CV

Machine Learning Specialist at Pacefactory

Pacefactory inc., Oakville, Canada

• Optimized Lightweight Models for Real-Time Computer Vision:
• Built a high-performance XGBoost-based object detection pipeline (Macro Avg 0.92; 100k samples processed in 0.3s)
• Developed a lightweight CNN-based sliding window detector for resource-constrained environments
• Built object/keypoint detection and semantic segmentation models from raw camera footage
• Analyzed capabilities and constraints of Aruco markers for accurate fiducial tracking
• Implemented CLIP-based state classification and HDBSCAN clustering for unsupervised category discovery
• Semi-Automatic Annotation Tool Development:
• Built a dockerized Django-based web app integrating XGBoost and YOLO for iterative object annotation
• Developed annotation tools using Grounding DINO (text prompts) and Rex-Omni (visual prompts)
• Built zero-shot training from SAM masks via multi-view bboxes

Research Assistant at CyberRange

University of Ottawa, Ottawa, Canada

• Built a system to bypass face verification systems - Improved the current state-of-the-art approach's success rate by 15% - from 43% to 58%
• Created a system for training a GAN without requiring human oversight - Efficiently solved "Is the GAN sufficiently trained?" using a novel algorithm tested on imagery and tabular data
• Employed GANs to tackle the class imbalance problem the results of which are published in two papers

Software Engineer at Armeh Sazeh Co.

Armeh Sazeh Co., Tehran, Iran

• Designed and implemented web-based software to automate the company's processes - I served as a liaison between my team and the client, handling communication, task delegation, requirement gathering and back-end development.

Software Engineer at Armeh Sazeh Co.

Armeh Sazeh Co., Tehran, Iran

• Designed and implemented Windows-based software to automate the company's processes in C++

Master of Computer Science in Applied AI

University of Ottawa, Ottawa, Canada

• Thesis - Benevolent and Malevolent Adversaries, A Study of GANs and Face Verification Systems
• GPA 10/10

Bachelor of Science in Computer Science

Amirkabir University of Technology, Tehran, Iran

• Thesis - Generating 2D and 3D human stick figure poses using Generative Adversarial Networks(GANs)
• Final Two Years' GPA 9.54/10

• Awarded "excellent student" at the University of Ottawa's graduate studies Excellence Ceremony

• Recipient of Vector Scholarship in Artificial Intelligence - $17,500

• Nazari, Ehsan, Paula Branco, and Guy-Vincent Jourdan. "Generalized Attacks on Face Verification Systems" (arXiv preprint).

• Nazari, Ehsan, Paula Branco, and Guy-Vincent Jourdan. "AutoGAN - An Automated Human-Out-of-the-Loop Approach for Training Generative Adversarial Networks." MDPI 11.4 (2023) - 977.

• Nazari, Ehsan, Paula Branco, and Guy-Vincent Jourdan. "Using cgan to deal with class imbalance and small sample size in cybersecurity problems." 2021 18th International Conference on Privacy, Security and Trust (PST). IEEE, 2021.

• Nazari, Ehsan, and Paula Branco. "On oversampling via generative adversarial networks under different data difficulty factors." Third international workshop on learning with imbalanced domains - Theory and applications. PMLR, 2021.

Papers&Snacks

Event Creator and Organizer

• Established and organized a lecture oriented weekly event for Computer Science graduate students

Running Club

Co-Event Organizer

• Co-lead a team of 15+ runners for weekly 5K sessions in Ottawa

UofO's ISAUO club

Volunteer

• Tour leader to welcome new students at the University of Ottawa

Mentorship

Volunteer

• Co-building communities for new students in Ottawa, overseeing groups of 200+ members each