Paniz Eilkhani

About

I am passionate about applying my knowledge and skills in electrical and software engineering to solve real-world problems and challenges. I am especially interested in control systems, AI, robotics, data science and machine learning. As a machine learning software engineer at McMaster Automotive Resource Center (MARC) and Center for Mechatronics and Hybrid Technologies (CMHT), I designed a residual neural network with feature wise attention to identify the mode of travel, wether the user is driving, walking, biking or using bus or subway. In my role as a smart transportation engineer at Cubic Corporation, I developed a state-of-the-art system for frictionless travel using GPS data and real-time transit information. I have co-authored a paper on this topic, which was presented at the 2023 IEEE Transportation Electrification Conference & Expo. In my Co-Op as a software and controls developer at General Motors, I supported and developed Thermal EV control and diagnostic algorithms, conducted testing of prototype and production software, supported supplier component design and development activities, Investigated and supported continuous improvement activities associated with producti

Experience

Education