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. in Biosystems Engineering from the University of Tehran with a thesis on machine learning for computational imaging — building MLP-based refinements of Electrical Capacitance Tomography (ECT) reconstructions. Experienced in developing hardware and computer-vision prototypes on Raspberry Pi and Arduino, and building cross-platform Python/Kivy applications.
Education
- Thesis: Development of a machine learning algorithm to improve image reconstruction in an electrical capacitance tomography system.
- Built an MLP-based refinement (Python, scikit-learn) of linear back-projection (LBP) and Tikhonov ECT reconstructions on about 4,800 simulated phantoms (577-element FEM mesh, 485 imaging elements).
- The best of about 2,800 configurations, chosen by cross-validation, reached R² = 0.906 and RMSE = 0.134 (normalised), against best R² of about 0.85 for logistic regression and gradient boosting and 0.53 for k-NN.
- Selected Courses: Artificial Intelligence, Advanced Finite Element Method, Design and Analysis of Engineering Experiments, Computer-Aided Design (CAD), Automatic Control Systems, Advanced Instrumentation.
- Course Project: Collaborated on the CAD design and structural finite-element analysis of a scissor jack (SolidWorks, Ansys), achieving the highest project score in the course. (Machine Elements Design II project)
- Course Project: Team concept design of a mechanical harvester for small-plot farms, as a follow-up to the scissor-jack project in the following semester. (Design of Harvesting Machinery project)
- Selected Courses: Computer Programming, Fundamentals of Electrical Engineering I & II, Machine Elements Design, Engineering Statistics, Differential Equations, Numerical Methods, Maintenance Engineering, Post-Harvest Technology and Storage, Industrial Drawing I & II (CAD).
Experiences
- Built a Google Apps Script tool that looks up each book by ISBN in WorldCat and fills a spreadsheet with its title, author, publisher and year. Entries were then checked by hand.
- Maintained the library website and gave IT and network support for library operations.
Supervised by Prof. Hossein Navid and Dr. Hadi Karimi .
(continued part-time remotely after Sep 2020)
- Worked on a laboratory prototype that uses a camera to find flat surface regions on pipes, as a first design for automated pipe cutting. Set up and maintained the Raspberry Pi platform (Raspberry Pi OS, a Miniconda Python environment, the Pi camera) and contributed to the OpenCV image-processing code, which used classical methods. Designed the hardware interface between the Raspberry Pi, its camera and the machine electronics, and maintained the hardware and software.
- Built the control side of a phone-based remote control for a silage-block-cutting machine: Arduino firmware in C and circuits (relays, resistors, capacitors) that drive the actuators. The Arduino ran its own Wi-Fi hotspot, and the machine worked only while it was receiving commands from the phone.
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.
Selected Projects
Selected projects showcasing applied skills in machine learning, computer vision, and software development.
- Developed and released (v1.0) 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.