Computer Science Tracks

Computational Science and Engineering

The Computational Science and Engineering track focuses on using computation to model, simulate, analyze, and optimize complex systems arising in science, engineering, and data-driven applications. This track is designed to prepare you for careers in industry and research, as well as for graduate study, in areas that rely on mathematical modeling, numerical computation, algorithms, and large-scale software systems.

Coursework and Topics:

Students completing this track explore topics including:

  • Ordinary differential equations and mathematical modeling
  • Numerical methods and numerical linear algebra
  • Algorithm design and analysis
  • Optimization and operations research
  • Data mining, machine learning, and bioinformatics
  • Parallel, concurrent, and systems-level computing

The required coursework ensures breadth across mathematics, algorithms, and systems, while electives allow students to tailor the track toward numerical analysis, optimization, parallel computing, robotics, artificial intelligence, or theoretical foundations.

Courses

Course Title

Math 26600 

or

Math 36600

Ordinary Differential Equations
CS 31400 Numerical Methods
CS 38100 Introduction to the Analysis of Algorithms
Applications - 1 From the Following List
CS 37300 Data Mining and Machine Learning
CS 47300 Web Information Search and Management
CS 47800 Introduction to Bioinformatics
IE 33600 Operations Research - Stochastic Models
ECE 30100 Signals and Systems
Systems - 1 From the Following List
CS 35200 Compilers: Principles and Practice
CS 35300 Principles of Concurrency and Parallelism
CS 35400 Operating Systems

Note: Any course beyond the ones required class from the list of Applications/Systems courses also counts as an elective.

Electives (2)
Course Title
CS 30700 Software Engineering I
CS 42200 Computer Networks
CS 45600 Programming Languages
CS 45800 Introduction to Robotics
CS 47100 Introduction to Artificial Intelligence
CS 48300 Introduction to the Theory of Computation
CS 51400 Numerical Analysis
CS 51500 Numerical Linear Algebra
CS 52000 Computational Methods In Optimization
CS 52500 Parallel Computing
IE 33500 Operations Research - Optimization
MA 34100 Foundations of Analysis
MA 44000 Honors Real Analysis I

Graduate Pathways

Jobs and activities for students graduating from this track may include:

  • Computational Science and Engineering Industry Roles – working on simulation software, numerical libraries, performance-critical code, and computational modeling tools.
  • Data Science and Machine Learning Applications – developing algorithms and systems for data mining, bioinformatics, information retrieval, and large-scale data analysis.
  • Engineering and Scientific Software Development – building and maintaining software for modeling, simulation, and analysis in areas such as aerospace, energy, materials science, and biomedical engineering.
  • High-Performance and Parallel Computing – designing and optimizing parallel and distributed algorithms for multicore processors, GPUs, and large computing systems.
  • Research Laboratories and National Labs – working in applied research environments focused on scientific computing, modeling, and large-scale simulation (often with a graduate degree).
  • Graduate School – pursuing an MS or PhD in computer science, computational science and engineering, applied mathematics, data science, or engineering disciplines, leading to careers in research labs, industry R&D, and academia.

Student Experience

Pranav Konda

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