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# Gianluca Guadagni

##### Assistant Professor

Assistant Professor, Department of Engineering and Society

Unit: School of Engineering and Applied Science

Department: Department of Engineering and Society

##### Office location and address

156 Engineer's Way

Charlottesville,
Virginia
22903
##### Publications

##### Courses

Credits: 4

The concepts of differential and integral calculus are developed and applied to the elementary functions of a single variable. Limits, rates of change, derivatives, and integrals. Applications are made to problems in analytic geometry and elementary physics. For students with no exposure to high school calculus.

Credits: 4

First order differential equations, second order and higher order linear differential equations, undetermined coefficients, variation of parameters, Laplace transforms, linear systems of first order differential equations and the associated matrix theory, numerical methods. Applications. Prerequisite: APMA 2120 or equivalent.

Credits: 3

Introduces computational techniques for solving biomedical engineering problems & constructing models of biologic processes. Numerical techniques include regression, interpolation, differentiation, integration, root finding, systems of equations, optimization and approaches to ordinary differential equations. Applications include bioreactors, biotransport, pharmacokinetics & biomechanics. Prereq: APMA 2120 & CS 1110; recommended co-req APMA 2130.

Credits: 3

This course invites students to explore the implications of STS core concepts within a specific topical or disciplinary area, drawing out the implications of STS 1500 in depth. The course explores the social and global context of engineering, science and technology. Although writing and speaking skills are emphasized, more attention is given to course content and the students' analytical abilities. Prerequisites: STS 1500 or an equivalent STS course.

Credits: 1–4

Special topics in applied mathematics

Credits: 3

This course uses a Case-Study approach to teach statistical techniques with R. Basic statistical techniques include confidence intervals, hypotheses tests, regression, and anova. In addition, the course covers major statistical learning techniques for both supervised and unsupervised learning. Supervised learning topics cover regression and classification while unsupervised learning topics cover clustering and principal component analysis.

Credits: 1–4

Applies mathematical techniques to special problems of current interest. Topic for each semester are announced at the time of course enrollment.

Credits: 3

Applies mathematical techniques to special problems of current interest. Topic for each semester are announced at the time of course enrollment.

Credits: 1–3

Reading and research under the direction of a faculty member. Prerequisite: Fourth-year standing.

Credits: 3

Introduces techniques used in obtaining numerical solutions, emphasizing error estimation. Includes approximation and integration of functions, and solution of algebraic and differential equations. Prerequisite: Two years of college mathematics, including some linear algebra and differential equations, and the ability to write computer programs in any language.

Credits: 3

An introduction to essential programming concepts, structures, and techniques. Students will gain confidence in not only reading code, but learning what it means to write good quality code. Additionally, essential and complementary topics are taught, such as testing and debugging, exception handling, and an introduction to visualization. This course is project based, consisting of a semester project and final project presentations.

Credits: 3

Role of statistics in science, hypothesis tests of significance, confidence intervals, design of experiments, regression, correlation analysis, analysis of variance, and introduction to statistical computing with statistical software libraries. Cross-listed as APMA 6430. Prerequisite: Admission to graduate studies or instructor permission.

Credits: 1–3

Topics vary from year to year and are selected to fill special needs of graduate students.

Credits: 1–12

For master's students.

Credits: 1–12

For doctoral students.