Welcome! - Armin Danesh Pazho

Armin Danesh Pazho

About Me

I am a Ph.D. candidate with a strong focus on Artificial Intelligence and Machine Learning. I am passionate about research and development of cutting edge technologies. My work is driven by the goal of creating innovative, scalable solutions that have a tangible impact.

Computer Engineer & AI/ML Researcher

Here you can find some personal information about me:

Email: armin.daneshpazho@gmail.com
Degree: Current Ph.D. Candidate
Major: Electrical and Computer Engineering

Academic Email: adaneshp@charlotte.edu
Last Received Degree: Master of Science
Foucs: AI/ML and Computer Vision

My current work centers on the research and development of novel AI/ML algorithms, systems, and datasets. I am passionate about addressing real-world challenges of AI/ML developments (with a focus on Computer Vision).

Skills

Here is a selection of tools and skills that I have acquired throughout my education and professional experiences.

Python 95%

TensorFlow 90%

Foundation Models 80%

Diffusion Models 80%

Large Language Models (LLMs) 75%

NumPy 90%

OpenCV 80%

Docker 80%

SQL 75%

PyTorch 95%

C/C++ 85%

Generative AI 80%

Vision Language Models (VLMs) 80%

Cloud Platforms (AWS, GCP, Azure) 70%

Pandas 85%

ONNX 75%

Kubernetes 75%

GOLANG 75%

Resume

Sumary

Armin Danesh Pazho

An enthusiastic and driven Ph.D. candidate specializing in Artificial Intelligence and Machine learning, with a focus on real-world applications. Building a strong foundation in developing cutting-edge AI solutions, driven by a commitments to enchance the well-being of society.

Charlotte, NC
adaneshp@charlotte.edu

Education

Ph.D. in Electrical & Computer Engineering

August 2021 - December 2025 (Fall25 expected graduation)

UNC Charlotte, NC, USA

GPA: 4.0/4.0

Started my PH.D. with Dr. Hamed Tabkhi as my adviosr, working on AI-enable real-world solutions with a focus on Computer Vision.

Master of Science in Computer Engineering

January 2023 - December 2023

UNC Charlotte, NC, USA

GPA: 4.0/4.0

With a focus on AI and ML, some important courses are Introduction to Machine Learning, Real-time Machine Learning, Applied Artificial Intelligence, Machine Learning for the Internet of Things, and Computer Vision

Professional Experience

Graduate Research Assistant

August 2021 - Present

Charlotte, NC, USA

Research and development of SotA AI/ML algorithms, systems, and datasets.
Optimization and development of solutions to bridge the gap between research and real-world deployment.
Management of all the training servers of our laboratory.
Collaborating directly with testbed partners to continuously refine and adjust algorithms based on real‑world feedback and challenges.

Graduate Teaching Assistant

August 2021 - May 2022

Charlotte, NC, USA

Assisted in teaching responsibilites.
Designed projects for the courses in languages such as C/C++.
Course 1: Data Structures and Algorithms
Course 2: Embedded Systems

Demo Videos

Explore some of the demos of the projects and research papers I have worked on!

Video Anomaly Detection Demo.

Community Engagement - AI for Public Safety

Anomaly Detection with Real-Time Application Notification (Videos from CHAD dataset)

Moblie Application - Privacy Preserving AI for Public Safety and Situational Awareness

Publications

All
Conference
Journal
Arxiv

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

IEEE Internet of Thigs Journal (IoTJ 2023)

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

IEEE Transactions on Knowledge and Data Engineering (TKDE 2023)

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)

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

Springer Journal of Computational Urban Science (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 (AAAI-AI4SG 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 (IEEE T-ITS2024)

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)

Real-time Online Unsupervised Domain Adaptation for Real-world Person Re-identification

Springer Journal of Real-Time Image Processing (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)

VEI: A Multicloud Edge Gateway for Computer Vision in IoT

ACM Proceedings of the 1st Workshop on Middleware for the Edge

Awards, Affiliations, and Services

Best Demo Award

Received best demo award for presenting our Computer Vision paper and work in Cyber-Physical Systems and Internet-of-Things Week, IEEE/ACM CPS-IoT Week 2023.

Received U.S. NSF Travel Grant

Received U.S. NSF travel grant judged by my proposal/abstract on my research and Ph.D. studies.

Scientific Reviewer

Active reviewer of well known conferences and journals such as IEEE Transactions on Neural Networks and Learning Systems and IEEE Internet of Things Journal.

TV Programs

My graduate research and university laboratory have been recognized and appeared in three television programs.

IEEE Student Memeber

Active member of IEEE since 2022.

Contact

Best way to reach out to me is through my email address armin.daneshpazho@gmail.com. You can also message me on LinkedIn. I will try my best to respond as soon as possible.