Abu Yousha Mohammad Abdullah

Healthcare project and research professional with an MSc in Public Health Sciences and experience leading digital-health implementation, authoring RFP responses, and supporting government and private health providers.

Led Therap

Connect, a connected-device application for automated health-data collection, coordinating users, product stakeholders, and developers. Combines public-health research, statistical evaluation, and stakeholder feedback synthesis with practical experience in requirements gathering, provider training, and cross-functional delivery.

Research Intern at York Region Public Health (2025-11 – Present)

• Enabled public-health staff to explore measles misinformation and sentiment through a custom web dashboard deployed with AWS Amplify by gathering reporting requirements and collaborating with technical colleagues on interactive visualizations.
• Translated stakeholder needs into actionable Jira user stories and acceptance criteria by clarifying requirements, maintaining backlog items, and contributing to sprint planning and reviews.
• Prepared reporting-ready records with consistent application inputs by using basic SQL to extract and validate records and developing pipeline controls and status displays with Python-based backend services.
• Resolved authentication, access-permission, and API-connection issues across development, staging, and production by investigating AWS Cognito, API Gateway, and Lambda integrations and correcting environment-specific configurations.
• Established documented steps for release verification and team handoff by creating checklists and runbooks in Microsoft Word and Jira covering deployment, configuration, and application operating procedures.

Training and Content Specialist at Therap (BD) Ltd (2022-04 – 2024-08)

• Led the Therap Connect project, a digital-health application that automatically captures vitals, sleep, and weight measurements from connected devices, by coordinating healthcare users, product stakeholders, and developers on workflows that reduce reliance on manual data entry.
• Supported healthcare procurement submissions by authoring formal RFP responses that mapped product capabilities to client requirements and applicable healthcare policy frameworks.
• Made beta-stage feedback actionable as usability and device-integration requirements by liaising between users and developers, including feedback on making the interface easier for older adults to understand; used Jira tickets to support follow-up on reported application issues.
• Supported adoption among government and private health providers by delivering virtual training on software supporting person-centred documentation for individuals with intellectual and developmental disabilities.
• Made complex healthcare workflows accessible to users with varied technical backgrounds by authoring HTML/CSS user guides, release notes, and reference content in an Oracle environment, alongside tutorial scripts, FAQs, and instructional materials.
• Supported state-specific implementation of specialized modules, including a Training Management System, by collaborating with developers to translate customization requirements into user-friendly functionality.
• Informed improvements to module performance and data collection quality by applying logistic regression, decision trees, and time-series analysis to user behaviour and temporal health data; provided statistical guidance and documented data processing and analysis workflows in SOPs.

Graduate Research Assistant at University of Waterloo (2025-04 – Present)

• Produced an integrated research dataset combining national COVID-19 outcomes, 12 government-policy indicators, and 5,971 Reddit posts by aligning heterogeneous sources in Python and pandas and documenting data-quality and processing rules.
• Generated weekly and biweekly misinformation and sentiment indicators by assessing annotation quality in a stratified 2,000-post dataset, applying NLP classifiers, and aggregating predictions in Python.
• Established evidence on the forecasting value of policy and social-media indicators by comparing XGBoost models with an epidemiological-only baseline using chronological data partitions and interpreting predictor contributions with SHAP.
• Communicated research methods, findings, and limitations through reports, manuscripts, and a public master’s thesis, using Overleaf for writing and Python and Claude Design for supporting visualizations.

Graduate Teaching Assistant at University of Waterloo (2024-09 – 2025-12)

• Supported learning across HLTH 100, 101, 104, and 333 by explaining health promotion, health systems and policy, epidemiological methods, and data cleaning, visualization, and analysis in R.

MSc in Public Health Sciences – University of Waterloo (2024-09 – 2026-09)

MSc in Computer Science Engineering – BRAC University (2023)

BSc in Electrical & Electronic Engineering – BRAC University (2016-01 – 2020-04)