1 - 10 of 11 results for: ENGR 217

Capstone Biomedical Data Science experience. Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Issues related to research reproducibility. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Because the team projects start in the first week of class, attendance that week is strongly recommended. Prerequisites: BIOMEDIN 210 or 214 or 215 or 217 or 260. Preference to BMI graduate students. Consent of instructor required.NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.

This is a multidisciplinary graduate level course designed to give students hands-on experience working in teams through real-world project-based research and experiential classroom activities. Students work in dynamic teams with the support of course faculty and mentors, researching preselected topics. Students apply a computational and data analytics lens and use design thinking methodology. The course exposes students to ethics, unintended consequences and team building exercises supported by relevant lectures on data science and subject matter topics. Pre-requisites: none. Enrollment by application only. Graduate students only. The course application closes November 30, 2021. Application and more information: https://forms.gle/VW6KKWN4AUV6cPzZA

CME 217A introduces students to potential computational mathematics research projects at Stanford and with outside organizations. This seminar series is an introduction to winter quarter CME 217B, a multidisciplinary graduate level course designed to give students hands-on experience working in teams through real-world project-based research. Each week throughout the quarter, a project mentor will present their research. In November, students preference projects and apply for the winter quarter CME 217B. Pre-requisites: none. Graduate students only.

This course explores the design, programming, and performance of modern AI accelerators. It covers architectural techniques, dataflow, tensor processing, memory hierarchies, compilation for accelerators, and emerging trends in AI computing. This course will cover modern AI/ML algorithms such as convolutional neural nets, and Transformer-based models / LLMs. We will consider both training and inference for these models and discuss the impact of parameters such as batch size, precision, sparsity and compression on the accuracy of these models. Students will become familiar with hardware implementation techniques for using parallelism, locality, and low precision to implement the core computational kernels used in ML. Students will develop intuitions to make system-level trade-offs to design energy-efficient accelerators. Students will read recent research papers and complete a final design project.

Capstone Biomedical Data Science experience. Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Issues related to research reproducibility. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Because the team projects start in the first week of class, attendance that week is strongly recommended. Prerequisites: BIOMEDIN 210 or 214 or 215 or 217 or 260. Preference to BMI graduate students. Consent of instructor required.NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.

This course provides an introduction to the sensor systems found in modern-day smartphones, wearables, and hearable devices. As much as we take their functionality for granted, there is a tremendous amount of engineering needed to sense "real world" signals such as acceleration, touch, or altitude. There will be an overview on the actual circuitry and hardware used in sensor implementations, with a focus on MEMS devices (eg, accelerometer/gyro), going up through the algorithms commonly seen in sensors processing, and finally fusion of data from multiple sensors to yield final data presented to a user. The four broad areas that will be covered are: Inertial sensing/movement; Touch sensing/authentication; Health sensing (PPG, ECG, SpO2); Next-generation (force, radar/ranging, ultrasonics, and more). There is a lab/project associated with each of these areas, each project spanning roughly two weeks. The projects are designed to be more at a system level; the student will be required to explore the performance and limitations of sensing hardware, and then take that understanding to solve real-world sensor problems. All projects will be built on a Raspberry Pi with various sensor boards; students should be comfortable with wiring up a small breadboard, and coding on an RPi a high-level language such as Python or Java.

This course investigates how culture and diversity shape who becomes an engineer, what problems get solved, and the quality of designs, technology, and products. As a course community, we consider how cultural beliefs about race, ethnicity, gender, sexuality, abilities, socioeconomic status, and other intersectional aspects of identity interact with beliefs about engineering, influence diversity in the field, and affect equity in engineering education and practice. We also explore how engineering cultures and environments respond to and change with individual and institutional agency. The course involves weekly presentations by scholars and engineers, readings, short writing assignments, small-group discussion, and hands-on, student-driven projects. Students can enroll in the course for 1 unit (lectures only), or 3 units (lectures+discussion+project). For 1 unit, students should sign up for Section 1 and Credit/No Credit grading, and for 3 units students should sign up for Section 2 and either the C/NC or Grade option.

Engineering Design Analytics is for engineering students seeking greater depth in new product development. Students will develop structured methods for addressing key design questions: Who are 'customers'? What do customers value? What are leverage points for designing systems? What are robust metrics for assessing system performance and customer satisfaction? What are failure modes? What belongs in a product requirements document? In addition, students will have the chance to develop their own design ethos by examining deeper questions: Where do ethics come into play for engineering projects? How might designers leverage AI to empower their design process? Where do we ultimately find meaning, and can we design for that? Assignments will include readings, case studies, applied activities, and write-ups. In class activities will include lectures, discussions, and working sessions. Prerequisites: ME 103/203 or consent of instructor. By application only, see notes below.

Topics in stochastic processes, emphasizing applications. Markov chains in discrete and continuous time; Markov processes in general state space; Lyapunov functions; regenerative process theory; renewal theory; martingales, Brownian motion, and diffusion processes. Application to queueing theory, storage theory, reliability, and finance. Prerequisites: 221 or STATS 217; MATH 113, 115.