University of Toronto

MSc. in Applied Computing
Artificial Intelligence Concentration

Sep. 2022 - Dec. 2023 • Email

• CGPA: A$^+$

• Thesis (Capstone project): Efficient Neural Network Encoding for 3D Color Lookup Tables

• Courses: Introduction to Causality, Deep Learning: Theory & Datascience, Neural Networks & Deep Learning, Empirical Software Engineering

• Teaching Assistantships: Programming on the Web (Fall'22, Spring'23)

Sharif University of Technology

BSc. in Computer Engineering

Sep. 2017 - Jun. 2022 • Email

• CGPA: 18.64/20 (3.8/4) — 141/140 credits
• Thesis (Capstone project): Anomaly Detection via Explicit Density Estimation
• Selected Courses (All courses): Digital Image Processing (Graduate), Stochastic Processes, Artificial Intelligence, Linear Algebra, Signals & Systems, Probability and Statistics, Design of Algorithms, Data Structures and Algorithms

• Selected Teaching Assistantships (All TAships): Head TA of Artificial Intelligence, supervised graduate TAs in Machine Learning Privacy (Graduate), tutored deep learning workshops in Machine Learning for Bioinformatics (Graduate), in addition to TAing and tutoring recitation classes for undergraduate courses such Bioinformatics, Probability theory and Statistics, Linear Algebra, and Data structures and Algorithms.

• Selected Extracurricular Activities (All activities): Led the scientific team for Iran's largest data science contest on recommender systems (Sharif’s Datadays 2021), Designed interactive AI courses for highschoolers and undergrad freshmen for Sharif University of Technology’s RASTA and MIL events.

Huawei Technologies (Noah's Ark Lab)

Computer Vision Research Scientist

Jun. 2024 - Present • Toronto, ON, Canada

• Led the invention of a patent pending computational optics algorithm that intelligently pre-compensates for subjects' prescriptions, improving human visual acuity of colored images on conventional displays by +10 dB (PSNR) when facing spherical aberrations.
• Working on a first of its kind conversationally interactive camera auto-focus control system for wearable devices.

Tides Medical

Machine Learning R&D Consultant

Jan. 2024 - Present • Toronto, ON, Canada

• Collaborated with biomedical researchers to translate project objectives into Machine Learning formulations.
• Designed and developed custom data annotation tooling and a U-Net-like foundation segmentation model to facilitate annotation and evaluation of histopathology samples, resulting in faster turnaround time for histologists with over 99% prediction accuracies.

Samsung AI Center (Toronto)

Machine Learning Researcher
Intern advised by: M.S. Brown, M.A. Brubaker, D.B. Lindell

May 2023 - Dec. 2023 • Toronto, ON, Canada

• Devised a patent-pending neural framework for efficient representation of color transformations and inverse vision processing for modern Image Processing Pipelines, surpassing the SOTA by over 100x in terms of compression efficacy with enhanced color fidelities.
• Assisted the camera quality team in noise modeling and denoising of RAW quad Bayer sensor outputs.

Vector Institute

Graduate researcher
Intern advised by R.G. Krishnan at University of Toronto's Machine Learning & Computational Healthcare Lab

Oct. 2022 - Dec. 2023 • Toronto, ON, Canada

• Codeveloped a differentiable Bayesian causal structure discovery algorithm with Autoregressive Normalizing Flows that explicitly enforces acyclicity in causal DAGs, achieving the current SOTA for real-world (Sachs & SynTReN) datasets and synthetic benchmarks.
• Developed DyPy & Lightning-Toolbox for fast implementation of complex deep learning research experiments, cutting the amount of raw code in half on average.

Sharif University of Technology

Undergraduate researcher supervised by M.H. Rohban

Dec. 2019 - Jul. 2022 • Tehran, Iran

• Assessed the potential of repurposed adversarial training of robust reconstructive models for visual anomaly detection tasks.
• Developed a novel deep training procedure for anomaly detection using explicit density estimation, on par with SOTA Autoencoder-based methods.
• Studied energy-based and other explicit deep density estimation models and developed TorchDE, a unified pytorch library for deep density estimation.

École Polytechnique Fédérale de Lausanne (EPFL)

Research Assistant
Intern advised by A. Alahi at VITA lab
Research collaborator with LTS4 & MLO students

Jul. 2020 - Dec. 2020 • Lausanne, Switzerland (Remote)

• Explored alternatives to rasterized data representation schemes and codeveloped a Transformer based model that unifies the processing of various perception data representations to predict SOTA reason-aware vehicle trajectories for autonomous driving.
• Studied the preliminary theoretical foundations of deep learning and computed the directional inductive bias of Vision Transformers using Jax, facilitating the identification of the tasks Transformers are better suited for vs. traditional Convolution Neural Networks

Advanced Technology Lab (ZLAB), Fanap Co.

Machine Learning Engineer

Jun. 2020 - Mar. 2021 • Tehran, Iran

• Developed a novel satellite imagery super-resolution residual model for urban maps, capable of performing up to 8x up sampling.
• Created a computer vision deep learning prototyping framework that enables direct Neural Architecture Search over pure PyTorch.

Cafebazaar

Chaos Engineering Intern

Jul. - Oct. 2019 • Tehran, Iran

• Implemented a Kubernetes cluster node failure simulator and designed a cluster monitoring system for internal stress-testing of the underlying infrastructure.
• Designed and implemented a decentralized load-tester that learns and mimics true user behaviour and can scale to simulate up to millions of requests per second, for internal use.

Pido

Computer Vision Engineering Intern

Jun. 2018 - Jun. 2019 • Tehran, Iran

• Co-developed an efficiently accurate OCR engine in C++, based on Google's Tesseract OCR Engine, for extracting information from Iranian debit card scans.