New Classes for Fall 2019 Semester
14:332:436:06 (16:332:579:06) Biomedical Technologies: Design and Development
Taught by Professor Umer Hassan
This course is geared towards providing an individualized learning and training experience based on didactic and Socratic approaches to senior undergraduate and graduate students in the area of designing and implementing the biomedical technologies for Global health applications. The course will introduce students to a step-wise design process of biotechnology development and students will develop/ implement the specific biotechnology during the course. Students will work in groups of 2-3 and will do a project on development of a personalized biosensor/ technology for a specific biomedical application. Biomedical technologies will utilize the microfluidic principles, different biosensing modalities (electrical, optical, acoustic, etc.), surface functionalization, mathematical modeling and on-chip sample processing. Course outline for Personalized Biosensors for Global Health
14:332:493:06 (16:332:579:07) Machine Learning for IoT
Taught by Prof. Jorge Ortiz
In this class we will review the applied machine learning literature in the context of smart cities and smart health applications. We will start by reviewing the core machine learning literature and concepts and then move to applications of ML pipeline for class sensors data analysis, including anomaly detection, data integration and cleaning methods. We will also review machine learning-based techniques for data-driven control. In the second half of the semester, we will review the applied ML literature for health applications, including body sensors networks, smart devices, and video analysis. Students will be expected to read and review 2-3 papers a week and a student will lead the discussion for their assignment paper, each week. A midterm may be given at the midpoint of the semester and a final project and poster session will be the main evaluation component of the class.
New Classes that were introduced in the Spring 2019 Semester
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.
New classes introduced in prior semesters