Hello, This is Shadi Khodagholi!
I am a Ph.D. student pursuing Computer Science at the University of Central Florida. My current research interests lie in federated learning and computer vision, particularly focusing on applications in healthcare systems. I am passionate about using these technologies to improve medical imaging and diagnostics, and I aim to contribute to advancements in healthcare through my work in these fields. My background includes a Bachelor’s in Computer Engineering, with a solid foundation in machine learning and object detection. I am currently working under the supervision of Dr. Niloofar Yousefi.
Education
Ph.D in Industrial Engineering (May 2025 - Present)
GPA: 3.77/4.0 (Not Completed)
Master in Computer Science (September 2022 - May 2025)
GPA: 3.7/4.0
Bachelor of Science in Computer Engineering (September 2016 - August 2020)
GPA: 16.18/20.0
Junior/Senior GPA: 17.17/20.0
Papers
- ULTRA-AIR: Ultrasound Landmark Tracking for Real-time Anatomical Airway Identification and Reliability Check - Accepted at IEEE Body Sensor Networks, 2024
Authors: Zahra Khodagholi, Jasmine Sun, Nour Awad, Gregory R. Dion, and Laura J. Brattain
Summary: This paper presents ULTRA-AIR, a deep learning system for real-time tracking of neck anatomical landmarks to assist with ultrasound-guided airway management. It achieves high accuracy across various classes and computes uncertainty scores to enhance the system's reliability in critical care settings.
Experiences
Working on spatio-temporal vision models using BrainChip’s proprietary hardware-accelerated TENNs (Temporal Event-based Neural Networks). This project evaluates TENNs for efficient object detection and segmentation, optimized for edge AI.
Focused on mammography image analysis using advanced segmentation models to aid in early detection of breast cancer.
Supported labs and grading for COP3503 (Computer Science II) and CNT4603 (System Administration), ensuring timely feedback and student engagement.
Developed medical image segmentation tools to detect COVID-19 in pediatric lung scans, enhancing diagnostic automation and precision.
Applied ML models such as K-means, Random Forest, and regression on real-world data; contributed to data preprocessing and insights generation.
Projects
- Breast Cancer Segmentation (March - May 2024) - Research at UCF utilizing the UNET architecture and MONAI on the TIGER dataset. The project focuses on improving segmentation accuracy for early detection and treatment of breast cancer. GitHub Link
- Branch Prediction Using CNNs (February - April 2023) - Designed a branch predictor using Convolutional Neural Networks (CNNs) to enhance prediction accuracy. This project involved implementing traditional branch predictors such as Gshare and bimodal predictors alongside CNN-based architectures for more accurate branch outcomes in computer architecture simulations.
- Improvement of the paper Spage2Vec (February - April 2023) - Under the supervision of Haiyan Hu at UCF, this project aimed to enhance the accuracy of Spage2Vec for detecting spatial gene expression constellations. Adjusting hyperparameters led to a slight improvement in spatial gene expression detection accuracy. GitHub Link
- Object Detection using ResNet (September 2019 - July 2020) - Bachelor's thesis project, where the COCO dataset was trained using ResNet architecture. This project developed a pipeline for detecting multiple objects, with a focus on improving accuracy and training speed using pre-trained models. GitHub Link
Hobbies & Interests
When I'm not working, I love to go on long walks, get lost in a good book, or watch beautiful movies. I'm a big fan of Camus's The Stranger, Dostoevsky's The Brothers Karamazov, Crime and Punishment, The Idiot, Kazantzakis's Zorba the Greek and Murakami's Colorless Tsukuru Tazaki.
As for movies and series, Ikiru by Kurosawa, Taste of Cherry by Kiarostami and Fleabag have my heart.
📚 Feel free to add me on Goodreads to see more of my favorite reads!
📽️ And you can also follow me on Letterboxd to check out my favorite films!