Courses Offering

The course provides an overview of fundamental concepts in probability and statistics. Students learn how to correctly analyze data from experimental measurements. The content includes basic probability theory, random variables, random processes, basic statistics, error analysis, linear regression, etc.

Latest advances in surface science, quantum and 2D materials science and technology, soft matters, biophysics, computational physics, particle physics and cosmology will be showcased.

Exposure in latest development in science and career paths through Physics Departmental seminars, invited speakers from industry to talk about career/job/research.

Introduce the electronic, transport and optical properties of semiconductor materials and their relations with the devices performance.  Various fabrication techniques (i.e., MBE, MOCVD, PVD and CVD) and processing steps such as photolithography, etching, thermal oxidation, ion-implantation and thin-film deposition for semiconductor device fabrication will be presented.
(Required course for Advanced Materials Physics and Technology concentration)

Physics and fundamental properties of various quantum materials, and how to use them for manipulate and measure quantum states for applications in secure communication, information processing and sensing. 

This course introduce the physics and latest development of metamaterials (i.e., artificial structures at nanoscale) and their applications for photonic devices with new functionalities.

The physics and potential application of topological and correlated phenomena appearing in two-dimensional material platforms will be covered: Chern and topological insulators, superconductivity, Mott insulator, quantum spin liquid, quantum hall effect, and superconductivity and edge state arising from 2D and topological materials will be introduced. The potential of these phenomena and platforms toward their use in opto-electronic, spintronic and quantum information applications are also discussed.

This course introduce the various physical properties of materials such mechanical properties (i.e., strength, durability, hardness, flexibility, etc.), optical properties (i.e., refractive index, absorption coefficient, luminosity, etc.), thermal properties (i.e., melting point, expansion, conductivity, etc.), electrical properties (i.e., resistance, conductivity, etc.) and chemical properties (i.e., pH, corrosion resistance, reactivity, surface tension)
(Required course for Advanced Materials Physics and Technology concentration)

Introduce various techniques such as SEM, TEM AFM, XPS/UPS, XRD, Photoluminescence, Raman and optical spectroscopy to characterize the physical, optical and electronic properties of film and nanomaterials.
(Required course for Advanced Materials Physics and Technology concentration)

This course introduces hardware and software computational and modeling tools.  Hands-on experience on some common mathematics and physics simulation and modelling software such as MATLAB, Mathematica, COMSOL Multiphysics and Lumerical will be presented.  The basics about CPU, GPU and their applications in high performance computing in areas such as operating systems, parallel program design and quantum computation will also be introduced.
(Required course for Scientific Computing concentration)

Basic numerical and symbolic computation techniques will be introduced. Topics include methods of interpolation and extrapolation, approximation methods of root finding, numerical integration and solving ordinary differential equations, symbolic algebra and calculus, and Monte Carlo simulations. (Similar as the MSDM 5004, we will offer this course independently since it is our required course) (Exclusion: MSDM 5004 – Numerical Methods and Modeling in Science)
(Required course for Scientific Computing concentration)

This course provides students with an overview of the field of Artificial Intelligence (AI) focusing on machine learning ML) and its applications. It introduces several basic AI/ML Python software packages with emphasis on using existing AI techniques provided by the Python packages to solve problems in science. Throughout the course, students will gain hands-on experiences of using modern AI/ML software via solving practical problems. Students without the prerequisites but possess relevant knowledge may seek instructor's approval for enrolling in the course.
(Exclusion: DASC 4010 - Practical Artificial Intelligence in Science)
(Required course for Scientific Computing concentration)

Experimental or theoretical research project under supervision of a faculty. Program director approval is required. A final report and presentation by the student is required at the conclusion of the project.

This course introduces modern methodologies in machine learning, including tools in both supervised learning and unsupervised learning. Examples include linear regression and classification, tree-based methods, kernel methods and principal component analysis. Students will practice R or Python, and apply them to real data analysis.

This course will cover: (1) decision theory and its applications to finance; options and payoff diagrams, binomial trees; (2) portfolio management of financial time series using mean variance analysis; (3) evolutionary computation for optimization, with applications in finding good prediction rules in finance; (4) measure of information, various information entropies, and methods of maximum entropy; (5) game theory and its applications in competitive situations; (6) multi-agent systems modeling and applications to social networks and financial systems.

This course introduces atomistic computational methods to model, understand, and predict the properties and behavior of real materials, including solids, liquids, and nanostructures. Their application to sustainable energy will be discussed. Specific topics include: (1) Density-functional theory (DFT), Kohn-Sham equations, local and semi-local density approximations and hybrid functionals, basis sets, pseudopotentials; Hartree-Fock method. (2) Ab initio molecular dynamics with interatomic interactions derived on the fly from DFT, Car-Parrinello molecular dynamics, Monte-Carlo sampling; computational spectroscopy from first principles, IR and Raman.

An introductory course on postgraduate level solid state physics. The topics covered include: electronic band structures of solids, phonons, electron dynamics in crystals, electron interactions in solids, linear response theory, electronic transitions and optical properties of solids, electron phonon interactions, integer quantum Hall effects, superconductivity and magnetism.

Discussion of various applications of quantum mechanics, such as collision theory, theory of spectra of atoms and molecules, theory of solids, second quantization, emission of radiation, relativistic quantum mechanics. 

Laws and applications of thermodynamics, kinetic theory, transport phenomena, classical statistical mechanics, canonical and grand canonical ensemble, quantum statistical mechanics, Fermi and Bose systems, non-equilibrium statistical mechanics.

This is an introductory course on quantum field theory (QFT). The covered topics mainly include field quantization, interacting theory, quantum electrodynamics, renormalization and renormalization group.

Fundamental crystallography; crystalline structure and defects; X-ray and electron diffractions; imaging contrast mechanisms; structure determination; analytical electron microscopy. The instructor's approval is required for taking this course.