MWSCAS Student Research Forum

We invite students to apply to participate in the MWSCAS Student Research Forum. This is an opportunity for students to display a poster describing their thesis research. 

New for 2023. The Student Research Forum is a new initiative at MWSCAS 2023. All graduate and undergraduate students are invited to apply to participate (see below)

What is it? The MWSCAS Student Research Forum is a poster section that will provide an opportunity for students to share their research, get feedback, and introduce themselves professionally to the MWSCAS Community. Posters can be of previous or recently published work, or unpublished work relevant to MWSCAS 2023. Participation is open to all undergraduate and graduate students and does not require the publication of a paper at the conference. The goal is to provide an opportunity for all students to present their research and receive feedback from MWSCAS participants. 

To participate in the Student Research Forum, applicants will need to submit an application with their name, institution, name of the advisor (if any), title of the presentation, and a short abstract (limited to 100 words) for their poster. The deadline for applications is July 15, 2023. Participants will be notified by July 22, 2023 on the acceptance of their application.

Each participating student will receive a certification for their attendance and presentation. 18 judges from industry and academia will review the posters and interact with the student researchers. Six best poster awards will be given in the following research areas:

And the best posters in the MWSCAS 2023 Student Research Forum are ...

Analog and Mixed Signal Circuits

Sutton Hathorn, Purdue University

All Digital PLLs as Intrinsic Odometers

Biomedical Circuits and Systems

Vi Nguyen, Arizona State University

Point of Care Diagnostic for Real

Time Viral Detection

Digital, Communications, and Signal Processing Circuits

Mohit Sharma, University of Windsor

Design Space Exploration in the Physical Design of an AI Processor at 12 nm using Relative Placement Methodology

Artificial Intelligence and Neuromorphic Engineering

Christopher Wolters, Technical University of Munich

Biologically Plausible Learning on Neuromorphic Hardware Architectures

Logistical Details:

To participate, please apply by clicking the "Student Research Forum Application" button below.