Rishad Arfin
Graduate Research Assistant, Computational Electromagnetic & System Optimization Lab, Ece Dept @Faculty Of Engineering - Mcmaster University
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WORK HISTORY
Graduate Research Assistant, Computational Electromagnetic & System Optimization Lab, Ece Dept @Faculty Of Engineering - Mcmaster University
ON, CA
EDUCATION
Dhaka City College
Higher Secondary Certificate
McMaster University
Master of Applied Science
McMaster University
Doctor of Philosophy - PhD (Ongoing)
DRMC - Dhaka Residential Model College
Secondary School Certificate
Islamic University of Technology
Bachelor of Science
SKILLS
ABOUT RISHAD ARFIN
Hi there! I am currently working as a full-time Ph.D. Candidate in the Department of Electrical & Computer Engineering (ECE) at McMaster University, Canada. My research broadly focuses on the development of emerging nanophotonic, optical, and THz devices for energy harvesting, optical communications, sensing, and imaging applications. Currently, my research works involve the domain of inverse design for different classes of nanophotonic devices by using various optimization techniques ranging from conventional to contemporary methods. In essence, my primary interest lies to develop efficient computational framework for nanophotonic devices by leveraging selective gradient-based optimization techniques that use powerful adjoint methods. To elaborate on my academic teaching experience, I am working as a graduate teaching assistant (TA) in the Department of ECE at McMaster University for 3+years. Also, I served as a full-time lecturer (presently on leave) in the Department of Electrical & Computer Engineering at North South University, Bangladesh for 3+ years. During my tenure, I have conducted several undergraduate courses in and CSE disciplines. Previously, I was employed as a full-time lecturer in the Department of Electrical & Electronic Engineering at Southeast University, Bangladesh for 3+ years. My research interest broadly covers the field of computational nanohotonics, optics, plasmonics, optimization techniques, applied machine learning, and deep learning.
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