About
I’m a Staff Software Engineer at Capital One. I hold an MSc in Computer Science from the University of British Columbia (2019) and a BSc in Computer Engineering from Sharif University of Technology (2016).
Education
University of British Columbia
Thesis: Machine learning of lineaments from magnetic, gravity and elevation maps
Supervisor: Prof. David Poole
Sharif University of Technology
Research in Machine Learning Lab
Supervisor: Dr. Mahdieh Baghshah
Publications & Talks
University of British Columbia
- Machine learning of lineaments from magnetic, gravity and elevation maps, MSc Thesis, 2019.
- A Convolutional Neural Network for semi-automated lineament detection, Computer & Geosciences, Elsevier, 2021.
- Deep Learning for Automated Lineament Mapping, TGDG Invited Talk, March 2021.
Sharif University of Technology
- Active Distance-Based Clustering using K-medoids, PAKDD 2016.
Industry / Independent
- Identifying blockchain-based cryptocurrency accounts using investment portfolios, arXiv, 2021.
- 3D Geophysical Inversion and Integration, EAGE Invited Talk, 2021.
- TorchMinusOne: A Tool for Deterministic Geophysical Inversion, Goldschmidt Conference, 2021.
Teaching Experience
University of British Columbia
- Computing Platforms for Data Science (DSCI 521)
- Probabilistic Graphical Models (CPSC 532R)
- Advanced Machine Learning (DSCI 575)
- Data Science Programming (DSCI 511)
- Internet Computing (CPSC 317)
Sharif University of Technology
- Digital Systems Design
- Signals and Systems
- Electrical Circuits
- Computer Structure and Languages
- Operating Systems
- Logic Design
- Fundamentals of Programming
Experience
Capital One
Working on backend systems and services for Capital One’s international credit card platform, with a focus on scalable, reliable, and secure distributed systems.
- Design and develop scalable backend services and APIs supporting high-volume credit card products and customer experiences.
- Build and maintain distributed, cloud-based systems with a focus on reliability, performance, security, and operational excellence.
- Collaborate with engineers, product managers, and cross-functional teams to design and deliver high-quality features and services.
- Contribute to technical architecture and engineering practices, helping drive scalable solutions and improve the overall platform.
Force Fleet Tracking ( f.k.a Mojio )
Mojio is a connected mobility platform processing large-scale telematics data from millions of vehicles across North America and Europe.
- Architected and developed scalable APIs on platform, hot-path message processing gateways & ingress, and distributed processing pipelines handling terabytes of vehicle telemetry data daily.
- Designed and implemented large-scale data analytics workflows to transform high-volume telemetry datasets into production-ready AI solutions for end-users.
- Leveraged and maintained different services including Event Hubs, Stream Processing, Cosmos DB, ASP.NET, and Kubernetes to build event-driven, containerized microservices and real-time processing systems.
- Led a large-scale data migration from Azure Cosmos DB to Couchbase, ensuring data consistency, performance optimization, and zero-downtime transition.
- Engineered and maintained video ingestion and live streaming systems, optimizing delivery pipelines for low-latency live streams, replay generation, and secure user access control.
- Built an automated reporting service powered by RAG framework to generate periodic fleet efficiency reports, maintenance summaries, driver behavior analysis (road-score system)
ALS – Geoanalytics
ALS – Geoanalytics (formerly GoldSpot Discoveries ) applies machine learning and data science to mineral exploration, integrating geophysical, geological, and geochemical data.
- Developed 3D models of subsurface survey data using stochastic modeling techniques and Gaussian Process optimization to improve accuracy and uncertainty quantification in geological exploration
- Designed feature extraction pipelines and implemented clustering algorithms on geological datasets using Deep Learning methods to enhance structural interpretation and data-driven decision making.
- Accelerated large-scale kernel computations and reduced memory footprint by implementing Lazy Tensors on GPUs using PyKeOps, enabling efficient processing of high-dimensional geospatial data.
Avestec Technologies
Avestec Technologies provides software solutions for geoscience data analysis, modeling, and inversion.
- Developed the full stack of the inspector platform, Avesoft™, for automated report generation and control of cameras and sensors on Avestec's SKYRON drone using Django.
- Enabled real-time transmission of sensor and image data across multiple protocols, including RS232, UART, XBee-900, and IEEE 802.11, ensuring reliable drone-to-ground communication.
- Designed, trained, and deployed a high-speed CNN model for live crack and defect detection on metal surfaces, running on NVIDIA's Jetson Nano embedded system mounted on the SKYRON drone.
Minerva Intelligence
Minerva Intelligence develops AI-driven tools for mineral targeting and exploration decision support.
- Assisted in developing machine learning models for mineral prospectivity mapping.
- Worked on data integration pipelines combining geological, geophysical, and geochemical datasets.
- Prototyped research ideas and supported senior engineers in deploying analytical tools.