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.
For more information: https://pics.upenn.edu/masters-science-engineering-scientific-computing/
Curriculum
10 course units are required for the MSE in Scientific Computing.
| Code | Title | Course Units |
|---|---|---|
| Foundations | ||
| CIT 590 | Programming Languages and Techniques | 1 |
| or CIT 591 | Introduction to Software Development | |
| CIT 596 | Algorithms and Computation | 1 |
| Core Requirements | ||
| ENM 502 | Numerical Methods and Modeling | 1 |
| CIS 545 | Big Data Analytics | 1 |
| Select 1 of the following: | 1 | |
| Applied Machine Learning | ||
or CIS 520 | Machine Learning | |
or STAT 571 | Modern Data Mining | |
| Technical & Depth Area Electives | ||
| Select 2 Simulation Methods for Natural Science/Engineering courses | 2 | |
| Select Thesis/Independent Study or 2 Natural Science/Engineering electives | 2 | |
| Select 1 Technical & Depth Area elective 1 | 1 | |
| Total Course Units | 10 | |
Technical & Depth Area Electives
| Code | Title | Course Units |
|---|---|---|
| Applications | ||
| 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: | ||
| Aerodynamics | ||
| Nanotribology | ||
| Micro and Nano Fluidics | ||
| Bioengineering: | ||
| Nanoscale Systems Biology | ||
| Fundamental Techniques of Imaging I | ||
| Biomedical Image Analysis | ||
| Materials Science and Engineering: | ||
| Nanotribology | ||
| Phase Transformations | ||
| Elasticity and Micromechanics of Materials | ||
| Methods | ||
| 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 | ||
| Modelling | ||
| 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 | ||
| 1 | Or a free elective (subject to approval) |
| 2 | Select 2 course units of SCMP 597 Thesis Research or SCMP 599 Independent Study. |
| 3 | Generally, any course in which the primary focus is a physical/chemical/biological/mechanical application area that may be studied computationally is allowed. |
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.