Masoumeh Farhadi Nia
Ph.D. Student in Electrical Engineering
Francis College of Engineering, UMass Lowell
Image and Video Processing
Signal Processing and AI
EEG-Based Brain Computer Interface
Skills
- Programming Language: Python, MATLAB
- Others: FFmpeg, Luna, Docker
Biography
Masoumeh is a PhD student in ECE at UMass Lowell, contributing to research at CARE, Collaborative Assistive and Rehabilitative Robotics Engineering, and CACT, Center for Advanced Computation and Telecommunications, labs. Her focus is on Signal Processing and AI, specializing in EEG-based Brain Computer Interface (BCI) for electric wheelchair navigation.
Education
- Ph.D. (expected 2025) Electrical Engineering, University of Massachusetts Lowell, Lowell, MA, USA
- M.Sc. (2021) Image and Video Communications and Signal Processing, University of Bristol, Bristol, UK
Experiences
- Graduate Teaching Assistant at UMass Lowell (Jan 2024 – Current) for EECE. 2070 Circuit I Lab
- Graduate Teaching Assistant at UMass Lowell (Sep 2023 – Dec 2023) for EECE.1070 Introduction to Electrical and Computer Engineering
- Graduate Teaching Assistant at UMass Lowell (Jan 2023 – May 2024) for EECE.3630 Introduction to Probability and Random Processes
- Graduate Teaching Assistant at UMass Lowell (Jan 2022 – May 2022) for COMP.3050 Computer Architecture and Design
- Graduate Research Assistant at UMass Lowell (Jan 2022 – Current) Advised by Prof. Chandra and Prof. Wolkowicz
- Data Engineer at Ipsotek Ltd ( Feb 2021 – March 2021) for Video Processing
- Project Coordinator/Manager at Delta Ertebatat Iranian (Dec 2016 – Dec 2019) for Wireless Telecommunication Projects
- Project Coordinator at Partcell (May 2016 – Dec 2016) for Wireless Telecommunication Projects
Selected Publications
- Experiential Learning for Interdisciplinary Education on Vestibular System Models, Available at: https://peer.asee.org/experiential-learning-for-interdisciplinary-education-on-vestibular-system-models
- Comparative Analysis of Segment Anything
Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography
Images, Available at : https://doi.org/10.48550/arXiv.2306.12510
Affiliated Organizations
- American Society for Engineering Education
- IEEE
- Association for Women in Science
- Women in Robotics