A professional electromagnetic engineer with doctoral-level (Ph.D. in electrical engineering, Arizona State University) skill set in antenna design and analysis, backed by over 5 years of hands-on experience in RF system design and advanced simulation software. Proficient in ANSYS HFSS, VNA, MATLAB, and Python, leveraging these tools to optimize design processes and ensure superior performance in near-/far-field measurements. I also received a B.S. and an M.S. in electrical engineering from Michigan State University in 2013 and 2017 respectively.

Selected Works

Integrated Sensing Mechanism for Reconfigurable Intelligent surface (RIS)

RISs can alter the propagation environment to improve wireless communication and power transfer. Thus, Deploying RISs has the potential to mitigate this signal squandering by redirecting the blocked or diffracted wave in a passive manner toward the intended receiver(s). However, the full potential of RISs cannot be realized without the knowledge of the propagation environment at each constitutive element of the RISs.

In this collaborative work, a fully autonomous reconfigurable reflective metasurface with integrated sensing capabilities is being developed to address this need.

Hybrid RIS (HRIS) architecture for Compressive sensing

When the reconfigurable reflective surface is used as an intelligent agent in wireless environments, it needs to know about the transmitters, receivers, and propagation environment—quantities that can change rapidly. However, capturing these data is a practical challenge as RISs are electrically large surfaces comprising huge amounts of elements with subwavelength spacing.

This project deals with this shortcoming by developing a hybrid RIS for compressive sensing. This hybrid design can pave the way for an autonomous smart reflector. As an illustration, the angle of arrival detection capability of the HRIS is depicted here for a number of cases.

Machine learning based radar signal classifier

The utilization of machine learning to classify radio signals has become ubiquitous recently. However, the detection of different classes of motion using radar signals necessitates the successful resolution of Doppler signatures. More specifically, discriminating subtle features in radar-Doppler signatures is crucial for many remote sensing applications. Therefore, in this project, I am developing a remote sensing scheme using a deep neural network to classify closely related movements. For instance, the radar return data for two classes of motions are presented in the time-frequency plots. A major aspect of this project is to fine-tune these plots to ensure appropriate data input to the neural network.