Ghazal Alinezhad Noghre

I'm

About

Hi! I am Ghazal, a Ph.D. candidate in Electrical and Computer Engineering at the University of North Carolina at Charlotte, with a strong focus on Artificial Intelligence (AI), Computer Vision, and Deep Learning. Passionate about leveraging AI technologies to drive innovative solutions in real‑world applications, I have extensive hands‑on experience in developing scalable, efficient AI systems. My work includes building high‑impact models for anomaly detection, real‑time video analytics, and deep learning‑based safety systems. I am eager to bring my AI and deep learning skills to industry challenges, contributing to the development of advanced AI systems that improve efficiency, scalability, and user experience.

AI/ML Researcher and Engineer

Here you can find some basic information about me.

Degree: Current Ph.D. Candidate
Advisor: Dr. Hamed Tabkhi
Email: gh.alinezhad@gmail.com
City: Charlotte, NC, USA

Last Recieved Degree: M.Sc.
Major: Electrical and Computer Engineering
Academic Email: galinezh@charlotte.edu

Skills

Expertise in programming languages, AI/ML/DL development frameworks, and cloud technologies.

Python 95%

PyTorch 95%

TensorFlow 80%

Numpy 95%

Pandas 95%

Opencv 90%

Scikit-learn 90%

C/C++ 80%

AWS 75%

Docker 80%

Kubernetes 75%

Go 75%

Matlab 75%

SQL 60%

Bash/Shell Scripting 80%

Verilog 70%

Resume

Extensive experience in AI research, real-time system development, and deep learning applications.

Education

Ph.D. in Electrical and Computer Engineering

2021 - 2025 (Expected)

University of North Carolina at Charlotte, NC, USA

Focus: Deep Learning, Computer Vision, Anomaly Detection, Generative AI

GPA: 4.0

M.Sc. in Computer Engineering

2023

University of North Carolina at Charlotte, NC, USA

GPA: 4.0

B.S. in Electrical Engineering

2014 - 2019

University of Tehran, Tehran, Iran

Experience

Research Assistant

2021 - Present

University of North Carolina at Charlotte, NC, USA

Developed state-of-the-art computer vision algorithms and large-scale datasets.
Deployed AI systems using domain adaptation techniques in real-world environments.
Designed real-time transportation AI systems on edge platforms.

Teaching Assistant

2019 - 2020

University of Tehran, Tehran, Iran

Courses: Computer Networks, Microprocessors
Skills: Assembly, C++, Python, Socket Programming

Achievements

Best Demo Award

Best Demo Award for paper entitled "Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems", Cyber-Physical Systems and IoT Week, 2023

NSF Travel Grant

Honored to be awarded the NSF Travel Grant for participating in scientific conferences.

Featured in TV Programs

I have made three television appearances to showcase the research project conducted at the university laboratory where I am currently engaged.

Serving as a Reviewer

Served as a reviewer for esteemed conferences and journals, including IEEE Transactions on Intelligent Vehicles, Elsevier Journal of Pattern Recognition, and more.

IEEE Student Member

Active member of IEEE since 2022.

Demo Videos

Here are some real-world demos of the projects that I have worked on!

Video Anomaly Detection

AI for Public Safety - Community Perception of AI

Application for Real-Time Anomaly Detection Alert

Mobile Application - Privacy Preserving AI for Public Safety

Publications

All
Conference
Journal
Arxiv

VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)

Evaluating the Effectiveness of Video Anomaly Detection in the Wild: Online Learning and Inference for Real-world Deployment

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)

An Exploratory Study on Human-Centric Video Anomaly Detection Through Variational Autoencoders and Trajectory Prediction

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024)

Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things

IEEE Internet of Thigs Journal (IoTJ2023)

A Survey of Graph-Based Deep Learning for Anomaly Detection in Distributed Systems

IEEE Transactions on Knowledge and Data Engineering (TKDE 2023)

Understanding Policy and Technical Aspects of AI-enabled Smart Video Surveillance to Address Public Safety

Springer Journal of Computational Urban Science (2023)

Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems

ACM/IEEE 14th International Conference on Cyber-Physical Systems (CPS-IoT Week 2023)

Understanding the Challenges and Opportunities of Pose-based Anomaly Detection

8th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence (iWOAR 2023)

CHAD: Charlotte Anomaly Dataset

Scandinavian Conference on Image Analysis (SCIA 2023)

Understanding Ethics, Privacy, and Regulations in Smart Video Surveillance for Public Safety

Association for the Advancement of Artificial Intelligence - AISG (AAAI-AISG 2023)

Integrating AI into CCTV Systems: A Comprehensive Evaluation of Smart Video Surveillance in Community Space

arXiv preprint arXiv:2312.02078

A POV-Based Highway Vehicle Trajectory Dataset and Prediction Architecture

IEEE Transactions on Intelligent Transportation Systems (2024)

Exploring Public's perception of safety and video surveillance technology: A survey approach

Elsevie Journal of Technology in Society (2024)

ADG-Pose: Automated Dataset Generation for Real-World Human Pose Estimation

International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI 2022)

Privacy-preserving Real-world Video Anomaly Detection

IEEE International Conference on Smart Computing (SMARTCOMP 2023)

Real-World Community-in-the-Loop Smart Video Surveillance--A Case Study at a Community College

arXiv preprint arXiv:2303.12934

Real-World Community-in-the-Loop Smart Video Surveillance System

IEEE International Conference on Smart Computing (SMARTCOMP 2023)

VegaEdge: Edge AI confluence for real-time IoT-applications in highway safety

Elsevier Journal of Internet of Things (2024)

PoseWatch: A Transformer-based Architecture for Human-centric Video Anomaly Detection Using Spatio-temporal Pose Tokenization

arXiv preprint arXiv:2408.15185

PHEVA: A Privacy-preserving Human-centric Video Anomaly Detection Dataset

arXiv preprint arXiv:2408.14329

Demonstration of Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems

ACM/IEEE 14th International Conference on Cyber-Physical Systems (CPS-IoT Week 2023)

Contact

If you'd like to get in touch, the best way to reach me is via email at gh.alinezhad@gmail.com. I look forward to hearing from you!