Fall 2022 Courses

ISC 1057
Computational Thinking
Young Hwan Kim
This introductory course considers the question of how computers have come to imitate many kinds of human intelligence. The answer seems to involve our detecting patterns in nature, but also in being able to detect patterns in the very way we think. We will look at some popular computational methods that shape our lives, and try to explain the ideas that make them work. This course has been approved to satisfy the Liberal Studies Quantitative/Logical Thinking requirement.
ISC 2310
Introduction to Computational Thinking in Data Science with Python
This course investigates strategies behind popular computational methods used in data science. In addition, many of the algorithms are implemented using the programming language Python. No prior programming experience is required so the course presents the basics of the Python language as well as how to leverage Python’s libraries to solve real-world problems in data science. Prerequisite: MAC 1105 or equivalent.
ISC 3222
Symbolic and Numerical Computations
M W F 8:00-8:50, 152 DSL
Introduces state-of-the-art software environments for solving scientific and engineering problems. Topics include solving simple problems in algebra and calculus; 2-D and 3-D graphics; non-linear function fitting and root finding; basic procedural programming; methods for finding numerical solutions to DE's with applications to chemistry, biology, physics, and engineering. Prerequisite: MAC 2311.
ISC 3313
Introduction to Scientific Computing with C++
M W F 12:00-12:50, 152 DSL
This course introduces the student to the science of computations. Topics cover algorithms for standard problems in computational science, as well as the basics of an object-oriented programming language, to facilitate the student’s implementation of algorithms. The computer language will be C++. Prerequisite: MAC 2311.
ISC 4221C
Discrete Algorithms for Science Applications
T R 9:45-11:00, 152 DSL - T 3:05-5:35 (Lab), 152 DSL
Chen Huang
This course offers stochastic algorithms, linear programming, optimization techniques, clustering and feature extraction presented in the context of science problems. The laboratory component includes algorithm implementation for simple problems in the sciences and applying visualization software for interpretation of results. Prerequisite: MAC 2311.
ISC 4223C
Computational Methods for Discrete Problems
M W F 1:20-2:10, 152 DSL - F 3:05-5:35 (Lab), 152 DSL
Tomasz Plewa
This course describes several discrete problems arising in science applications, a survey of methods and tools for solving the problems on computers, and detailed studies of methods and their use in science and engineering. The laboratory component illustrates the concepts learned in the context of science problems. Prerequisites: MAS 3105, ISC 4304C.
ISC 4232C
Computational Methods for Continuous Problems
M W F 9:20-10:10, 152 DSL - M 3:05-5:35 (Lab), 152 DSL
Bryan Quaife
This course provides numerical discretization of differential equations and implementation for case studies drawn from several science areas. Finite difference, finite element, and spectral methods are introduced and standard software packages used. The lab component illustrates the concepts learned on a variety of application problems. Prerequisites: MAS 3105, ISC 4220, ISC 4304C.
ISC 5305
Scientific Programming
T R 1:20-2:35, 499 DSL
Gordon Erlebacher
This course uses the C language to present object-oriented coding, data structures, and parallel computing for scientific programming. Discussion of class hierarchies, pointers, function and operator overloading, and portability. Examples include computational grids and multidimensional arrays.
ISC 5315
Applied Computational Science I
T R 11:35-12:50, 152 DSL - R 3:05-5:35 (Lab), 152 DSL
Chen Huang
Course provides students with high-performance computational tools necessary to investigate problems arising in science and engineering, with an emphasis on combining them to accomplish more complex tasks. A combination of course work and lab work provides the proper blend of theory and practice with problems culled from the applied sciences. Topics include numerical solutions to ODEs and PDEs, data handling, interpolation and approximation and visualization. Prerequisites: ISC 5305, MAP 2302.
ISC 5228 / ISC 4933
Markov Chain Monte Carlo Simulations
T R 9:45-11:00, 422 DSL
Sachin Shanbhag
Covered are statistical foundations of Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) simulations, applications of MC and MCMC simulations, which may range from social sciences to statistical physics models, statistical analysis of autocorrelated MCMC data, and parallel computing for MCMC simulations.
ISC 4933 Understanding Covid
ISC 5935 Understanding Covid
M W F 10:40-11:30, DSL 499
Alan Lemmon
In this course, students will explore the different elements that determine the outcome of infectious disease in humans, using COVID-19 as a case study. Starting with a very basic model that includes susceptible, infected, and recovered individuals, students will gradually incorporate factors that may affect the outcome of an outbreak, such as masking/quarantining, gain and loss of natural and vaccine-based immunity, and changing virulence/strains. After summarizing data already collected during the recent pandemic, students will inform the model to determine the conditions under which different outcomes may occur. Students will summarize their results graphically and present their findings. No prerequisites or programming experience required. The course is designed to be accessible to all students, regardless of background or major.

ISC 4931
Junior Seminar
F 1:20 - 2:20, 422 DSL
Alan Lemmon
Junior Seminar in Computational Science.
ISC 4932
Senior Seminar
W 4:50 - 5:50, 416 DSL
Tomasz Plewa
Senior Seminar in Computational Science.
ISC 5934
Graduate Seminar
F 3:05-3:55, 499 DSL
Xiaoqiang Wang
A series of lectures given by faculty on the research being conducted.
W 3:30 - 4:30, 499 DSL
Peter Beerli
Weekly colloquium given by invited speakers to showcase research.