ADVANCED PROJECTS

May be repeated for up to 6 points of credit. Graduate-level projects in various areas of electrical engineering and computer science. In consultation with an instructor, each student designs his or her project depending on the students previous training and experience. Students should consult with a professor in their area for detailed arrangements no later than the last day of registration.

• Instructor: Debasis Mitra
  Wikipedia h-index: 61 Info
• Instructor: John W Paisley
  CULPA: 1 Info
• Instructor: Matthias Preindl
  h-index: 26 Info
• Instructor: Mingoo Seok
  h-index: 31 Info
• Instructor: Kenneth Shepard
  CULPA: 2 Wikipedia h-index: 64 Info
• Instructor: Tanvir Ahmed Khan
  Info
• Instructor: Yannis P Tsividis
  CULPA: 4 Wikipedia Info
• Instructor: David G Vallancourt
  CULPA: 28 Info
• Instructor: Wen I Wang
  CULPA: 1 Info
• Instructor: Xiaodong Wang
  CULPA: 15 Info
• Instructor: John Wright
  CULPA: 1 h-index: 44 Info
• Instructor: Charles A Zukowski
  CULPA: 5 Wikipedia Info
• Instructor: Gil Zussman
  CULPA: 3 h-index: 39 Info
• Instructor: Alexander Gaeta
  Wikipedia h-index: 89 Info
• Instructor: Savannah Eisner
  Info
• Instructor: James Anderson
  h-index: 18 Info
• Instructor: Asaf Cidon
  h-index: 20 Info
• Instructor: Xiang Meng
  h-index: 8 Info
• Instructor: Micah Goldblum
  Info
• Instructor: Homayoon Beigi
  Info
• Instructor: Henning G Schulzrinne
  CULPA: 12 Info
• 1-4 points

TPC NEUROSCI & DEEP LEARN

Comp. w. Brain Cir of MO

Selected advanced topics in neuroscience and deep learning. Content varies from year to year, and different topics rotate through the course numbers 6070 to 6079.

• M 7 p.m.-9:30 p.m.
• Instructor: Aurel A Lazar
  h-index: 53 Info
• 3 points

ADV ANALOG INTEGRATED CIRCUITS

Integrated circuit device characteristics and models; temperature- and supply-independent biasing; IC operational amplifier analysis and design and their applications; feedback amplifiers, stability and frequency compensation techniques; noise in circuits and low-noise design; mismatch in circuits and low-offset design. Computer-aided analysis techniques are used in homework(s) or a design project.

• T 4:10 p.m.-6:40 p.m.
• Instructor: Harish Krishnaswamy
  Info
• 3 points

Analog-Digital Interfaces in VLSI

Analog-to-digital and digital-to-analog conversion techniques for very large scale integrated circuits and systems. Precision sampling; quantization; A/D and D/A converter architectures and metrics; Nyquist architectures; oversampling architectures; correction techniques; system considerations. A design project is an integral part of this course.

• T 7 p.m.-9:30 p.m.
• Instructor: Timothy Dickson
  Info
• 3 points

MICROWAVE CIRCUIT DESIGN

Introduction to microwave engineering and microwave circuit design. Review of transmission lines. Smith chart, S-parameters, microwave impedance matching, transformation and power combining networks, active and passive microwave devices, S-parameter-based design of RF and microwave amplifiers. A microwave circuit design project (using microwave CAD) is an integral part of the course.

• M 4:10 p.m.-6:40 p.m.
• Instructor: Yves Baeyens
  h-index: 31 Info
• 3 points

ADV DIGITAL ELECTRONIC CIRCUIT

Advanced topics in the design of digital integrated circuits. Clocked and non-clocked combinational logic styles. Timing circuits: latches and flip-flops, phase-locked loops, delay-locked loops. SRAM and DRAM memory circuits. Modeling and analysis of on-chip interconnect. Power distribution and power-supply noise. Clocking, timing, and synchronization issues. Circuits for chip-to-chip electrical communication. Advanced technology issues that affect circuit design. The class may include a team circuit design project.

• TR 2:40 p.m.-3:55 p.m.
• Instructor: Mingoo Seok
  h-index: 31 Info
• 3 points

SEMICONDUCTOR DEVICE PHYSICS

Physics and properties of semiconductors. Transport and recombination of excess carriers. Schottky, P-N, MOS, and heterojunction diodes. Field effect and bipolar junction transistors. Dielectric and optical properties. Optical devices including semiconductor lamps, lasers, and detectors.

• R 4:10 p.m.-6:40 p.m.
• Instructor: Xiang Meng
  h-index: 8 Info
• 3 points

PHOTONIC INTEGRATED CIRCUITS

Photonic integrated circuits are important subsystem components for telecommunications, optically controlled radar, optical signal processing, and photonic local area networks. An introduction to the devices and the design of these circuits. Principle and modeling of dielectric waveguides (including silica on silicon and InP based materials), waveguide devices (simple and star couplers), and surface diffractive elements. Discussion of numerical techniques for modeling circuits, including beam propagation and finite difference codes, and design of other devices: optical isolators, demultiplexers.

• W 4:10 p.m.-6:40 p.m.
• Instructor: Michal Lipson
  CULPA: 1 Wikipedia h-index: 114 Info
• 3 points

Future Energy: Economics, Systems, Polic

Holistic understanding of the science and socioeconomic modelling of climate change, the properties of renewable energy sources, their economic and engineering
implications, and the policy decisions in the transition to future energy. Fundamentals of systems needed to support deep penetration of renewable energy, including grid stability, transmission and storage systems, and the design and operations of carbonaware datacenters.

• MW 11:40 a.m.-12:55 p.m.
• Instructor: Debasis Mitra
  Wikipedia h-index: 61 Info
• 3 points

Topics in Smart Electric Energy

DERs & Smart Grid Systems

Selected advanced topics in smart electric energy. Content varies from year to year.

• R 4:10 p.m.-6:40 p.m.
• Instructor: Mohamed Kamaludeen
  Info
• 3 points

Topics in Smart Electric Energy

Selected advanced topics in smart electric energy. Content varies from year to year.

• T 7 p.m.-9:30 p.m.
• Instructor: Navid Asr
  Info
• 3 points

TOPICS DATA-DRIVEN ANAL & COMP

Advanced Deep Learning

Selected advanced topics in data-driven analysis and computation. Content varies from year to year, and different topics rotate through the course numbers 6690 to 6699.

• W 10:10 a.m.-12:40 p.m.
• Instructor: Zoran Kostic
  CULPA: 1 h-index: 24 Info
• 3 points

TOPICS DATA-DRIVEN ANAL & COMP

Deep Learning on the Edge

Selected advanced topics in data-driven analysis and computation. Content varies from year to year, and different topics rotate through the course numbers 6690 to 6699.

• W 1:10 p.m.-3:40 p.m.
• Instructor: Zoran Kostic
  CULPA: 1 h-index: 24 Info
• 3 points

TOPICS DATA-DRIVEN ANAL & COMP

TPC: MATHEMATICS OF DEEP LEARN

Selected advanced topics in data-driven analysis and computation. Content varies from year to year, and different topics rotate through the course numbers 6690 to 6699.

• M 4:10 p.m.-6:40 p.m.
• Instructor: Predrag R Jelenkovic
  CULPA: 1 h-index: 35 Info
• 3 points

BAYESIAN MOD MACHINE LEARNING

Basic statistics and machine learning strongly recommended. Bayesian approaches to machine learning. Topics include mixed-membership models, latent factor models, Bayesian nonparametric methods, probit classification, hidden Markov models, Gaussian mixture models, model learning with mean-field variational inference, scalable inference for Big Data. Applications include image processing, topic modeling, collaborative filtering and recommendation systems.

• W 4:10 p.m.-6:40 p.m.
• Instructor: John W Paisley
  CULPA: 1 Info
• 3 points

Quantum Sensing Theory

QUANTUM SENSING THEORY

Introduction to quantum detection and estimation theory and its applications to quantum communications, quantum radar, quantum metrology, and quantum tomography. Background on quantum mechanics, quantum detection, composite quantum systems, Gaussian states, and quantum estimation.

• F 1:10 p.m.-3:40 p.m.
• Instructor: Xiaodong Wang
  CULPA: 15 Info
• 3 points

TOPICS IN NETWORKING

5G&NxtG Programmable Ntwr

Further study of areas such as communication protocols and architectures, flow and congestion control in data networks, performance evaluation in integrated networks. Content varies from year to year, and different topics rotate through the course numbers 6770 to 6779.

• F 1:10 p.m.-3:40 p.m.
• Instructor: Doru Calin
  h-index: 26 Info
• 3 points

TOPICS IN NETWORKING

MachineLearn for Networks

Further study of areas such as communication protocols and architectures, flow and congestion control in data networks, performance evaluation in integrated networks. Content varies from year to year, and different topics rotate through the course numbers 6770 to 6779.

• W 7 p.m.-9:30 p.m.
• Instructor: Anwar I Walid
  CULPA: 1 Info
• 3 points

Statistical signal processing and learni

STATISTICAL SIGNAL PROCES

Introduction to the fundamental principles of statistical signal processing related to detection and estimation. Hypothesis testing, signal detection, parameter estimation, signal estimation, and selected advanced topics. Suitable for students doing research in communications, control, signal processing, and related areas.

• M 10:10 a.m.-12:40 p.m.
• Instructor: Guido H Jajamovich
  Info
• 3 points

Sparse and Low-Dimensional Models for Hi

SPARSE MODELS FOR HI-D DA

Overview of theory, computation and applications for sparse and low-dimensional data modeling. Recoverability of sparse and low-rank models. Optimization methods for low-dim data modeling. Applications to imaging, neuroscience, communications, web data.

• T 4:10 p.m.-6:40 p.m.
• Instructor: John Wright
  CULPA: 1 h-index: 44 Info
• 3 points

TOPICS IN SIGNAL PROCESSING

INTRO TO BLOCKCHAIN TECH

Advanced topics in signal processing, such as multidimensional signal processing, image feature extraction, image/video editing and indexing, advanced digital filter design, multirate signal processing, adaptive signal processing, and wave-form coding of signals. Content varies from year to year, and different topics rotate through the course numbers 6880 to 6889.

• F 8:10 a.m.-10 a.m.
• 3 points

TOPICS IN SIGNAL PROCESSING

LARGE-SCALE STREAM PROCESSING

Advanced topics in signal processing, such as multidimensional signal processing, image feature extraction, image/video editing and indexing, advanced digital filter design, multirate signal processing, adaptive signal processing, and wave-form coding of signals. Content varies from year to year, and different topics rotate through the course numbers 6880 to 6889.  Topic: Large Data Stream Processing.

• R 7 p.m.-9:30 p.m.
• Instructor: Deepak S Turaga
  Info
• 3 points

TOPICS-INFORMATION PROCESSING

Quantum Engineering

The class will give you a richer understanding of networking, focusing on principles and systems that have shaped the Internet. Via classic and contemporary literature, topics covered include routing, content delivery, congestion control, data centers, SDN, and the cloud.

• R 4:10 p.m.-6:40 p.m.
• Instructor: Zhaoyou Wang
  Info
• 3 points

TOPICS-INFORMATION PROCESSING

Reinforcement Learning

Advanced topics spanning electrical engineering and computer science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899.

• R 10:10 a.m.-12:40 p.m.
• Instructor: Javad Ghaderi
  h-index: 17 Info
• 3 points

TOPICS-INFORMATION PROCESSING

TPC: Adv Big Data & AI

Advanced topics spanning Electrical Engineering and Computer Science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899.  Topic: Advanced Big Data Analytics.

• T 7 p.m.-9:30 p.m.
• Instructor: Ching-Yung Lin
  h-index: None Info
• 3 points

Topics-Information Processing

Computer Storage Systems

Advanced topics spanning electrical engineering and computer science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899.

• MW 4:10 p.m.-5:25 p.m.
• Instructor: Jack L Kouloheris
  Info
• 3 points

TOPICS IN ELECTRICAL AND COMPUTER ENGINE

Embedded AI

Selected topics in electrical and computer engineering. Content varies from year to year, and different topics rotate through the course numbers 6900 to 6909.

• M 1:10 p.m.-3:40 p.m.
• Instructor: Xiaofan Jiang
  h-index: 24 Info
• 3 points

Topics in Control

Model Predictive Control

This proposed course allows for a variety of potential advanced courses to be taught as part of the proposed concentration on control.

• T 7 p.m.-9:30 p.m.
• Instructor: Navid Asr
  Info
• 3 points

TECH WRITING FOR CS AND ENGINRS

Topics to help CS/CE and EE graduate students’ communication skills. Emphasis on writing, presenting clear, concise proposals, journal articles, conference papers, theses, and technical presentations.  Credit may not be used to satisfy degree requirements.

• F 10 a.m.-noon
• Instructor: Janet Kayfetz
  Info
• 3 points

FIELDWORK

May be repeated for credit, but no more than 3 total points may be used for degree credit. Only for electrical engineering and computer engineering graduate students who include relevant off-campus work experience as part of their approved program of study. Final report required. May not be taken for pass/fail credit or audited.

• Instructor: James Anderson
  h-index: 18 Info
• Instructor: Zoran Kostic
  CULPA: 1 h-index: 24 Info
• 1-1.5 points

RESEARCH II

Points of credit to be approved by the department. Requires submission of an outline of the proposed research for approval by the faculty member who is to supervise the work of the student. The research facilities of the department are available to qualified students interested in advanced study.

• Instructor: Dimitris Anastassiou
  Wikipedia Info
• Instructor: Keren Bergman
  CULPA: 1 Wikipedia h-index: 69 Info
• Instructor: Shih-Fu Chang
  CULPA: 3 Wikipedia h-index: 122 Info
• Instructor: Javad Ghaderi
  h-index: 17 Info
• Instructor: Christine P Hendon
  Wikipedia h-index: 21 Info
• Instructor: Predrag R Jelenkovic
  CULPA: 1 h-index: 35 Info
• Instructor: Xiaofan Jiang
  h-index: 24 Info
• Instructor: Ethan Katz-Bassett
  h-index: 31 Info
• Instructor: Dion Khodagholy Araghy
  Info
• Instructor: Peter Kinget
  CULPA: 1 h-index: 47 Info
• Instructor: Zoran Kostic
  CULPA: 1 h-index: 24 Info
• Instructor: Harish Krishnaswamy
  Info
• Instructor: Ioannis Kymissis
  CULPA: 4 h-index: 48 Info
• Instructor: Aurel A Lazar
  h-index: 53 Info
• Instructor: Michal Lipson
  CULPA: 1 Wikipedia h-index: 114 Info
• Instructor: Nima Mesgarani
  h-index: 35 Info
• Instructor: Debasis Mitra
  Wikipedia h-index: 61 Info
• Instructor: John W Paisley
  CULPA: 1 Info
• Instructor: Matthias Preindl
  h-index: 26 Info
• Instructor: Mingoo Seok
  h-index: 31 Info
• Instructor: Kenneth Shepard
  CULPA: 2 Wikipedia h-index: 64 Info
• Instructor: Tanvir Ahmed Khan
  Info
• Instructor: Yannis P Tsividis
  CULPA: 4 Wikipedia Info
• Instructor: David G Vallancourt
  CULPA: 28 Info
• Instructor: Wen I Wang
  CULPA: 1 Info
• Instructor: Xiaodong Wang
  CULPA: 15 Info
• Instructor: John Wright
  CULPA: 1 h-index: 44 Info
• Instructor: Charles A Zukowski
  CULPA: 5 Wikipedia Info
• Instructor: Gil Zussman
  CULPA: 3 h-index: 39 Info
• Instructor: James Anderson
  h-index: 18 Info
• Instructor: Savannah Eisner
  Info
• Instructor: James Anderson
  h-index: 18 Info
• Instructor: Asaf Cidon
  h-index: 20 Info
• Instructor: Xiang Meng
  h-index: 8 Info
• Instructor: Micah Goldblum
  Info
• 0-6 points