AI Researcher and Developer with a Primary Focus on Deep Learning, Computer Vision, and Generative Models.
https://orcid.org/0009-0003-1273-9435
Here you can download the academic version of my CV
Hey! I’m a Master’s student with a deep passion for Artificial Intelligence, Deep Learning, and Computer Vision, currently under the guidance of Dr. Ali Ahmadi. My research dives into the fascinating world of generative models and medical image analysis. But life’s not all code and research! When I’m not tinkering with AI, you’ll find me strumming on my Guitar or Setar, breaking a sweat with Calisthenics and Yoga, or inventing cool IoT gadgets that blend the digital and real worlds. Whether it’s exploring the latest in AI, hitting the perfect chord, or building something new, I’m all about blending tech, art, and a bit of zen.
Dec 2023 - Present
I specialize in Medical Image-to-Image Translation within the field of ophthalmology, focusing on the interpretation of eye-related diagnostic images. My expertise encompasses Fundus Photography, Optical Coherence Tomography (OCT), and Ultrasound Imaging.
Dec 2022 - Present
As a Research Assistant at H. N. Toosi University of Technology, I specialize in deep learning and generative models, developing and implementing models like Diffusion Models and Generative Adversarial Networks (GANs). Additionally, I assist in teaching courses such as Artificial Intelligence and Computer Vision, helping students learn the course better and designing assignments to enhance their understanding.
Oct 2023 - Jun 2023
During my time as a teacher at an elementary school, I had the unique opportunity to introduce young students to the world of programming. I taught the basics of Python, sparking their interest in coding by breaking down complex concepts into fun, engaging lessons. As their understanding grew, I gradually introduced them to foundational concepts in data analysis and artificial intelligence.
Dec 2022 - Aug 2023
At Irapardas company, I worked as a Data Scientist and Engineer, where I utilized Hadoop and Apache Hive for managing and querying large datasets. I also employed data visualization tools like Metabase to create insightful visualizations that informed key decisions. My role involved not only backend development but also leveraging these technologies to enhance system efficiency and optimize data-driven processes.
Feb 2020 - Sep 20222
As a Research Assistant in Dr. Ali Amiri's laboratory at University of Zanjan, I specialize in deep learning, object detection, 3D scanning, and real face recognition. My work focuses on developing and implementing advanced models in these areas, contributing to innovative research. Additionally, I assist in teaching courses such as Artificial Intelligence and Advanced Computer Programming, designing assignments to help students deepen their understanding.
Jun 2021 - Aug 2022
As a Project Leader and Software Engineer, I led a startup project focused on developing a mobile application for streamlining date and meeting scheduling. In my role as Project Leader, I guided the team, coordinated efforts, and ensured milestones were met. Simultaneously, as a Software Engineer, I contributed to the technical development, designing, implementing, and refining the application. This experience allowed me to combine leadership with hands-on software engineering, ensuring both strategic and technical success.
This project aims to leverage advanced generative models to translate fundus images into Optical Coherence Tomography (OCT) images, facilitating improved diagnosis of diabetic retinopathy. By utilizing state-of-the-art deep learning techniques, specifically Generative Adversarial Networks.
Segmenting blood vessels in fundus images enables better comprehension of retinal diseases and facilitates the computation of image-based biomarkers. However, manual vessel segmentation is a labor-intensive task. This project aims to automate vessel segmentation using generative adversarial networks (GANs).
A smart device to open the door or control the entry and exit of companies or organizations. We have built both the devices and a face authentication application using advanced AI and computer vision techniques for secure access in different security places like apartments, offices, and more.
This system allows students to reserve machines online, while an admin panel empowers staff to monitor usage, track data, and even control machines remotely.
Microhardness testing measures the hardness of small areas or thin materials using Vickers and Knoop methods, with forces below one kilogram. Image processing techniques analyze the indentation to calculate the material’s hardness.
Sunlight intensity is measured to analyze atmospheric conditions and detect pollutants. Using OpenCV, Matplotlib, and CNNs, this data helps predict and prevent air pollution.
Journal : Nanoscale
https://doi.org/10.1039/D4NR01018CThe study developed an automated technique using deep learning for precise nanoparticle dispersion mapping in SEM micrographs. By introducing size uniformity and supercritical clustering factors, the approach quantified the impact on properties and correlated dispersion characteristics with interfacial strength and thickness in polymer nanocomposites.
Journal : ACS Applied Materials & Interfaces
https://doi.org/10.1021/acsami.4c02797This study introduces a computer vision-based method for measuring nanoparticle sizes in polymer nanocomposites. The technique improves accuracy in predicting elastic modulus by integrating segmented images into a finite element model, resulting in precise quantification of interphase properties with minimal discrepancy from experimental data.
Journal : Composites Science and Technology
https://doi.org/10.1016/j.compscitech.2023.110360This study explores the use of deep learning techniques, specifically a visual particle recognition strategy, to accurately assess nanoparticle dispersion in nano-zeolite polyethylene nanocomposites. By analyzing scanning electron microscopy images, the approach improves the accuracy of particle size measurements, enhancing the understanding of interphase effects on the material's macroscopic properties. The findings demonstrate a significant advancement in the quantitative analysis of nanocomposite morphology.
Computer Science Department of K. N. Toosi
Seyed Khandan Bridge, Shariati
Tehran, Iran