Recently Introduced Classes and Topics in Electrical & Computer Engineering

14:332:436:06 & 16:332:579:06 Biomedical Technologies: Design and Development

Taught by Professor Umer Hassan

This is an interdisciplinary course which encompasses fields of micro-nano sensing, bioelectrical engineering, microfluidics, biomedical devices and life sciences. This course is geared towards providing a unique learning and training experience to undergraduate and graduate students in the area of biomedical technologies. The course will provide a detailed background on scientific and engineering principles employed for developing biomedical devices for various biological applications. Fabrication and characterization of the point-of-care biotechnologies will be taught as well. The course will also introduce students to a step wise design process of biotechnology development. In a class project, students will work in the groups of 2-3 and will design an “idea biomedical device” applicable for a specific application. Biomedical technologies taught in the course will be around the operational principles of microfluidics, BioMEMS, multi biosensing modalities, surface functionalization, mathematical modeling and on-chip sample processing; the concepts which will be taught throughout the course.

Course outline for Personalized Biosensors for Global Health

14:332:493:05 & 16:332:579:04 Quantum Computing Algorithms

Taught by Professor Emina Soljanin

This course provides an introduction to the theory of quantum computing and information. The covered topics include 1) the fundamental elements of quantum information processing (qubits, unitary transformations, density matrices, measurements), 2) entanglement based communications protocols (e.g., teleportation) and games (e.g., CHSH), the Bell inequalities, 3) quantum algorithms such as Shor’s factoring and Grover’s search, and 4) basic (quantum) error correction algorithms. The course material will be accessible to undergraduate and graduate students with a variety of backgrounds, e.g., electrical engineers, physicists, mathematicians, and computer scientists.

Course outline for Quantum Computing Algorithms

14:332:493:06 & 16:332:579:07 Machine Learning for IoT

Machine learning for Engineers covers various topics in machine learning, with an emphasis on fundamental statistical technique and programming. While the course will motivate the covered material through the use of various engineering applications, being an engineering student (or a particular major within engineering) is not a pre-requisite for enrollment. However, enrolled students must have taken undergraduate courses in probability theory and linear algebra. The course will also require extensive programming for reinforcement of concepts introduced in the course. In keeping with the industry standards, all programming will need to be done in notebooks [e.g., Jupyter (http://jupyter.org/) and Google Colab (https://colab.research.google.com/)] using either Julia, Python, or R (individual students will get to pick any one of these languages in most assignments). In many instances, students will be forbidden from using popular machine learning packages such as scikit-learn for assignments.

14:332:436:02 (16:332:519:02) Personalized Biosensors for Global Health

Taught by Professor Umer Hassan

This course provides a detailed background on the engineering principles used for biosensing applications in disease diagnostics, and therapeutics for global health. Fabrication and characterization of the point-of-care biosensors will be taught. The course will also introduce students to the microfluidics principles, on-chip sample processing, surface functionalization techniques and label-free detection of biomolecules. Course will highlight the development of personalized predictive systems for global health care using machine learning techniques. Course will also include case studies of POC sensors for global health-care. Finally, students will work in groups of 2-3 and will do a project on a personalized biosensor design for a specific global health application.

14:332:446:04 & 16:332:579:04 Hardware and System Security

Taught by Professor Sheng Wei

This course focuses on introductions and research discussions on hardware and system security. We will review and discuss the state-of-the-art practices and research efforts on hardware and system attacks and the effective countermeasures, in order to motivate research interests and new insights in building secure and trustworthy hardware systems. In addition, we will discuss how the advances in hardware security technologies can provide fundamental support and enhancement for software and system security. In particular, we will explore the interesting connections between hardware security and other system and application domains, such as multimedia systems, mobile computing, cloud computing, big data analytics/visualization, and Internet of things. Finally, we will conclude the course by looking into the research topics related to end user experiences while interacting with the secure hardware systems, such as fraud/spam detection and usable security.

14:332:435:04 & 16:332:579:06 Energy Efficient Machine Learning Systems

Taught by Professor Bo Yuan

Machine learning has emerged as the critical technique in a massive amount of artificial intelligence-demanded scenarios. From the view of practical deployment, design energy-efficient machine learning systems, especially the state-of-the-art deep learning system, is particularly important due to the high computation and storage requirement. This course will introduce and discuss various types of approaches, ranging from high-level algorithm to hardware architecture to underlying circuits, to address the emerging energy challenge for machine learning system design.