Career Profile
Resourceful engineer who enjoys coding, exploration, and solving real-world problems through creative, practical thinking. Especially interested in applied research and development, using machine learning, computer vision, and software tools to turn technical ideas into reliable, deployable solutions. Holds an M.Sc. from the University of Tehran with a thesis on machine learning for computational imaging — refinement of reconstructions of ECT, an ill-posed inverse problem. Also experienced in co-developing a real-time computer vision system deployed into industrial operation on embedded Linux, and currently building deep-learning pipelines for crack detection in surface imagery.
Education
- Grade: 16.70/20
- Thesis: Machine Learning–Based Image Reconstruction Refinement in Electrical Capacitance Tomography, an ill-posed inverse problem.
- Supervisor: Dr. Hossein Mousazadeh
- Co-supervisors: Dr. Soleiman Hosseinpour and Dr. Hadi Karimi
- Proposed and developed within a Port & Maritime Organization–funded ECT pipeline-monitoring project.
- Across separate best-performing configurations, test-set R² reached 0.87 and test-set RMSE was as low as 0.147.
- Highlighted Courses: Artificial Intelligence, Finite Element Analysis, Advanced Control Systems
- Grade: 15.11/20
- Ranking: Ranked 9th in a cohort of 36 students.
- Collaborated on the design and structural analysis of a scissor jack using SolidWorks and Ansys, achieving the highest score in the Mechanical Engineering Design course; following this success, the instructor suggested tackling a real-world problem, leading the team to develop a basil-harvesting machine concept for small-plot farms.
Experiences
Amrieh — Iran’s mandatory alternative to military service
- Built a semi-automated data-entry system in Google Apps Script, reducing manual paperwork for library staff.
- Provided IT and network support, and delivered training in common software tools and basic computer literacy to staff and students.
Volunteer position, supervised by Prof. Hossein Navid and Prof. Hadi Karimi .
(continued part-time remotely after Sep 2020)
- Co-developed a real-time computer vision system for automated pipe cutting by identifying flat surface regions, deployed in industrial operation and integrated with high-speed machinery.
- Designed and integrated the hardware and electronic circuits for the real-time vision system, including Raspberry Pi interfacing.
- Configured the Raspberry Pi deployment end-to-end, setting up the Linux environment, Python dependencies, and GPIO pin control for hardware communication.
- Collaborated on a wireless remote-control system for a silage-block-cutting machine to improve operator safety.
- Responsible for electronic circuit design and integration with pneumatic and hydraulic valve systems for precise actuation.
- Developed the Arduino-based control firmware and hardware-side logic for executing TCP/UDP commands.
Certifications & Training
A rigorous, self-paced online course offered by Harvard University and taught by Prof. David J. Malan. It covers fundamental computer science concepts including algorithms, data structures, memory management, and web development through hands-on problem sets in C, Python, SQL, and JavaScript. The course is equivalent to Harvard’s on-campus CS50 and is one of the most widely taken CS courses globally, recognized for its depth and demanding project-based assessments.
An in-depth MOOC taught by scikit-learn core developers at Inria, focusing on practical predictive modeling. It covers the complete machine learning pipeline, including model selection, hyperparameter tuning, performance evaluation, and the implementation of linear, tree-based, and ensemble algorithms using Python.
Taught by AI pioneer Andrew Ng, this foundational course covers machine learning, data mining, and statistical pattern recognition. It provides practical implementation of supervised and unsupervised algorithms (including SVMs, neural networks, and clustering) alongside industry best practices for model evaluation and real-world AI applications.
Projects
Selected projects showcasing applied skills in machine learning, computer vision, and software development.
- Developing a PyTorch-based pipeline for detecting cracks in concrete-surface images.
- Using the Kaggle Surface Crack Detection dataset to build foundational computer-vision and deep-learning skills, with plans to extend the work toward pixel-level segmentation.
Skills: PyTorch, Computer Vision, Deep Learning
A Python/Kivy mobile application for recording shrimp-farming data and storing records locally in SQLite.
- Completed a Python/Kivy mobile application for recording shrimp-farming data and storing records locally in SQLite.
- Implemented a Persian interface with RTL support and automated Android APK builds through GitHub Actions.
Skills: Python, Kivy, SQLite, GitHub Actions
OSS Contributions
Some of my contribution to Open Source projects.