New Course Announcements

“First comes thought; then organization of that thought, into ideas and plans; then transformation of those plans into reality. The beginning, as you will observe, is in your imagination.” – wrote Napoleon Hill, an entrepreneur, author of self-help books, and a conman, in the early 20th century. His point was that innovation requires a certain action-oriented but critical mindset for success and that starting on that journey can often be the hardest part. Whether trying to innovate inside an organization, working on an open source project, or attempting to get a startup off the ground, the tools and techniques discussed in this class will be applicable to increase the chances of success. Students will be expected to propose and critically evaluate project ideas, form groups, and execute autonomously to achieve objectives. The groups will report directly to the “general manager” (faculty), with biweekly project meetings.

This course will provide the students with the foundation knowledge to understand the development of this rapidly evolving field, leading to the discussion of new technologies. We will address how the mysterious quantum phenomena are brought to real world realizations that will further advance our knowledge. After introducing the founding principles of quantum science and quantum information, the second half of the semester will focus on photonic realization because of its appeal in delivering near-term quantum technologies that would create far-reaching societal impacts. By the end of the course, the students will grasp a fundamental understanding for quantum information and be able to bridge quantum physical phenomena and new technologies for communication, sensing, and computing.

This class will introduce you to the ways in which applications of computing affect social institutions and how these social consequences produce questions about how to conceptualize, critique, and ensure our all-too-human values in computing.

This class will introduce you to the key concepts and the state-of-the-art in practical, scalable, and fault-tolerant software systems for emerging Generative AI (GenAI) and encourage you to think about either building new tools or how to apply an existing one in your own research.

This course will study the theoretical foundations of machine learning. We will present the frameworks and rigorously analyze some of the most successful algorithms in machine learning that are extensively used. The course will prepare students for thinking rigorously about machine learning and doing research in a relevant area.

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.

his course explores the theoretical and practical limitations of machine learning algorithms, such as computational complexity, data quality and quantity. The course covers both classical and modern results in algorithmic learning theory, as well as recent advances and challenges in deep learning. The course also discusses the implications of these limitations for the design and deployment of machine learning systems in various domains.

See link below for more information.

See link below for more information.

See link below for more information.

This course is focused on describing the technologies available or envisioned that can replace fossil fuels with alternative renewable sources of energy.

During this course, you will learn how to formally specify a system’s behavior, how to prove that the high-level design of the system meets that specification and finally how to show that the system’s low-level implementation retains those properties. The course does not assume any prior knowledge in formal verification. We will start from the basics of the Dafny language and build from there. In the end, you should be able to design and prove correct a complex system. The experience of doing so will make you a more careful and effective programmer, even when you don’t write formally verified code.

This course will primarily focus on learning and research at the intersection of quantum and classical computing.

The course will cover several im-portant algorithms in data science and demonstrate how their performances can be analyzed. While fun-damental ideas covered in EECS 376 (e.g., design and analysis of algorithms) will be important, some topics will introduce new concepts and ideas, includ-ing randomized dimensionality reduction, sketching algorithms, and optimization algorithms (e.g., for training machine learning models).

This course will introduce students to the quantum theory of electromagnetic radiation, matter and their interactions, which underpins all new quantum technologies.

Quantum computing, should current technical barriers be overcome, makes bold promises to revolutionize key applications including cryptography, machine learning, and computational physics. This course will explore the potential impact and limitations of this paradigm shift from a computer science perspective. Lectures will cover the bare physics and mathematics needed to investigate how each layer of the computing stack (logic, system architecture, algorithm, and application design) is impacted. Labs and programming assignments will provide students a hands-on approach towards writing quantum programs, simulating their execution, deploying them to real quantum hardware available on the cloud, and analyzing their performance.

Description: With a number of quantum machines already available to researchers and scientists, the big question is when and how these machines will find their way into the mainstream. The challenge lies in improving these systems to be large enough, fast enough, and accurate enough to solve problems that are intractable for classical computers. This course will primarily focus on architectural and microarchitectural advancements that pertain to quantum error correction and control. In that regard, we will review recent literature on these topics, identify challenges that remain unsolved, and investigate potential solutions.

This is a multidisciplinary class that covers the optical and electronic properties of a wide range of solution-processed semiconductors, including inorganic nanomaterials, hybrid organicinorganic metal halide perovskite, conjugated polymer, etc.

This course introduces the fundamental concepts of causality, and causal inference using machine learning models. Topics will include:counterfactuals (potential outcomes and graphs), identification and estimation of conditional average treatment effects from randomized control trials and observational data, as well as causal inference under hidden confounding and limited overlap. 

This is a proof-based course that lies at the intersection of algorithms and graph theory. We will tour through some classic algorithms and cutting-edge work in the area of network design. Topics will include distance oracles, spanners, emulators, preservers, shortcut sets, hopsets, algorithmic applications of these objects, and methods for making these objects tolerant to temporary failures in a network.

The aim of the course is to develop the key concepts of quantum computing and information as well provide hands-on quantum programming skills (Qiskit platform). 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 find this material of interest.

 AI is transforming many industries and has caused an explosion of applications. Areas that have been affected by ML and deep learning include self-driving cars, speech and image recognition, effective web searching, fraud detection, human genome analysis, and many other advances. Knowledge of AI, ML and Deep Learning is becoming a must for any engineer or scientist. This course is intended to give you exposure to the underlying theory and language. This is an introduction for non-experts, and it will enable you to go onto other AI, ML and deep learning courses offered in various departments. 

 EECS 298-051, Fall 2023, will be a seminar-type course with presentations by the instructor and invited speakers. The goal of this course is two-fold. First, the understanding of how human activities, from electricity generation to transportation, construction, agriculture, and heating/cooling, contribute to the release of greenhouse gases (GHG) in the Earth’s atmosphere. Second, the study of current and prospective engineering solutions for reducing and potentially eliminating GHG emissions, with several presentations by UM experts.