) completed application forms; (b) transcripts of college and university academic records; (c) three recommendation letters; (d) Graduate Record Examination (GRE) scores for the General Test; and (e) TOEFL score for international applicants whose native language is not English. All original application documents must be sent directly to the Graduate School, not the department. Requests for exemption from submitting some of the required documents may be considered if reasonable justification is given. Criteria for admission include:
- A bachelor’s degree with a grade point average higher than 3.0 out of 4.0
- A sound background in mathematics and/or statistics
- A GRE score in the 85th percentile in the quantitative section
- A TOEFL score of at least 600 (paper-based test) or 250 (computer-based test) or 100 (iBT) for international students
Applicants whose backgrounds are considered deficient occasionally may be admitted on provisional basis. In such cases, the department specifies conditions in the form of courses to be completed in a specified time before a recommendation for change to regular status is made.
Facilities and Special Resources
The department’s focus on applied aspects of mathematics and statistics calls for state-of-the-art computing facilities. The students and faculty have easy access both to the university’s central computing facilities and the extensive departmental network of computers and workstations. Essentially all of the department’s computing is done on Linux workstations. Several Linux Beowulf clusters enable parallel processing of computationally intensive tasks. The department has its own full-time systems administrator, who oversees the installation and maintenance of hardware and software and manages the operation of department’s graduate student computer laboratory. The department and the university maintain site licenses for Maple, Mathematica, MATLAB, Femlab, SAS, S-Plus and other software of interest to mathematicians and statisticians.
Financial Assistance
Teaching and research assistantships are available on a competitive basis. Students also have the opportunity to obtain summer teaching at the university and summer internships in industry and government agencies.
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