Special Topics

Undergraduate Courses

Fall 2026

EEC189L

• CRN: 22425 and 22426
• Units: 4
• Professor: Marina Radulaski
• Lecture: M, W 2:10-3:30 PM
• Lab: 6:10 -8:00 PM (Kemper Hall 2161)
• Prerequisites: MAT 022A; Recommended a prior course on computer programming.
• Course Description: Quantum computing, how quantum information differs from classical information, and fundamental quantum mechanics concepts such as superposition and entanglement, physical implementations of quantum hardware, and the design of quantum circuits. This course covers principles of quantum information and hands-on quantum programming. Dirac notation, quantum gates, quantum circuits, quantum algorithms, and the basics of the physical implementation of quantum computers.
  • Quantum Information
    • Qubits
    • Bloch Sphere
    • Dirac notation
    • Quantum measurement
    • Density matrix
    • Quantum Entanglement
  • Quantum Circuits
    • ​Single-qubit gates
    • Multi-qubit gates
    • Quantum register
    • Measurement
  • Quantum Algorithms
    • Amplitude Amplification algorithm
    • Grover’s search algorithm
    • Deutsch-Jozsa algorithm
    • Quantum Fourier Transform  
    • Shor’s Algorithm
  • Physical implementation of quantum computing
    • DiVincenzo criteria
    • Qubit implementations
    • Quantum networking
    • Quantum sensing

Graduate EEC 289 Courses

Fall 2026 Special Topics

EEC289Q: Neurally Inspired Algorithms and Architectures

• CRN: 29862
• Units: 4
• Professor: Anthony Thomas
• Lecture: M/W 4:10PM-6:00PM
• Prerequisites: EEC 161 (or equivalent background in probability), MAT 22A (linear algebra), helpful, but not required – background in machine learning, signal processing, or optimization.
• Course Description: This course provides an overview of topics at the intersection of neural computation and algorithm design and is intended for students with a solid background in linear algebra and probability. Prior exposure to the basic principles of statistical estimation and optimization will be helpful, but not strictly required. The goals for the course are for students to learn about:
  • Mathematically rigorous tools for understanding and analyzing algorithms in neural computation.
  • The use of randomized methods in neural computation and algorithms.
  • Other paradigms for learning in addition to the currently dominant statistical one.
  • Connections between topics in the analysis of algorithms and neuroscience/neural computation.
  • Applications of some of the above to developing new kinds of computer hardware.

Course grades will be based on a mix of problem sets, in-class presentations, paper-readings, and a final project.

EEC 289O: Integrated Circuits Design and Tapeout

• CRN: 47979
• Units: 2
• Professor: Omeed Momeni
• Lecture: TR 1:40 3:00
• Prerequisites: EEC 210
• Course Description: This course series takes the students on a journey of designing an integrated circuit (IC) from scratch all the way to making it ready for fabrication and tapeout and finally preparing the measurement setup and packaging when the chip is back from the foundry. Enrolling in the Spring quarter is mandatory. Enrolling in the Fall quarter is also mandatory for the students who want their chips to be fabricated and characterized. It is highly recommended to enroll in this class if the student can enroll in all 3 quarters. The course starts in the Winter quarter by covering the basics of chip design flow including process technology overview, layout process, design rules, simulation and verification, packaging, and measurement. Students will use Cadence Virtuoso and the TSMC 180 nm CMOS PDK to perform schematic capture, run simulations, create transistor-level layouts, extract parasitics, and verify DRC/LVS compliance. There will be 5-6 homework assignments to design, simulate and prepare the layout for a few analog and digital circuit blocks such as an inverter chain, a ring oscillator, a divide by two circuit, and an amplifier in the TSMC’s 180 nm CMOS process. Toward the end of the quarter the students propose the circuit they would like to design and fabricate in the Spring quarter.
• Syllabus

EEC 289Q: Modeling and Optimization in Computer System Design

• CRN: 48724
• Units: 4
• Section 2
• Professor: Soheil Ghiasi
• Lecture: TR 9:00am-10:20am
• Prerequisites: ECS36C and (EEC170 or EEC180) 
• Course Objectives: 
  • The objective of this course to enable graduate students working in the broad area of computer engineering to approach optimization problems encountered in the domain in a more principled fashion, and to enable them to leverage effective techniques from optimization, particularly discrete and combinatorial optimization, to model, to explore tradeoffs, and to solve problems that may be encountered in their field of research. 
  • The course aims to address the gap between core computer engineering classes, such as those focused on digital circuit design and programming of computing systems, and courses in optimization, which typically focus on foundational treatment of continuous optimization problems. Through the course, the students will gain a deeper understanding of both classic and recent techniques used in discrete optimization, and will grow into informed users of optimization tools and methodologies that are likely to find applications in their research area. 
  • A significant component of the course is a collection of example problems that arise in different subareas of computer engineering, such as layout synthesis, high-level synthesis, software compilers, and operating systems, which will be used both to motivate the concepts, and to illustrate the tradeoffs and utility of the techniques.