Ali Zonoozi Email & Phone Number

Toronto, On, Ca

Toronto, Ontario, Canada

Q2 is a financial experience company dedicated to providing digital banking and lending solutions to banks, credit unions, alternative finance, and fintech companies in the U.S. and internationally. Today, one out of 10 digital banking customers in America is using Q2’s single platform to bank. - As part of the AI centre of excellence at Q2 we develop assisting software and copilot applications powered by generative AI technology to help bankers better optimize their day to day tasks.

Toronto, Ontario, Canada

Sensibill is a receipt management company that provides a receipt extraction and digitization solution to financial institutions. With this service, end users are able to capture their purchase receipts and invoices, keep track of their expenses and organize their spendings efficiently. At Data and AI team in Sensibill, we leverage anonymized data in the receipts and use machine learning solutions to extract user and merchant level insights, enrich the extracted data using various NLP techniques and provide personalized insights to the users and clients.

Singapore

In Grab we use data and technology to improve everything from transportation to payments and logistics across a region of more than 620 million people. Working with governments, drivers, passengers, and charities, we aim to unlock the true potential of the region by solving problems that hinder progress.Grab began as a taxi-hailing app in 2012, but has extended its product platform to include private car services (GrabCar & GrabShare), motorcycle taxis (GrabBike), social carpooling (GrabHitch) and last mile delivery (GrabExpress). We are focused on pioneering new commuting alternatives for drivers and passengers with an emphasis on speed, safety, and reliability. Grab currently offers services in Singapore, Indonesia, Philippines, Malaysia, Thailand and Vietnam; however, it also has R&D offices in Bangalore, Beijing, & Seattle.

Singapore

Thesis title: Machine Learning-Based approaches for Large-Scale Temporal Data Analytics.---As a PhD scholar in the field of Data Science and Machine Learning, I have been part of Data Management and Analytics Graduate Lab (DMAL) in Nanyang Technological University (NTU) and Data Analytics Department (DAD) in Institute for Infocomm Research, A*Star.During my PhD, I have worked on multiple projects related to data stream mining and Machine Learning based approaches for temporal data analytics. My projects involved optimization, mathematical formulations, distributed and parallel machine learning for large scale data and deep learning based approaches for spatio-temporal forecasting and abnormal event detection problems.

Singapore

• SINGA scholarship award• Attached to Data Analytics Department• Research area: Data Stream Mining, Parallel and Distributed Machine Learning, Deep neural networks• During my attachment I have collaborated with DBS joint lab in I2R for a project on concept tracking on customers spending patterns.

Tehran, Iran

• Business Intelligence• Data Analysis and Report,• Data Warehousing, Database Administration• Fraud Detection & Anti-Money Laundry Systems

Singapore

• Mobile Activity Recognition Using Data Stream Mining Project• Data Mining Department

Tehran

Android Programming- News Reader Application on Android TV devices- mBanking Application on Android Platform

Tehran

-Working on a Desktop application to manage and control data loggers sold tocustomers of the company.-Developed a Web based Portal to manage and view telemetric data for SamaCorporation using ASP .Net and MS. SQLServer technology.

Doctor Of Philosophy (Ph.D.), Data Science, 4.75/5

Thesis title: Machine Learning-Based Approaches for Large-Scale Temporal Data Analytics.

Master Of Science (M.Sc.), Computer Software Engineering, 17.08/20

Thesis title: User Modeling and adapting user interface in tactile mobile devices using stereotype building

Bachelor Of Science (B.Sc.), Computer Engineering, 16.91/20

Activities and Societies: +head editor of the Computer engineering and Information Technology department internal journal. +Member.