Prepare yourself to explore future science and technology with our interdisciplinary
graduate certificate program. These certificates allow students to learn beyond their graduate research. Current
graduate students can also earn these credentials simultaneously with their degrees.
Enrolled graduate students interested in earning a physics graduate certificate should request to add it using the Add a Graduate Certificate form. Once this is processed, students will be notified to review the requirements in
the Academic Audit. No other form or signature is needed. See full details at the
Graduate School.
Advanced Computational Physics
Numerical programming and computer simulations are ubiquitous through all subjects
in physics. Thus, computational physics has grown to be an appealing field for those
who wish to acquire advanced and modern skills to solve interdisciplinary problems.
The graduate certificate in Advanced Computational Physics develops a foundation in programming, UNIX computing environment, system libraries,
and computer graphics, to enable you to start exploring more advanced computational
topics. You will learn basic and advanced numerical algorithms, develop and implement
numerical methods and computer simulations using these new skills, and explore the
application of advanced computation to scientific problems in your research area.
Big Data Statistics in Astrophysics
Astronomy and astrophysics data are undergoing dramatic growth in size and complexity
as detectors, telescopes, and computers become ever more powerful. Modern telescopes
produce terabytes of data per observation, and over the next decade the data volume
is expected to enter the petabyte domain.
The graduate certificate Big Data Statistics in Astrophysics will help you to develop a foundation of statistical analysis, data mining, and machine
learning; understand how to implement algorithms; how to use databases to manage the
data; and how to learn from the data with machine learning tools. You will develop
and implement new machine learning methods for problems in astrophysics. Based on
these skills, you can explore applications of statistical techniques and machine learning
tools to analyze and interpret astrophysical data.
Frontiers in Materials Physics
Experimental materials physics is an indispensable field for advanced electronic,
photonic, energy devices, and future quantum computation and communication. This growing
area is expected to meet many academic and industrial needs.
The graduate certificate in Frontiers in Materials Physics aims to help you develop foundational knowledge and techniques in the areas of: low-dimensional
materials, quantum and topological materials, energy materials, and atmospheric particles
and nanomaterials. You may also explore applications for spectroscopy, photovoltaics,
optoelectronic devices, and environmental optics.
Frontiers in Optics and Photonics
Optical and photonic physics are emerging fields for telecommunication, optical
computing, biophotonics, quantum sensing/communication and computing. This growing
field is expected to meet many academic and industrial needs.
The graduate certificate in Frontiers in Optics and Photonics will develop your foundation in integrated photonics, nano optics, computational
electromagnetics, quantum optics, and optical wave propagation in complex media. Based
on these skills, you can explore applications in telecommunications, biophotonics,
quantum computing, sensing and imaging.
Certificate Requirements
Each certificate is comprised of at least one required course, with the remainder
of the 9 credits selected from a list of elective courses. The program advisor will
help you select courses that fit your interests and skills.
Advanced Computational Physics - Required
PH 5395 - Computer Simulation in Physics
Role of computer simulation in physics with emphasis on methodologies, data and error analysis, approximations, and potential pitfalls. Methodologies may include Monte Carlo simulation, molecular dynamics, and first-principles calculations for materials, astrophysics simulation, and biophysics simulations.
- Credits:
3.0
- Lec-Rec-Lab: (2-0-3)
- Semesters Offered:
Spring, in odd years
Advanced Computational Physics - Electives
This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Spring
- Restrictions:
Permission of instructor required;
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
- Pre-Requisite(s): CS 4801
EE 5841 - Machine Learning
This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Spring
- Restrictions:
Permission of instructor required;
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
EE 5821 - Computational Intelligence - Theory and application
This course covers the four main paradigms of Computational Intelligence, viz., fuzzy systems, artificial neural networks, evolutionary computing, and swarm intelligence, and their integration to develop hybrid systems. Applications of Computational Intelligence include classification, regression, clustering, controls, robotics, etc.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
On Demand
- Restrictions:
Permission of instructor required;
Must be enrolled in one of the following Level(s): Graduate
Big Data Statistics in Astrophysics - Required
Big Data Statistics in Astrophysics - Electives (4000 level - Maximum 3 credits)
PH 4620 - Galaxies and Cosmology
Topics cover the structure and dynamics of galaxies, highlighting the roles of stellar populations and the interstellar medium, as well as the role of dark matter. The course includes galaxy formation and evolution and introduces cosmology, covering the physics of the early universe, theoretical models, large-scale structure, and observational cosmology, with emphasis on how models are connected to observables to build confidence in our physical understanding of fundamental cosmic phenomena.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Spring, in odd years
- Pre-Requisite(s): PH 1600 and PH 2400 and (MA 3520 or MA 3521 or MA 3530 or MA 3560)
PH 4630 - High-Energy Astrophysics
This course examines the physics of cosmic particle acceleration, revealed through nonthermal radiative processes and particle astrophysics. Topics include compact objects such as neutron stars and black holes, and the extreme environments around them. The course may also explore gamma-ray instrumentation, multi-messenger methods (including high-energy photons, neutrinos, and gravitational waves)m and theoretical models that describe these extreme astrophysical environments.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Spring, in even years
- Pre-Requisite(s): PH 2400 and (MA 3520 or MA 3521 or MA 3530 or MA 3560)
Big Data Statistics in Astrophysics - Electives (5000 level)
PH 5395 - Computer Simulation in Physics
Role of computer simulation in physics with emphasis on methodologies, data and error analysis, approximations, and potential pitfalls. Methodologies may include Monte Carlo simulation, molecular dynamics, and first-principles calculations for materials, astrophysics simulation, and biophysics simulations.
- Credits:
3.0
- Lec-Rec-Lab: (2-0-3)
- Semesters Offered:
Spring, in odd years
This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Spring
- Restrictions:
Permission of instructor required;
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
- Pre-Requisite(s): CS 4801
EE 5841 - Machine Learning
This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Spring
- Restrictions:
Permission of instructor required;
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
EE 5821 - Computational Intelligence - Theory and application
This course covers the four main paradigms of Computational Intelligence, viz., fuzzy systems, artificial neural networks, evolutionary computing, and swarm intelligence, and their integration to develop hybrid systems. Applications of Computational Intelligence include classification, regression, clustering, controls, robotics, etc.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
On Demand
- Restrictions:
Permission of instructor required;
Must be enrolled in one of the following Level(s): Graduate
Frontiers in Materials Physics - Required - Choose one of the following:
PH 5520 - Materials Physics
Materials classification and structures; phase diagrams; lattice imperfections; quasiparticles; boundaries and interfaces; mechanical, electronic, optical, magnetic and superconducting properties of materials.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Spring, in odd years
- Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Frontiers in Materials Physics - Electives
Only three credits at the 4000 level may count toward the certificate requirements.
PH 5510 - Theory of Solids
Free electron theory, Bloch's theorem, electronic band structure theory, Fermi surfaces, electron transport in metals and semiconductors. Lattice vibrations and phonons, other topics as time permits.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Spring, in even years
- Restrictions:
Must be enrolled in one of the following Level(s): Graduate
- Pre-Requisite(s): PH 5320 and PH 5410
PH 5520 - Materials Physics
Materials classification and structures; phase diagrams; lattice imperfections; quasiparticles; boundaries and interfaces; mechanical, electronic, optical, magnetic and superconducting properties of materials.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Spring, in odd years
- Restrictions:
Must be enrolled in one of the following Level(s): Graduate
PH 5530 - Selected Topics in Nanoscale Science and Technology
Presentation and discussion of selected topics in nanoscale science and engineering. Topics include growth, properties, applications, and societal implication of nanoscale materials. Evaluation: attendance and assignment.
- Credits:
2.0
- Lec-Rec-Lab: (2-0-0)
- Semesters Offered:
On Demand
- Restrictions:
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
PH 5151 - Quantum Field Theory for Photonics and Materials
This course will review the basics of quantum mechanics and second quantization, and cover quantum field theoretical methods, including Wick's theorem and Feynman diagram techniques, for absolute zero and non-zero temperatures (Matsubara frequencies) and their application in photonics, properties of materials and condensed matter physics.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Spring, in even years
- Pre-Requisite(s): PH 3410 and PH 3411(C)
MSE 5151 - Quantum Field Theory for Photonics and Materials
This course will review the basics of quantum mechanics and second quantization, and cover quantum field theoretical methods, including Wick's theorem and Feynman diagram techniques, for absolute zero and non-zero temperatures (Matsubara frequencies) and their application in photonics, properties of materials and condensed matter physics.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Spring, in even years
EE 5430 - Electronic Materials
A study of the physical principles of electronic materials, their applications in solid-state devices, and future trends in their development.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Fall
- Restrictions:
Must be enrolled in one of the following Level(s): Graduate
EE 5460 - Solid State Devices
A study of the physical principles and evolution of solid-state devices, such as transistors: from conventional to novel types utilizing hetero-junctions and quantum effects; light emitting devices, semiconductor lasers; and displays of various types.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Fall, Spring
- Restrictions:
May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
MSE 5130 - Crystallography & Diffraction
Crystallographic concepts and diffraction analyses in materials science.
- Credits:
3.0
- Lec-Rec-Lab: (2-0-3)
- Semesters Offered:
Spring
- Restrictions:
Must be enrolled in one of the following Level(s): Graduate
PH 4292 - Light and Photonic Materials
Material properties controlling light wave propagation in optical crystals and optical waveguides. Photonic crystals and photonic devices based on electrical, magnetic, and strain effects.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
On Demand
- Pre-Requisite(s): PH 2200(C)
Frontiers in Optics and Photonics - Required
PH 4292 - Light and Photonic Materials
Material properties controlling light wave propagation in optical crystals and optical waveguides. Photonic crystals and photonic devices based on electrical, magnetic, and strain effects.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
On Demand
- Pre-Requisite(s): PH 2200(C)
Frontiers in Optics and Photonics - Electives
PH 5210 - Electrodynamics I
Electrostatics and magnetostatics, boundary value problems, multipoles, Maxwell's equations, time-dependent fields, propagating wave solutions, radiation.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Fall
- Restrictions:
Must be enrolled in one of the following Level(s): Graduate
- Pre-Requisite(s): PH 5320
PH 5151 - Quantum Field Theory for Photonics and Materials
This course will review the basics of quantum mechanics and second quantization, and cover quantum field theoretical methods, including Wick's theorem and Feynman diagram techniques, for absolute zero and non-zero temperatures (Matsubara frequencies) and their application in photonics, properties of materials and condensed matter physics.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Spring, in even years
- Pre-Requisite(s): PH 3410 and PH 3411(C)
MSE 5151 - Quantum Field Theory for Photonics and Materials
This course will review the basics of quantum mechanics and second quantization, and cover quantum field theoretical methods, including Wick's theorem and Feynman diagram techniques, for absolute zero and non-zero temperatures (Matsubara frequencies) and their application in photonics, properties of materials and condensed matter physics.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Spring, in even years
PH 5410 - Quantum Mechanics I
Study of the postulates of quantum mechanics framed in Dirac notation, the Heisenberg uncertainty relations, simple problems in one dimension, the harmonic oscillator, the principles of quantum dynamics, rotational invariance and angular momentum, spherically symmetric potentials including the hydrogen atom, and spin.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Fall
- Restrictions:
Must be enrolled in one of the following Level(s): Graduate
Analysis and modeling of diffraction effects on optical systems, emphasizing frequency-domain analytic and computational approaches. Presents wave propagation, imaging, and optical information processing applications.
- Credits:
3.0
- Lec-Rec-Lab: (3-0-0)
- Semesters Offered:
Fall
- Restrictions:
Must be enrolled in one of the following Level(s): Graduate;
Must be enrolled in one of the following Major(s): Electrical Engineering, Electrical Engineering, Electrical & Computer Engineer
- Pre-Requisite(s): EE 3190
Contacts
Advanced Computational Physics
Big Data Statistics in Astrophysics
Frontiers in Materials Physics
Frontiers in Optics and Photonics
Graduate Program Assistant
