Department of Mathematical Sciences 40 Faculty 41 Graduate

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Department of Mathematical Sciences

Department of Mathematical Sciences

 • 40 Faculty • 41 Graduate Students • Approximately 80 Undergraduate Students

• 40 Faculty • 41 Graduate Students • Approximately 80 Undergraduate Students

Research Areas • Applied Mathematics • Statistics • Combinatorics and Pure Math • Mathematics

Research Areas • Applied Mathematics • Statistics • Combinatorics and Pure Math • Mathematics Education

Applied Mathematics – Computational Engine Research – F. Tanner – Simulation of Food Sprays

Applied Mathematics – Computational Engine Research – F. Tanner – Simulation of Food Sprays – F. Tanner – Multiphase Fluid Systems – K. Feigl – Cardiac Dynamics – W. Ying – Computational Biology – L. Zhang March 2008 Computing Initiative

 • Computational Engine Research • Modeling of flow, spray and combustion processes Prof.

• Computational Engine Research • Modeling of flow, spray and combustion processes Prof. Franz Tanner

Computational Engine Research • Motivation – Health and Environmental – Sustainability • Main Objectives

Computational Engine Research • Motivation – Health and Environmental – Sustainability • Main Objectives – Understand physical processes – Develop simulation tools • Results – Strategy to minimize fuel consumption and emissions – Multi-orifice asynchronous injection Mass fraction of an evaporating fuel spray

Modeling of Food Sprays • Motivation – Spray-drying and spray-freezing – Encapsulation of nutrients

Modeling of Food Sprays • Motivation – Spray-drying and spray-freezing – Encapsulation of nutrients • Main Objectives – Obtain desired drop size distributions – Maximize production • Modeling Challenges/Research – Complex flows and materials – Phase changes Air-assisted atomization of a nutriose liquid spray

 • Simulation of flow of complex fluids • Collaborations with ETH-Zurich and University

• Simulation of flow of complex fluids • Collaborations with ETH-Zurich and University of Tennessee Prof. Kathleen Feigl

Simulation of Fluid Systems • Examples/Applications – Emulsions, foams, polymer blends – Foods, plastics,

Simulation of Fluid Systems • Examples/Applications – Emulsions, foams, polymer blends – Foods, plastics, pharmaceuticals • Goals – Understand processmicrostructure- rheology relationship – Design processes to optimize product properties • Research Simulated deformation of a fluid droplet – Multidisciplinary approach – Combine and March 2008 modeling, simulation Computing Initiative experiments

Simulation of Fluid Systems Droplet deforming in supercritical shear flow Droplet deforming in supercritical

Simulation of Fluid Systems Droplet deforming in supercritical shear flow Droplet deforming in supercritical elongational flow

 • Ph. D. – Duke • Joined MTU Fall 2008 • Research Interests

• Ph. D. – Duke • Joined MTU Fall 2008 • Research Interests – – Scientific Computing Modeling/Simulation Mathematical Biology CFD Wenjun Ying, Asst. Prof.

Simulation of Cardiac Dynamics • Space-time adaptive mesh refinement • Multi-scale adaptive modeling of

Simulation of Cardiac Dynamics • Space-time adaptive mesh refinement • Multi-scale adaptive modeling of electrical dynamics in the heart Simulation of wave propagation in a virtual dog heart

Cartesian Grid Method • Beating heart • Droplet deformation • Multiphase flows • Other

Cartesian Grid Method • Beating heart • Droplet deformation • Multiphase flows • Other free-boundary or moving interface problems Grid lines not aligned with complex domain boundary

 • Ph. D. – Louisiana Tech • Post-doc – Harvard/MIT • Joined MTU

• Ph. D. – Louisiana Tech • Post-doc – Harvard/MIT • Joined MTU Fall 2008 • Research Interests – Computational biology – Cluster and classification algorithms – Software application development Le (Adam) Zhang, Asst. Prof.

Simulation of Brain Cancer Progression Brain Cancer Cell • Performing multi-scale, multi-resolution hybrid cancer

Simulation of Brain Cancer Progression Brain Cancer Cell • Performing multi-scale, multi-resolution hybrid cancer modelling • Regression analysis, multivariate analysis Simulation of Cancer Progression

Simulation of Hyperthermia in Skin Cancer Treatment Skin Cell Structure • Simulate bio-heat transfer

Simulation of Hyperthermia in Skin Cancer Treatment Skin Cell Structure • Simulate bio-heat transfer by finite difference method • Inverse heat convection problem Treatment Simulation

Statistics – Statistical Genetics – Q. Sha, R. Jiang, J. Dong, S. Zhang, H.

Statistics – Statistical Genetics – Q. Sha, R. Jiang, J. Dong, S. Zhang, H. Chen – Wildlife Population Studies – T. Drummer – Statistics , Probability, Optimization – I. Pinelis – Statistical Methodolgy and Data Analysis – Y. Munoz –Maldonado March 2008 Computing Initiative

 • Population studies for moose, wolves and sharp-tail grouse in U. P. •

• Population studies for moose, wolves and sharp-tail grouse in U. P. • Aerial Observation Prof. Tom Drummer

 • Moose survey conducted at 500 ft altitude over 1600 sq. mile area

• Moose survey conducted at 500 ft altitude over 1600 sq. mile area • Model developed to yield probability of sighting animals

 • Ph. D. – Texas A&M University • Statistical Methodology and Analysis of

• Ph. D. – Texas A&M University • Statistical Methodology and Analysis of Data – – Functional Data Analysis Non parametric Methods Linear and Mixed Models Multivariate Analysis Yolanda Munoz-Maldonado, Asst. Prof.

 • Ganglioside Profiles Analysis • Detect differences in brains of young and old

• Ganglioside Profiles Analysis • Detect differences in brains of young and old rats • Differences found in locus coeruleus of young rats which may affect sleep regulation

 • Study of effect of chronic exposure to particulate matter on mortality •

• Study of effect of chronic exposure to particulate matter on mortality • Temporal analysis of PM 10 in El Paso, TX • Study suggests use a principal component analysis

Statistical Genetics Group • 5 Faculty • 2 Post – docs • 9 Ph.

Statistical Genetics Group • 5 Faculty • 2 Post – docs • 9 Ph. D Students • Support from NIH and NSF

Statistical Genetics Group • Sixteen Members – 5 faculty – 2 post-docs – 9

Statistical Genetics Group • Sixteen Members – 5 faculty – 2 post-docs – 9 Ph. D Students • Supported by 4 NIH Grants • Total funding of over $1 million

Statistical Genetics Group Aims – Develop new tools for analysis of genomic data –

Statistical Genetics Group Aims – Develop new tools for analysis of genomic data – Use innovative models and methods in human genetic studies Key Research Areas – – – Functional gene mapping Pedigree analysis Gene interactions Computational methodologies Microarray analysis

 • Statistical Genetics • Prof. Quiying Sha • Ph. D Student Elena Kasyanova

• Statistical Genetics • Prof. Quiying Sha • Ph. D Student Elena Kasyanova

 • Development of new computational and statistical tools • Primary focus is analysis

• Development of new computational and statistical tools • Primary focus is analysis and interpretation of genomic data

 • Concentration on complex human diseases • Key activities – Functional gene mapping

• Concentration on complex human diseases • Key activities – Functional gene mapping – Pedigree analysis – Genetic diversity

Combinatorics and Pure Math – Combinatorics – J. Bierbauer, D. Kreher, P. Merkey, V.

Combinatorics and Pure Math – Combinatorics – J. Bierbauer, D. Kreher, P. Merkey, V. Tonchev, M. Keranen – Commutative Algebra – F. Zanello March 2008 Computing Initiative

Combinatorics Group • ? ? ? Members – ? faculty – ? post-docs –

Combinatorics Group • ? ? ? Members – ? faculty – ? post-docs – ? Ph. D Students • Supported by ? ?

 • Ph. D. – Queen’s University Kingston • Joined MTU Fall 2007 •

• Ph. D. – Queen’s University Kingston • Joined MTU Fall 2007 • Commutative Algebra Fabrizio Zanello, Asst. Prof.

Non-Unimodal Level Hilbert Functions • Identified in Codimension 3. • h = (1, 3,

Non-Unimodal Level Hilbert Functions • Identified in Codimension 3. • h = (1, 3, 6, 10, 15, 21, 28, 27, 28) • Existence was long-standing open problem, and has led to several publications

Gorenstein Hilbert Functions • Identified asymptotic lower bound for the least possible Degree 2

Gorenstein Hilbert Functions • Identified asymptotic lower bound for the least possible Degree 2 entry • Socle degree 4 and codimension r • Solved 1983 conjecture of Stanley, proved in collaboration with Juan Migliore (Notre Dame) and Uwe Nagel (U. Kentucky) • f(r) ~r (6 r)2/3

Teaching and Instructional Resources

Teaching and Instructional Resources

 • Prof. Allan Struthers • Graduate Student Yejun Gong • Excellent faculty accessibility

• Prof. Allan Struthers • Graduate Student Yejun Gong • Excellent faculty accessibility

 • Dr. Ghan Bhatt teaches an introductory calculus course • Typical calculus class

• Dr. Ghan Bhatt teaches an introductory calculus course • Typical calculus class size is ~ 50 students

 • Beth Reed uses document camera in statistics lecture • Math classrooms renovated

• Beth Reed uses document camera in statistics lecture • Math classrooms renovated in 2006 • Rooms equipped with latest audio-visual tools

 • Teaching Assistant Rachel Robertson works with a student in the Mathlab •

• Teaching Assistant Rachel Robertson works with a student in the Mathlab • Calculus courses include laboratory component to reinforce lectures

 • Tutoring session in the Math Learning Center • Walk-in assistance or appointments

• Tutoring session in the Math Learning Center • Walk-in assistance or appointments with regular tutors

 • Math Learning Center open 6 days per week • Students teach students

• Math Learning Center open 6 days per week • Students teach students