Scientific Computing, MSE

The MSE in Scientific Computing (SCMP) program at Penn provides multifaceted education in the fundamentals and applications of computational science. This education program provides a rigorous computational foundation for applications to a broad range of scientific disciplines. An education in SCMP combines a comprehensive set of core courses centered on numerical methods, algorithm development for high performance computational platforms, and the analysis of large data, and offers flexibility to specialize in different computational science application areas. Students may elect to pursue a thesis in computationally-oriented research within the School of Engineering and Applied Science.

We welcome applications from candidates who have a strong background in physical or theoretical sciences, engineering, math, or computer science. Some experience with computer programming is also strongly recommended.


10 course units are required for the MSE in Scientific Computing.

CIT 590Programming Languages and Techniques1
or CIT 591 Introduction to Software Development
CIT 596Algorithms and Computation1
Core Requirements
ENM 502Numerical Methods and Modeling1
CIS 545Big Data Analytics1
Select 1 of the following:1
Applied Machine Learning
Machine Learning
Modern Data Mining
Technical & Depth Area Electives
Select 2 Simulation Methods for Natural Science/Engineering courses2
Select Thesis/Independent Study or 2 Natural Science/Engineering electives2
Select 1 Technical & Depth Area elective 11
Total Course Units10

Technical & Depth Area Electives

Thesis/Independent Study 2
Bio medicine
Brain-Computer Interfaces
Networked Neuroscience
Mathematical Computation Methods for Modeling Biological Systems
Social/Network Science
Econometrics I: Fundamentals
Econometrics II: Methods & Models
Econometrics III: Advanced Techniques of Cross-Section Econometrics
Econometrics IV: Advanced Techniques of Time-Series Econometrics
Applied Probability Models in Marketing
Natural Science/Engineering 3
Chemical Engineering:
Advanced Chemical Kinetics and Reactor Design
Transport Processes II (Nanoscale Transport)
Interfacial Phenomena
Mechanical Engineering:
Micro and Nano Fluidics
Nanoscale Systems Biology
Fundamental Techniques of Imaging I
Biomedical Image Analysis
Materials Science and Engineering:
Phase Transformations
Elasticity and Micromechanics of Materials
Data-centric Programming
Software Systems
Software Engineering
Computer Systems Programming
Advanced Programming
Internet and Web Systems
Programming and Problem Solving
Data Collection, Representation, Management and Retrieval
Database and Information Systems
Sample Survey Methods
Observational Studies
Data Analysis, Artificial Intelligence
Computational Linguistics
Machine Perception
Computer Vision & Computational Photography
Advanced Topics in Machine Perception
Theory of Machine Learning
Data Mining: Learning from Massive Datasets
Modern Data Mining
Special Topics
Analysis of Algorithms
Advanced Topics in Algorithms and Complexity
Algorithms and Computation
Artificial Intelligence
Learning in Robotics
Modern Regression for the Social, Behavioral and Biological Sciences
Simulation Methods for Natural Science/Engineering
Atomic Modeling in Materials Science
Multiscale Modeling of Chemical Systems
Molecular Modeling and Simulations
Computational Science of Energy and Chemical Transformations
Finite Element Analysis
Computational Mechanics
Feedback Control Design and Analysis
Topics In Computational Science and Engineering
Statistics, Mathematical Foundations
Numerical Methods and Modeling
Fundamentals of Linear Algebra and Optimization
Complex Analysis
Intro to Linear, Nonlinear and Integer Optimization
Accelerated Regression Analysis for Business
Stochastic Processes
Modern Convex Optimization
Information Theory

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2020 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.