Computational Physics Master of Science Degree Program

Computational Physics is a discipline which focuses upon the numerical solutions of complex physical problems. In general, Physics problems are very difficult to solve exactly, which means that approximate solutions must be found. Computational Physicists develop computational models of real-world questions. This requires a solid knowledge of the mathematical and physical models employed as well as computational methods that will enable computers to reach precise and accurate solutions in reasonable periods of time.

This program draws from both the Physics and Computer Science/Engineering curriculum to prepare students for careers at the intersection of these fields. Students will develop pragmatic skills in this program applicable to much in demand careers in data science. This program is unique in that the department has specific strengths in quantum information, information physics, particle and astroparticle physics, and computational imaging.

The MS Degree in Computational Physics is a 2-year program which leads to employment in the field of computational physics, or an allied field. The program assumes some prior undergraduate experience in mathematics and physics and/or familiarity with a modern computer programming language, but is adaptable for students who need some introductory courses. Students will learn to develop models for discrete or continuous physical systems, how to elaborate algorithms, write and test the corresponding codes and finally perform relevant data analysis and visualization. Students will be required to choose an area of physics as well as computation for specialization and to either A) complete and defend a Master’s thesis in that subject area, or B) complete a final project in that subject area with an associated oral presentation/exam.

Students entering the program without an undergraduate degree in physics, may need to take Phy 550 Introduction QM Physics (3 credits) and Phy 511 Electromagnetism II: Introduction to Electrodynamics (3 credits) as admission deficiencies. Students entering the program without programming experience may need to take Phy 509 Programming in Physics (3 credits) as an admission deficiency. Students who do not have experience coding would be expected to take Phy 509 before taking more advanced courses such as Phy 577 or courses from the computational specialization list. Decisions on the need for the introductory courses will be based on placement exams taken before the start of the first semester.

Program Requirements (33 credits)

1. Core Courses - 18 credits:

• Phy 517 Statistical Mechanics (3)
• Phy 527 Classical Mechanics (3)
• Phy 537 Electrodynamics 1 (3)
• Phy 547 Quantum Mechanics 1 (3)
• Phy 577 Computational Methods (3)
• Phy 549 Quantum Information, Computation,and Foundations (3)

2. Physics Specialization Course - 3 credits:

• Phy 515 Electronics (3)
• Phy 516 Electronics Projects (3)
• Phy 525 Information Physics (3)
• Phy 526 Introduction to Particles Physics (3)
• Phy 528 The Physics of Radiation Therapy (3)
• Phy 530 Optics (3)
• Phy 533 Physics Measurements (3)
• Phy 543 Introduction to Cosmology (3)
• Phy 548 Medical Imaging (3)
• Phy 552 Astroparticle Physics (3)
• Phy 553 Microprocessor Applications (3)
• Phy 554 Microprocessor Applications Laboratory (3)
• Phy 557 Quantum Mechanics 2 (3)
• Phy 562 Structure and Properties of Materials (3)
• Phy 566 X-Ray Optics, Analysis and Imaging (3)
• Phy 580 Electron Diffraction and Microscopy (3)
• Phy 587 Solid State Physics 1 (3)
• Phy 632 Spectroscopy: Magnetic Resonance (3)

3. Computational/Math Specialization Courses - 9 credits:

• Phy/Csi/Inf 551 Bayesian Data Analysis and Signal Processing (3)
• Mat 502 Modern Computing for Mathematicians (3)
• Mat/Sta 554 Introduction to Theory of Statistics I (3)
• Mat/Sta 555 Introduction to Theory of Statistics II (3)
• Mat 583 Topological Data Analysis I (3)
• Mat 584 Topological Data Analysis II (3)
• Mat/Sta 664 Time Series Analysis I (3)
• Mat/Sta 665 Time Series Analysis II (3)
• Csi 501 Computational Linear Algebra, Nonlinear Algebra and Optimization (3)
• Csi 503 Algorithms and Data Structures (3)
• Csi 520 Topics in Distributed and Parallel Computing (3)
• Csi 523 Computational Geometry (3)
• Csi 531 Data Mining (3)
• Csi 536 Machine Learning (3)
• Csi 541 High Performance Scientific Computing II (3)
• Csi 663 Topics in Parallel Computation (1-3)
• Csi 665 Topics in Computer Graphics (1-3)
• Csi/Inf 671 Computer Vision (3)
• Ece 541 Parallel Computing (3)
• Ece 664 Statistical Pattern Recognition (3)
• Ece 673 Information Theory, Inference and Machine Learning (3)
• Inf 528 Analysis, Visualization and Prediction in Analytics (3)
• Inf 624 Predictive Modeling (3)
• Inf 625 Data Mining (3)

4. Research Requirement - 3 research credits of Phy 695 or Phy 699 leading to A) the successful defense of a Master’s Thesis or B) passing an oral examination upon completion of a final project