About
I am a driven and curious Electrical Engineering graduate with over 3 years of research experience applying deep learning techniques. Currently, I work as a Research Assistant in the Human and Robot Interaction Laboratory at the University of Tehran.
With a GPA of 3.87/4.0, I have a strong academic background including coursework in machine learning, computer vision, optimization, and control systems. I have published multiple papers on topics such as pedestrian re-identification, attribute recognition, and real-time vehicle tracking.
I am seeking out opportunities to expand my skills in areas that include optimization and reinforcement learning.
Outside of academics, I enjoy staying active and creative. I love playing basketball, gaming, and camping.
I am excited to apply my diverse skills and experiences to impactful research and engineering roles after graduation. Please view my CV and get in touch!
Resume
Honors and Awards
- Have been honored with the Best Undergraduate Project Award at the Project Day held in the Electrical and Computer Engineering Faculty of the University of Tehran. My project focused on implementing a novel approach for Pedestrian Attribute Recognition.
- Ranked 13th out of approximate 120 undergraduate students (Ranked 2nd in Control branch), school of Electrical and Computer Engineering, University of Tehran.
- Ranked 664th among more than 164,000 participants in Nationwide Universities Entrance Exam (B.Sc.).
Education
B.Sc in Electrical Engineering
Sep.2019 - July 2023
University of Tehran, Tehran, Iran
Rank 13th out of approximate 120 undergraduate students. GPA: 18.36/20 (3.87/4.0)
Diploma of Mathematics
June.2016 - Sep.2019
Salam High School, Tehran, Iran
GPA: 3.89/4.0
English Course
June.2011 - Sep.2017
Iran Language Institute, Tehran, Iran
Score: 87.5/100
Research Experience
Research Assistant
May.2021 - Present
Taarlab, Tehran, Iran
- Implementing deep convolutional neural networks for person-reID, attribute recognition and other computer vision tasks.
- Implementing object detection/segmentation models.
- Supervisor: Dr. Mehdi Tale Masouleh
Sep.2022 - June 2023
TIL: Telecommunications Innovation Lab, Tehran, Iran
- Predicting Arterial Blood Pressure (ABP) using subject’s PPG signal and 1D convolutional neural networks.
- Supervisor: Dr. Saeed Akhavan
Teaching Assistant
Sep.2020 - Present
University of Tehran, Tehran, Iran
- Neural Networks & Deep Learning | Spring 2023 | Dr. Ahmad Kalhor.
- Instrumentation | Spring 2023 | Dr. Mohammadreza Nayeri.
- Instrumentation | Fall 2022 | Dr. Rezvan Nasiri.
- Linear Control Systems | Fall 2022 | Dr. Fariba Bahrami.
- Engineering Mathematics | Fall 2021 | Dr. Mehdi Tale Masouleh.
- Electronics I | Fall 2021 | Dr. Zeynab Sanaei.
- Engineering Mathematics | Spring 2021 | Dr. Taheri.
- Introduction to computing systems and programming | Fall 2020 and Fall 2021 | Dr. Moradi.
- Introduction to Electrical engineering | Spring 2021 | Dr. Samimi.
Publications
Image-based and Partially Categorical Annotating Approach for Pedestrian Attribute Recognition
July 2023
In Processing of Computer Vision and Image Understanding Journal
- This research suggests an image-based partially categorical attribute dataset (CA-Duke) and also proposes a two-step learning method for evaluating the separability of data in the latent space via a new metric called the Separation Index.
SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera Video Tracking and Recognition
October 2022
Published in Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS 2022)
- In this paper, we propose a new web-scraped Iranian vehicle dataset (SIVD) (which has 29 classes and more than 36,000 images) for simultaneous real-time vehicle tracking and recognition.
Projects
DUKEMTMC Dataset Annotation
May.2021 - Present
Taarlab, Tehran, Iran
- Implementing deep convolutional neural networks for person-reID, attribute recognition and other computer vision tasks.
- Implementing object detection/segmentation models.
- Supervisor: Dr. Mehdi Tale Masouleh
