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CSE Course Announcements

Winter 2019: Advanced Topics and the Design of Power Electronics

Transformative technologies in energy conversion will be smaller, cheaper, and more efficient. This class will address some advanced topics and techniques in power electronics and the craft of design through case studies. Topics may include switched capacitor circuits, resonant power conversion, magnetics, wireless power transfer, and instrumentation, among others. Advanced methods in the analysis, manufacturing, and control of power electronics will also be discussed. Design cases may include audio switching power amplifiers, photovoltaic switch capacitor circuits, resonant converters for wireless power transfer, and solidstate lighting drivers, among others. Grading will be based on 34 hw problem sets, 34 design problems, and a term long final project with topics, specifications, and milestones agreed upon by the instructor and by teams composed of up to two students. Grading for the final projects will include 1520 minute inclass presentations and short papers of each individual students contribution. [More Info]

Winter 2019: Quantum Information, Probability and Computing

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: Sandeep Pradhan
Prerequisites: Permission of instructor

Course Description:
Description: The failures of classical theories to explain important physical phenomena led to revolutionary and unprecedented changes in our thinking, and, in turn, to the developmentof quantum mechanics in the rst half of the twentieth century. It turns out that the laws of quantum mechanics lead to a new theory of probability (quantum probability) which is a non-commutative generalization of classical theory of probability. It was long believed that information processing and computing were solely mathematical constructs and as such were independent of nature and the laws of quantum mechanics. In the 1980s this assumption was found to be untrue, and the consequences have been profound. The introduction of quantum mechanics into communications and computation has produced new paradigms (quantum information) and some unforeseen results in the elds of computation, communications and learning. For example, quantum algorithms have now been found for factoring composite numbers (Shor's algorithms 1994). In contrast, there are no known practical (i.e., polynomial time) classical solutions for the problem. Moreover, recently quantum probability models have been proposed for human cognition to explain question-order-eects in polling and violations of rational decision theory. This course is an introduction to this general area. A basic working knowledge of linear algebra is a prerequisite, but no prior knowledge of quantum mechanics, classical computing or information theory is assumed. Graduate students in all areas of engineering, computer science,system theory, the physical sciences and mathematics should nd this material of interest.
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Winter 2019: Grid Integration of Renewable Energy Sources

Course No.: EECS 498-008
Credit Hours: 4 credits
Instructor: Ian Hiskens
Prerequisites: EECS 215 or EECS 314 or permission of instructor

Course Description:
The course will consider large-scale integration of renewable generation in electricity grids, with a focus on wind and solar (photovoltaic and thermal) technologies. Impacts of generation variability on grid operation and control will be addressed. Both local (voltage) and grid-wide (frequency regulation) effects will be considered, and methods for analysing these phenomena will be developed. Wind and solar forecasting will be reviewed. Approaches to accounting for renewable uncertainty in optimal generation dispatch will be evaluated. The course will explore the use of energy storage and load control for offsetting variability. The design and operation of renewable-based microgrid energy systems will also be investigated.
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Winter 2019: Green's Functions in Electromagnetics with Applications

Course No.: EECS 598-014
Credit Hours: 3 credits
Instructor: Leung Tsang
Prerequisites: EECS 530 or equivalent

Course Description:
Greens functions for problems in electromagnetics. Applications are in microwave remote sensing, signal integrity, EMC, metamaterials, photonic crystals, near field radiative transfer and Casimir force. Topics include: Dyadic Greens functions, cylinders, spheres, layered media, multiple scattering, Broadband Greens functions in waveguides and periodic structures, fluctuation dissipation theorem
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Winter 2019: Electrical Engineering Systems Design

Course No.: EECS 298-001
Credit Hours: 2 credits
Instructor: Jamie Phillips and Leland Pierce
Prerequisites: ENGR 100 and ENGR 101; co-requisite EECS 215

Course Description:
Most everything we encounter in the modern world relies on electrical systems ranging from obvious applications such as smartphones, computers, satellites, and the electricity grid to lessobvious applications such as transportation, medical devices and instruments, and even the sensor networks that ensure safe buildings and clean drinking water. How do todays amazingtechnologies in electrical engineering work together to achieve the complex requirements of a system? In this course, we will examine how concepts in electrical engineering (circuits, computing, control and decision making, sensors, embedded systems, optics, power, signal processing, and wireless communications) come together in electrical systems. The course will be laboratory based, and centered around the build of the subsystems of a 2-wheeled robot, and adapted in a design competition to address a societally-relevant challenge scenario.
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Winter 2019: Introduction to Embedded Systems Research

Course No.: EECS 598-013
Credit Hours: TBD
Instructor: Robert Dick
Prerequisites: A prior >= 400-level course on computer system or sensor design and analysis

Course Description:
This course is designed to prepare graduate and advanced undergraduate students with a foundation in, and head start on, research related to embedded system analysis, design, and synthesis. The rst half of the course consists of lectures and assigned reading material on fundamental embedded systems topics on which future research will generally build. The second half of the course focuses on a specic, and possibly new, topic in the eld. This semester, the focus is embedded machine learning in the Internet-of-Things (IoT).
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Winter 2019: Computer Science Education Research

Course No.: EECS 498-001 / EECS 598-010
Credit Hours: 3 credits
Instructor: Mark Guzdial
Prerequisites: Permission of instructor

Course Description:
What do people think goes on inside a computer?

How do people learn to program? Why is it so hard for so many? How can we make it easier?

Why should anyone learn programming, other than to become a software developer?

Why is there so little diversity in the Tech industry?

How do researchers get answers to these questions?

Researchers in computing education explore how people come to understand computation, and how to improve that process. In this course, we will review the literature on computing education, and learn the research methods used in the field. Students will read research papers, discuss and critique them, write summaries of research findings, design a research study, and prepare an NSF-style proposal.

Course Overview: Introduction to computing education research (CER). History and influential early work. Design of research studies in CER, including Multi-Institutional Multi-National, laboratory, and classroom studies. Grounding in theory from learning sciences, educational psychology, science education, andHCI.
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Winter 2019: Modeling Human Behavior

Course No.: EECS 498-003
Credit Hours: 4 credits
Instructor: Nikola Banovic
Prerequisites: EECS 281

Course Description:
This course will teach students methods to track, collect, and express human behavior data as computational models of behavior. The course will have a particular focus on computational approaches to describe, simulate, and predict human behavior from empirical behavior traces data. It will contrastcomputational modeling with other methodologies to understand human behavior and compare computational modeling with existing behavior modeling methodologies in Human-Computer Interaction (HCI). Short individual assignments will give students exposure to existing modeling methods in HCI. Large, group-based final project will give students an opportunity to push the boundaries of computational modeling in HCI by modeling behaviors of their choice from an existing data set to design and implement a novel Computational Modeling system from scratch.
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Winter 2019: GaN-Based Electronic Devices

Course No.: EECS 598-011
Credit Hours: 3 credits
Instructor: Elaheh Ahmadi
Prerequisites: EECS 320 or equivalent, or permission of instructor

Course Description:
GaN and its alloys with InN and AlN provide a wide range of direct bandgap (0.7 eV to 6.2 eV), which makes this material system suitable for optoelectronic applications such as laser diodes (LDs), light emitting diodes (LEDs), and solar cells. Besides, GaN has a large potential for high frequency and high-power electronics. Because of its high breakdown electric field in addition to high electron saturation velocity, GaN is a great candidate for high-power amplifiers and switches. In addition, GaN and its alloys with AlN and InN give the possibility of designing complicated heterostructures suitable for various lateral and vertical transistor structures such as high electron mobility transistors (HEMTs), hot electron transistors (HETs), current blocking vertical transistors (CAVETs), and trench MOSFETs.

In this class, we will review (Al, In, Ga)N material properties which make this material system so attractive for optoelectronics and electronics applications. We will then discuss two main epitaxial growth techniques, molecular beam epitaxy (MBE) and metal-organic CVD (MOCVD), utilized to grow (Al, In, Ga)N thin films. Ga-polar and N-polar HEMTs will be discussed in detail. Different designs of GaN-based vertical transistors for power applications will be covered.
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Winter 2019: Quantum Optoelectronics

Course No.: EECS 598-004
Credit Hours: 3 credits
Instructor: Mackillo Kira
Prerequisites: PHYSICS 240 and (EECS 320 or 334 or 434 or 520 or 540)

Course Description:
Optoelectronic devices are already being revolutionized by the prospects of quantum technology. Ever smaller and faster components will inevitably reach a level where a collective can outperform individual parts due to emergent quantum effects such as entanglement. This lecture welcomes you to the central concepts of quantum engineering of semiconductors to explore optoelectronic, quantum-optical, and many-body processes, relevant for state-of-the-art experiments and the future of quantum technology.

Rough Syllabus: This lecture will provide a pragmatic and brief introduction to solid-state theory, many-body formalism, semiconductor quantum optics, and lightwave electronics to explore pragmatic possibilities for quantum technology. To develop your insights on rational quantum design, the coupling of the quantized light field to electrons is investigated in detail, while the many-body Coulomb interaction of charge carriers is systematically included. For example, we will analyze which quantum effects and quasiparticles can be used in optoelectronics devices from sensors to quasiparticle accelerators. To extend these quantum ideas further, we will follow how including quantum fluctuations of light to laser spectroscopy will transform it to quantum spectroscopy, a new realm where dropleton, entanglement, quantum memory etc. effects can be systematically explored and utilized.
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Winter 2019: Motion Planning

Course No.: EECS 598-003
Credit Hours: 3 credits
Instructor: Dmitry Berenson
Prerequisites: Linear algebra (e.g. MATH 214) and significant programming (e.g. EECS 281)

Course Description:
Motion planning is the study of algorithms that reasonabout the movement of physical or virtual entities. These algorithms can be used to generate sequences of motions for many kinds of robots, robot teams, animated characters, and even molecules. This course will cover the major topics of motion planning including (but not limited to) planning for manipulation with robot arms and hands, mobile robot path planning withnon-holonomic constraints, multi-robot path planning, high-dimensional sampling-based planning, and planning on constraint manifolds. Students will implement motion planning algorithms in open-source frameworks, read recent literature in the field, and complete a project that draws on the course material.
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Winter 2019: Analysis of Electric Power Distribution Systems and Loads

Course No.: EECS 598-007
Credit Hours: 3 credits
Instructor: Johanna Mathieu
Prerequisites: EECS 463 or permission of instructor

Course Description:
This course covers the fundamentals of electric power distribution systems and electric loads. Most power system courses focus on analysis of transmission systems; however, with increased amounts of distributed generation (photovoltaics, small-scale wind), distributed storage, and controllable loads, it has become more and more important for researchers and power industry professionals to understand power distribution systems. We will start with an introduction to distribution grids, including their components, typical topologies, and operational strategies. We will then study power flow in distribution grids and distributiontransformers. Additionally, we will discuss the fundamentals of electric loads, including electric load modeling, analysis, and control methodologies. Course material will befrom a combination of textbooks and recent research articles in the field. In addition to technical topics, we will also discuss energy economics and policy related to distribution grids and loads. All students will conduct an individual research project on a topic related to the course material.
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Winter 2019: Advanced Analog and Mixed Signal Circuits

Course No.: EECS 598-015
Credit Hours: 4 credits
Instructor: Michael Flynn
Prerequisites: EECS 413 or equivalent

Course Description:
This 4-credit special-topics course will deal with advanced topics in analog and mixed-signal circuit design, beyond what is covered in EECS 511 and EECS 522. Topics include PLLs, serial links, filtering, mixed-signal computation and machine learning, as well as some material on data-conversion and power management. The course will be a mixture of faculty and student instruction. During the course, students will review present state-of-the-art material from journal and conference papers. There will also be guest lectures from industry experts.

There will be a small number of graded homeworks and CAD assignments. Students will also be graded on the in-class presentations and the follow-on review papers.
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Fall 2018: Artificial Intelligence Application in Electrical Engineering

Course No.: EECS 598-014
Credit Hours: 3 credits
Instructor: Jared Chaar
Prerequisites: See instructor

Course Description:
The core concepts of AI and their applicability in Electrical Engineering are covered. Topics include search techniques and heuristics, logic and reasoning, knowledge representation, advanced planning, decisionmaking under uncertainty, andmachine learning. Using a number of these techniques and open source (Python) AI APIs, students will work in teams to implement the control components of an electric system.
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Fall 2018: Topics in Hardware Security

Course No.: EECS 598-012
Credit Hours: 3 credits
Instructor: Daniel Genkin
Prerequisites: Prior experience in low level programming

Course Description:
The security of a system is only as good as its weakest link. Even if a system's software is perfectly secure, the complex interactions between the system's hardware and the physical world have not been properly understood. Side-channel attacks exploit unintentional, abstraction-defying leakage from physical devices (such as the device's power consumption, electromagnetic radiation or execution timing variations) to recover otherwise-unavailable secret information. In this class, we shall review recent papers in the area of side channel attacks and their mitigations.

Specific topics include (but not limited to):1. Physical side channel attacks such as power and electromagnetic analysis2. Microarchitectural attacks such as cache attacks, and Rowhammer3. Speculative execution attacks: Spectre, Meltdown and Foreshadow4. Side channel mitigations and countermeasures
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Fall 2018: Engineering Interactive Systems for HCI

Course No.: EECS 598-013
Credit Hours: 3 credits
Instructor: Alanson Sample
Prerequisites:

Course Description:
Recent advances in the fields of Human-Computer Interaction and Ubiquitous Computing have focused on creating innovative devices and methods for user interaction, new ways of displaying information, and novel methods of sensing and understanding the state of users and their environment. This course will focus on both, reviewing the state-of-the-art of interactive systems and the technologies that enable them, as well as teaching the skills necessary to actually build these research prototypes.

Classroom instruction will focus on a review of current research topics and literature in technical HCI areas including interactive technologies, augmented reality, haptics, wearables, shape-changing interfaces, and more. Homework assignments will take the form of mini-projects designed to build hands-on skills in the use of laser cutters, 3D printers, sensing and signal acquisition circuits, embedded systems, PCB design, and machine learning for event and activity recognition. The class will culminate in a final project where teams of students will pitch, build, and demo a self-defined project using the skills developed in this course. In lieu of purchasing a course textbook, students will be expected to buy a lab kit.
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Fall 2018: Election Cybersecurity

Course No.: EECS 498-009
Credit Hours: 4 credits
Instructor: J. Alex Halderman
Prerequisites: See instructor

Course Description:
Elections, the foundation of democracy, are increasingly subject to electronic attacks. Manipulation of social media, hacks against campaigns, and vulnerabilities in voting equipment create unprecedented risks.

This new course will examine the past, present, and future of election security, informed by perspectives at the intersection of computer science, law and public policy, politics, and international affairs.

We will study how elections can be attached and work to help defend them, using a broad range of technical and public policy tools.
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Fall 2018: Computational Modeling in Human-Computer Interaction

Course No.: EECS 598-011
Credit Hours: 3 credits
Instructor: Nikola Banovic
Prerequisites: Programming experience in Java, Python, MATLAB or R

Course Description:
This seminar course will review current computational approaches to describe, simulate, and predict human behavior from empirical behavior traces data. It will contrast computational modeling with other methodologies to understand human behavior and compare computational modeling with existing behavior modeling methodologies in HumanFComputer Interaction (HCI). Short assignments will give students exposure to some of the cuttingFedge methods, while the final project will give them an opportunity to push the boundaries of computational modeling in HCI by modeling behaviors of their choice from an existing data set.
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Fall 2018: Discover Computer Science

Course No.: EECS 198-001
Credit Hours: 1 credit
Instructor: Rada Mihalcea
Prerequisites:

Course Description:
Interested in Computer Science? Heard about programming but not really sure how it works? Discover Computer Science!
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Fall 2018: Plasma Chemistry and Plasma Surface Interactions

Course No.: EECS 598-007
Credit Hours: 3 credits
Instructor: Mark Kushner
Prerequisites: See instructor

Course Description:
Low temperature plasmas are used for materials and microelectronics proc-essing, plasma aided combustion, lighting, lasers and medicine. This course will address the plasma initiated chemistry and plasma surface interactions of these systems. Electron impact, ion-molecule and excited state reactions, radiation transport; and the reaction of these species with inorganic, organic and liquid surfaces will be discussed.
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Fall 2018: Analysis of Societal Networks

Course No.: EECS 444
Credit Hours: 4 credits
Instructor: Vijay Subramanian
Prerequisites: EECS 301, MATH 425 or STATS 425, C or better for prerequisites

Course Description:
Networks are everywhere. We encounter a variety of networks of different sizes and forms on a daily basis: societal networks such as the network of retweets of a certain hashtag on Twitter or the friends network on Facebook; technological networks such as the Internet with the telecommunication network of computers, the links between webpages, the groupings of users generated by recommendation systems for predictions or the network of users on BitTorrent downloading a specific file; and economic networks such as trade networks or supply-chain networks. Some of these networks emerge naturally such as many societal networks, while others are planned such as the public transportation or road network. We depend on the efficient functioning of these networks to transact many of our activities. This course serves as an introduction to the broad class of networks described above: how these networks are connected, how they form, how processes and transactions take place on them, and how they are being transformed and interconnected in the modern world. Students will learn how to develop and apply mathematical models and tools from graph theory, linear algebra, probability and game theory in order to analyze network processes such as how opinions and fads spread on networks, how sponsored advertisements are developed, how web content is displayed, how recommendation systems work, etc.
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Fall 2018: Electromagnetic Metamaterials

Course No.: EECS 598-008
Credit Hours: 3 credits
Instructor: Anthony Grbic
Prerequisites: EECS 330 or permission of instructor

Course Description:

The course will present a detailed introduction to electromagnetic metamaterials. The field of metamaterials is an emerging area and limited resources are available to students that wish to learn about this research area. Textbooks and graduate courses on the subject matter are scarce. Therefore, the student is left to learn from research papers scattered throughout numerous journals. This course is offered in response to this growing need.

The course covers engineered structures possessing tailored electromagnetic properties, or properties that are difficult or impossible to achieve using conventional materials. The course content includes classical microwave structures like periodically loaded transmission lines and waveguides, corrugated surfaces, wire arrays, as well as more recent structures such as high impedance surfaces and metasurfaces, electromagnetic bandgap structures, negative refractive index and artificial magnetic media. Optical structures including photonic bandgap materials and metal-dielectric plasmonic media are also covered. The course allows students to develop an intuitive understanding of the electromagnetic response of various structures through exact and approximate methods. Periodic analysis, effective medium theories, and distributed circuit concepts are utilized to gain understanding.
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Fall 2018: Infrastructure for Vehicle Electrification

Course No.: EECS 598-001
Credit Hours: 3 credits
Instructor: Ian Hiskens
Prerequisites: EECS 215 or EECS 314

Course Description:
The course covers the fundamentals of the physical and cyber infrastructures that will underpin large-scale integration of plug-in electric vehicles (EVs). EV charger technology will be examined, with a particular focus on grid-side characteristics. V2G converter requirements will be considered. An overview of the design and operation of power systems will be provided. This will form the basis for a detailed examination of grid integration issues arising from large-scale charging and fast charging strategies. Quality-of-supply issues and protection requirements will be addressed. The information infrastructure and regulatory framework required to support various business models for flexible EV charging will be presented. Control strategies for coordinating large-scale EV charging will be developed. Upon completion of the course, students should have a comprehensive knowledge of the structure, capabilities and limitations of the physical and cyber infrastructures required to support large-scale EV integration.

Syllabus:1. Power system overview: Distribution supply systems; Reliability; Protection; Impact of high EV penetration; Fast charging; Vehicle-to-grid integration.2. Vehicle-grid interface: Grid-to-vehicle and vehicle-to-grid converter technologies; Standards; Safety systems; Quality-of-supply; Information transfer.3. Business models for ubiquitous charging facilities: Cyber-infrastructure requirements for supporting smart/dumb charging.4. System-wide control of charging: Time-based and price-based load shifting strategies; Optimal control of EV charger demand; Hierarchical control structures; EV control for supporting renewable generation.
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Fall 2018: Quantum Nanotechnology

Course No.: EECS 498-003
Credit Hours: 3 credits
Instructor: Duncan Steel
Prerequisites: MATH 215, MATH 216, PHYSICS 240 and co-req of EECS 230 or equivalent

Course Description:
The development and application of nano-technology is impacting nearly all the fields of engineering, from those who are developing it to those who use it. Future engineers working to design new devices will need a skill set that is considerably broadened to include the behavior of materials and devices when they becomesufficiently small. Devices like transistors and quantum well lasers have already forced engineers to understand the impact of Fermi-Dirac statistics and energy quantization on devices. However, the emergent field of nano-technology is revealing that the concepts we have from our current scale devices are no longer adequate to predict correct device experience. Moreover, in this new regime, new physical properties are emerging that may revolutionize how we think about information, its storage, transmission and processing. This course introduces students to basic concepts that are relevant to novel device concepts. The course will explore the new properties of nano-vibrators, quantum LC circuits, the role of loss, the impact of the quantum vacuum on nano-switches, coherent superposition, quantum entanglement, light (one photon at a time) and quantum information and computing. You will learn a new way to think about how things work.
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Fall 2018: Reinforcement Learning (RL)

Course No.: EECS 498-006 and EECS 598-006
Credit Hours: 3 credits
Instructor: Satinder Baveja
Prerequisites: See instructor

Course Description:
This course will be a fast-paced programming-based introduction to both the fundamentals of Reinforcement Learning (RL) as well as some of the recent advanced and exciting ideas at the intersection of Deep Learning and RL (or DeepRL)
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Fall 2018: Power Semiconductor Devices

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Becky Peterson
Prerequisites: EECS 320 or equivalent

Course Description:
Power devices are at the heart of all modern electronics, from the power grid and renewable energy to hybrid/electric vehicles, trains, space exploration, and industrial and consumer electronics. This course will cover design and operating principles of semiconductor devices for discrete and integrated power electronics. We will discuss the power MOSFET, IGBT, HEMT, thyristors, Schottky and PIN diodes, as well as emerging devicearchitectures. We will study the semiconductor materials, device fabrication and packaging required for power devices, including Si, GaN, SiC, and Ga2O3. Students will learn numerical device modeling using commercial software (Synopsys Sentaurus and Silvaco Atlas), and will do a final group presentation on a topicof their choice.
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Fall 2018: Data Mining

Course No.: EECS 498-001
Credit Hours: 4 credits
Instructor: Danai Koutra
Prerequisites: EECS 281 or graduate standing in CSE

Course Description:
Unprecedented amounts of data are being generated daily everywhere -- on the web, social networks, mobile apps, supermarket transactions, movie and music services, traffic sensors, smart home devices, healthcare, and more. Methods for extracting nuggets of information from mountains of data are transforming the world: data-driven approaches are changing thescientific and decision-making processes and solving various societal problems. This course covers the fundamental concepts in data mining, focuses on methods and algorithms and, at thesame time, aims to equip the students with practical skills for mining of large-scale, real data. The topics that will be covered include big data systems, frequent itemsets, similarity and clusteranalysis, mining of networks / time series / data streams, and applications, such as recommendation systems, social network analysis and web search.
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Fall 2018: Green Photonics

Course No.: EECS 598-004
Credit Hours: 3 credits
Instructor: Zetian Mi
Prerequisites: EECS 429 or equivalent

Course Description:
Energy, water, and environmental sustainability are among the most critical challenges we face in the next decades. Green Photonics is concerned with the application of semiconductor optoelectronics including light sources, detectors, and photovoltaic devices to these problems. The most familiar photonic technologies in this field are solar cells and LED lighting, which have had an enormous and growing impact over the past few decades. The course will cover the fundamentals of semiconductor photonic materials and devices, as well as new frontiers in green photonics, including integrated nanophotonic circuits and solar fuels. Important topics to be discussed include: solar cells, solar-to-hydrogen conversion, energy efficient nanophotonic devices including LEDs, lasers, and micro/nanoscale devices, as well as integrated nanophotonics.
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Fall 2018: Computational Data Science

Course No.: EECS 598-003
Credit Hours: 3 credits
Instructor: Raj Nadakuditi
Prerequisites: Programming experience in MATLAB, C, C++, Python or R

Course Description:
See attached flyer
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Fall 2018: Computer Hardware Design for Machine Learning

Course No.: EECS 598-005
Credit Hours: 3 (or 4 with an optional project)
Instructor: Zhengya Zhang
Prerequisites: EECS 427 or EECS 470

Course Description:
Machine learning has evolved rapidly in the last decade and it has become ubiquitous in applications from smart devices to self-driving cars. A key enabler of modern machine learning is the availability of low-cost, high-performance computer hardware, such as graphics processing units (GPUs) and specialized accelerators such as Googles tensor processing unit (TPU). New machine learning applications constantly impose new requirements and constraints on the hardware design. Hardware implementations must fit increasingly stringent area and power envelope. This course will survey the latest architecture and circuit designs for machine learning applications. Paper reviews and presentation will be the essential parts of this course. An optional unit can be earned by benchmarking or prototyping selected designs that leads to insightful conclusions.
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Fall 2018: Introduction to Algorithmic Robotics

Course No.: EECS 498-005
Credit Hours: 3 credits
Instructor: Dmitry Berenson
Prerequisites: EECS 280 is required, EECS 281 and MATH 214 are recommended

Course Description:
Build the foundation for your future in robotics:

-Convex Optimization-Motion Planning-Grasping-Point Cloud Processing-Probabilistic Reasoning-Kalman and Particle Filters
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Winter 2018: Organic Electronic Devices and Applications

Course No.: EECS 598-001
Credit Hours: 3 credits
Instructor: Stephen Forrest
Prerequisites: Senior level quantum mechanics, junior level electronic devices

Course Description:
Today, there is a revolution in optoelectronics: OLED displays are used in billions of smart phones, televisions, tablets and smart watches worldwide. They are now coming into use in lighting for both residential and automotive applications. Organic solar cells are achieving 15% efficiencies, bringing them to the cusp of generating a new, ultralow cost renewable energy source. Contemporaneously, the fundamental understanding of organic semiconductors used in these emerging applications has been a subject of intense study for over 70 years, and in many cases is still not fully understood. In this course, we will trace the history, science and modern applications of organic electronic technology. Since some students have taken the first course on this topic in W17, only the first few weeks of the course will provide the fundamental physics of organics primarily as a review. This will include the basics of the optical and electrical properties of organic semiconductors. Next, we will discuss how organics are deposited and patterned to achieve thin film device structures. The bulk of the class material is concerned with device physics, engineering and applications. In particular light emission from OLEDs, their various structures and adaptations for high efficiency displays and lighting will be discussed. This is followed by a treatment of organic thin film transistor physics and applications for sensing, medical applications etc. The course is concluded by a comprehensive treatment of organic solar cells: their status, efficiency limits, reliability, as an energy harvesting technology will be described.
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Winter 2018: Control and Modeling of Power Electronics

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Al Avestruz
Prerequisites: Familiarity with classical control concepts

Course Description:
Transformative technologies in energy conversion will be smarter, faster, and more reliable. This class will address the control and modeling of acdc, dcac, and dcdc power electronic systems. Topics include smallsignal models; digital and analog control; switched, sampleddata, and averaged models; large signal considerations; distributed power conversion; computer modeling in PLECS, MATLAB/Simulink, and LTSpice; and other advanced topics. Design cases may include audio switching power amplifiers, peak power point tracking for renewables and energy scavenging, resonant converters for wireless power transfer, power factor correction, and grid connected converters among others.
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Winter 2018: Multidisciplinary Capstone Design Project - Supplemental Information

Course No.: EECS 498-006 and EECS 498-007
Credit Hours: 3 or 4 credits
Instructor: Jay Guo and Hun Seok Kim
Prerequisites:

Course Description:
See attached PDF
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Winter 2018: Multidisciplinary Capstone (MDE) Design Pilot

Course No.: EECS 498-005
Credit Hours: 3 or 4 credits
Instructor: Brian Gilchrist
Prerequisites:

Course Description:
EECS students, together with ME and MSE students, work on common, interesting, significant major design experience (MDE) projects. This pilot course is about providing students real-world, multidisciplinary design project opportunities to satisfy their MDE requirement and for ECE masters students interested in meaningful project experiences.

For WN18, we expect to have several projects with application focus in biomedical, energy, spaceflight, and other areas needing EECS students (e.g. sensor/electronics, embedded systems, controls, and wireless). Please contact Prof. Gilchrist with questions.
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Winter 2018: Mining Large-scale Graph Data

Course No.: EECS 598-008
Credit Hours: 4 credits
Instructor: Danai Koutra
Prerequisites: Basic knowledge of linear algebra, programming, and machine learning

Course Description:
Graphs naturally represent information ranging from linksbetween webpages to friendships in social networks, tocollaborations between coauthors and connections betweenneurons in our brains. These graphs often span billions of nodesand interactions between them. Within this deluge of interconnected data, how can we extract useful knowledge,understand the underlying processes, make interesting discoveries, and contribute to decision-making?

This course will cover recent methods and algorithms foranalyzing large-scale graphs, as well as applications in variousdomains (e.g., neuroscience, web science, social science,computer networks). The focus will be on scalable and practicalmethods, and students will have the chance to analyzelarge-scale datasets. The topics that we will cover includeclustering and community detection, recommendation systems,similarity analysis, deep learning, summarization, and anomalydetection in the graph setting.
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Winter 2018: Social Computing Systems

Course No.: EECS 498-001
Credit Hours: 4 credits
Instructor: Walter Lasecki
Prerequisites: EECS 485 or EECS 493 or permission of instructor

Course Description:
Computation rarely exists in isolation. From social media, to collaboration and coordination tools, to crowdsourcing and collective intelligence, technology has risen from use as an individual tool for focused domains to play a role in or even mediate a majority of social interactions today. Social Computing is the study of this interplay between social processes and the computation that supports and augments them. This course will cover topics including collaborative systems, social media, systems for supporting collective action, data mining and analysis, crowdsourcing, human computation, and peer production.
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Winter 2018: Randomness in Computation

Course No.: EECS 598-010
Credit Hours: 3 credits
Instructor: Christopher Peikert
Prerequisites: EECS 376 or EECS 477

Course Description:
Randomness and the tools or probability theory have proven central in many areas of modern science, and especially in computing and the design and analysis of algorithms. This course will expose students to a wide variety of randomized algorithms and the main techniques (linearity of expectation, the second moment method, Chernoff bounds, martingales, and the probabilistic method) used to analyze them. The course also will explore applications of these tools to analyze random combinatorial objects and deterministic algorithms applied to random inputs drawn from some distribution.

Advanced topics may include: the Lovasz Local Lemma, Talagrands inequality, streaming algorithms, quantum computation, approximation algorithms, semidefinite programs, probabilistic proof systems, cryptographic protocols, and others. (The choice of advanced topics will depend on the interests of the students and instructor.)
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Winter 2018: Optics and Quantum Spectroscopy of Semiconductors

Course No.: EECS 598-004
Credit Hours: 3 credits
Instructor: Mack Kira
Prerequisites: PHYSICS 240 and (EECS 320 or 334 or 434 or 520 or 540)

Course Description:
Rough Syllabus: This lecture will provide a pragmatic and brief introduction to solid-state theory, many-body formalism, and semiconductor quantum optics to explore pragmatic possibilities for nanotechology. As a central theme, the coupling of the quantized light field to electrons is investigated in detail, while the many-body Coulomb interaction of charge carriers is fully included. In this context, we will analyze which quantum effects and quasiparticles optical experiments can detect and control in terms of excitonic effects, plasmonics, quasiparticle accelerators, and ultrafast spectroscopy. To extend the quantum ideas further, we will follow how including quantum fluctuations of light to laser spectroscopy will transform it to quantum spectroscopy, a new realm where dropleton, entanglement, quantum memory etc. effects can be explored.
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Winter 2018: Motion Planning

Course No.: EECS 598-003
Credit Hours: 3 credits
Instructor: Dmitry Berenson
Prerequisites: Linear algebra (e.g. MATH 214) and significant programming experience (e.g. EECS 281)

Course Description:
Motion planning is the study of algorithms that reason about the movement of physical or virtual entities. These algorithms can be used to generate sequences of motions for many kinds of robots, robot teams, animated characters, and even molecules. This course will cover the major topics of motion planning including (but not limited to) planning for manipulation with robot arms and hands, mobile robot path planning for non-holonomic constraints, multi-robot path planning, high-dimensional sampling-based planning, and planning on constraint manifolds. Students will implement motion planning algorithms in open-source frameworks, read recent literature in the field, and complete a project that draws on the course material.
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Winter 2018: Patent Fundamentals

Course No.: EECS/ENGR 410
Credit Hours: 4 credits
Instructor: Mohammed Islam
Prerequisites: Open to all students

Course Description:
Have you ever had a great idea, then discovered that someone else was using it? Do you wish you could protect your inventions? Learn how to get a patent and protect your rights. In this course, you will write your own patent application and learn how to shepherd it through the Patent Office. The basics of Patent Law are covered, including patentable subject matter, novelty, obviousness, specification and claims of a patent, and claim drafting. Both patent prosecution and litigation topics are covered. This course is open to all undergrad and grad students -- technical background not required.
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Winter 2018: Internet Foundations

Course No.: EECS 498-002
Credit Hours: 2 credits
Instructor: Mohammed Islam
Prerequisites: MUST BE TAKEN PASS/FAIL

Course Description:
This course introduces students to the fundamentals of the internet. You use the internet every day, and in this course we permit you to look under the hood to see the basics of how the internet works. The course is specifically intended for students who do not specialize in computers or computer science. We start by reviewing the differences between various applications, such as world wide web, skype, and Bit-Torrent. The 4-layer internet model will be explained, which includes the application, transport, network and link layers. Application layer examples include WWW, HTTP, email, DNS and P2P Applications. The two most commonly used Transport Layer protocols are TCP and UDP. The Internet uses IP as the Network Layer, and routers perform the IP layer functions. The Link Layers used most commonly include Ethernet (wired) and IEEE 802.11 or WiFi (wireless). Other topics covered briefly include Wireless and Mobile Networks, Software Defined Networks, Data Center Networks and Network Security. By taking this course you will have a better appreciation of how computer networks work and how your computer communicates over the internet.
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Winter 2018: Power System Markets and Optimization

Course No.: EECS 598-007
Credit Hours: 3 credits
Instructor: Johanna Mathieu
Prerequisites: EECS 463 or permission of instructor

Course Description:
This course covers the fundamentals of electric power system markets and the optimization methods required to solve planning and operational problems including economic dispatch, optimal power flow, and unit commitment. The course will highlight recent advances including convex relaxations of the optimal power flow problem, and formulations/solutions to stochastic dispatch problems. Problems will be placed in the context of actual electricity markets, and new issues, such as incorporation of renewable resources and demand response into markets, will be covered. All students will conduct an individual research project.
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Winter 2018: Network Information Theory

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: Sandeep Pradhan
Prerequisites: EECS 501 or equivalent

Course Description:
With the emergence of numerous applications, such as 5G and IoT, involving different types of communication networks, such as packet-switched networks, wireless sensor networks and mobile cellular wireless networks, there has been a significant interest in obtaining a deeper understanding of transmission, storage and processing of information in these networks.

Network information theory deals with information in communication networks, i.e., obtaining optimal performance limits as well as ecient information processing strategies to achieve these limits in such networks. A communication network is modeled as a system involving many transmitters and receivers working with many information sources and channels. There have been several exciting new developments in the recent past in this area.
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Fall 2017: Self-Driving Cars: Perception & Control

Course No.: EECS 498-009
Credit Hours: 4 credits
Instructor: Matthew Johnson-Roberson
Prerequisites: Programming skills in Python & MATLAB, Some C++

Course Description:
This course will teach the theoretical underpinnings of self-driving car algorithms and the practical application of the material in hands-on labs. Highlights will include field trips to M-City, a 32-acre autonomous vehicle site on the U's North Campus, demos and rides in full size autonomous vehicles, and small group work with a competition where students test their own self-driving car algorithms.
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Fall 2017: EECS 598-004 Laser Plasma Diagnostics

Course No.: EECS 598-004
Credit Hours: 3 credits
Instructor: Louise Willingale
Prerequisites: EECS 537 or permission of instructor

Course Description:
High power laser pulses are used to both create and diagnose high-energy density systems. In this course, we will discuss the techniques used for creating, characterizing and timing high power laser pulses from megajoule-nanosecond pulses to relativistic-intensity femtosecond pulses. We will explore the diagnostics used to characterize high-energy density plasmas through optical and other radiation measurements as well as backlighting techniques. Other important aspects of performing experiments, such as target positioning techniques, will be touched on. In addition to the material discussed in lectures, students will consider real experimental data and recent research publications to learn analysis techniques, gain appreciation for physical limitations (such as instrument resolution and background signals), and comparison with theoretical models. This course is suitable for graduate students studying plasma physics, optics and laser science and other related areas.
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Fall 2017: VLSI for Signal Processing and Communication Systems

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: Hun-Seok Kim
Prerequisites: See instructor

Course Description:
This course will survey methodologies to design energy efficient and/or high-performance VLSI systems for the state-of-the-art image/audio processing, machine learning, and wireless communication systems. The primary focus of the course is on designing hardware efficient algorithms and energy-aware VLSI IC architectures to deliver the performance and efficiency requiredby various signal processing applications. The course will be a mix of lectures and student-led presentations/projects. The content will be suitable for senior undergraduates or graduate students interested in hardware-efficient signal processing algorithms andtheir VLSI implementations.
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Fall 2017: Quantum Nanotechnology

Course No.: EECS 498-003
Credit Hours: 4 credits
Instructor: Duncan Steele
Prerequisites: MATH 215/216, PHYSICS 240, co-req of EECS 230

Course Description:
This course aims to introduce students to basic concepts in quantum physics that are relevant to novel device concepts.
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Fall 2017: Power System Dynamics and Control

Course No.: EECS 598-008
Credit Hours: 3 credits
Instructor: Ian Hiskens
Prerequisites: EECS 463 or permission of instructor

Course Description:
This course will introduce angle and voltage stability concepts and consider control strategies for improving dynamic performance. It will provide an overview of nonlinear dynamical systems, including geometrical properties of solutions, Lyapunov methods for approximating the region of attraction, and bifurcation analysis.
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Fall 2017: Beyond CMOS: Emerging Nanotechnologies

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Becky Peterson
Prerequisites: EECS 320 or graduate standing

Course Description:
This course will survey the devices, circuit architectures, and integration challenges facing the semiconductor industry in the "More than Moore" era, using a mix of lectures, discussions, and student-led projects. The content will be suitable for junior/senior undergraduates or graduate students interested in IC design/VLSI or solid state materials and device/nanotechnology.
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