Mohammad Hossein Hamian

• Dissertation: "Dynamic Point Cloud Compression Using Human Motion Detection by Deep Part Segmentation Network." • Deep part-segmentation networks for dynamic 3D point clouds, applied to human motion detection and spatiotemporal structure modelling. • Research stay at the University of Passau, Germany (Jan-Aug 2026) as part of the doctoral programme. • Research complete and dissertation finalized; defense pending. Available for full-time employment immediately. • Key skills: Deep Learning, Computer Vision,

• Doctoral research stay in deep learning and computer vision, focused on dynamic 3D point cloud compression and segmentation. • Developed deep part-segmentation networks for human motion detection in dynamic 3D scenes. • Collaborated with international researchers, connecting deep-learning methodology to applied computer-vision problems. • Key skills: Python, PyTorch, 3D Point Clouds, Deep Semantic Segmentation, Data Compression.

• Business Assurance project manager (a formally registered corporate project) and project lead for several RAFM office initiatives: customer segmentation, executive dashboard design, KPI definition, and international SMS fraud detection. • Implemented a 360-degree customer profile unifying charging-node data (CDR/CBS - voice and SMS), CRM and customer behavioural data into a single subscriber view for 70M+ subscribers - the foundation for segmentation, behavioural analysis and revenue reporting across the

• Taught Data Mining (2 semesters), Operating Systems (2 semesters) and the Database Laboratory (3 semesters), frequently running parallel sessions. • Designed and supervised laboratory work in database design, SQL querying and query performance optimisation. • Supervised applied machine-learning labs and undergraduate research projects. • Ranked highest in student evaluations among lecturers in the department. • Key skills: Data Mining, Operating Systems, SQL, Database Design, Technical Communication.

• Taught Information Retrieval (4 semesters) and Operating Systems (3 semesters), frequently running parallel sessions. • Covered data indexing, text processing and search-engine architecture; concurrency, memory management and process scheduling. • Designed course material and assessment, and mentored undergraduate students across multiple cohorts. • Key skills: Information Retrieval, Operating Systems, Curriculum Design, Academic Mentorship.

• Built ETL, data-cleansing and fraud-detection pipelines over billion-record customs and financial datasets across governmental and non-governmental sectors. • Applied deep learning and image processing to object detection in X-ray cargo scanner imagery, supporting automated customs risk assessment. • Developed classification models (Random Forest) and risk-assessment analytics for business entities. • Deployed modular data-mining workflows in KNIME, WEKA and RapidMiner; delivered management dashboards in

• Key Research Focus: Deep Learning, Federated Learning, • Dissertation topic: deep learning for dynamic 3D point cloud part segmentation and compression, including federated and privacy-preserving architectures for distributed human motion analysis. • Research stay at the University of Passau, Ger

• Master's Thesis: "Improvement of Image Semantic Segmentation in Autonomous Driving Based on Deep Convolutional Neural Network" (Focus: Deep Learning for autonomous vehicle perception). • Published 3 IEEE conference papers in Deep Learning, Human Activity Recognition, and Semantic Segmentation us

• Ranked 2nd among 80 Computer Engineering students. • Winner of the 1st Place in the ACM Programming Competition (2017). • Bachelor's Thesis: "Determine the seams and gaps of rock mass using image processing" (Focus: Computer Vision & Image Processing). • Served as a Teaching Assistant (TA) for 8