Dr. Mahdee Jodayree

Ph.D. in Computer Science 2019 – 2024

McMaster University, Faculty of Engineering — Hamilton, Ontario

Dissertation: Distributed Machine Learning and Secure Federated Learning for IoT and Healthcare Systems

• Research focused on distributed and secure machine learning systems with direct relevance to computer engineering topics such as cloud computing, virtualization, embedded intelligence, and distributed system architecture.
• Developed transformer-based and distributed ML models implemented in Python and Linux environments, supporting research themes in autonomous systems, real-time analytics, and hardware-aware optimization.
• Designed cryptographic verification and consistency-preservation mechanisms aligned with secure computing, privacy-preserving architectures, and reliability of cloud-edge intelligent systems.
• Built reproducible cloud-based experimentation pipelines and resource-efficient learning workflows applicable to embedded systems, IoT devices, and constrained computing environments.
• Contributed peer-reviewed research demonstrating high-impact scholarly activity and potential for developing an internationally recognized research program in computer engineering.

Master of Science in Information Systems 2016 – 2018

Athabasca University — Athabasca, Alberta

Dissertation: Machine-learning–driven predictive load balancing for cloud services

• Conducted research on predictive modeling, cloud system optimization, and distributed decision frameworks connected to cloud computing, virtualization, and high-availability engineering systems.
• Developed analytical and computational models relevant to system-level performance, resource management, and scalable cloud architectures used in modern computer engineering workflows.

Bachelor of Science in Computer Science and Mathematics 2005 – 2013

Carleton University — Ottawa, Ontario

• Completed advanced coursework in algorithms, discrete mathematics, optimization, and computation theory that form the foundation of computer architecture, embedded systems, and autonomous system modeling.
• Strengthened programming fundamentals essential for developing distributed ML systems, embedded intelligence, and cloud-native engineering technologies.

Ph.D. Research – Distributed, Secure, and Cloud-Integrated Machine Learning Systems

McMaster University, Department of Computing and Software — 2019–2024

• Conducted research on distributed machine learning systems and secure federated learning with direct relevance to cloud computing, embedded intelligence, and computer engineering.
• Designed transformer-based temporal models and distributed ML pipelines applicable to autonomous systems, real-time analytics, and robotics-oriented perception modeling.
• Developed cryptographic verification mechanisms supporting privacy-preserving computation, system integrity, and reliability across virtualized and cloud-edge environments.
• Built reproducible cloud workflows and resource-efficient learning pipelines supporting ML execution on IoT devices, constrained hardware, and embedded systems.
• Published peer-reviewed research demonstrating high-impact scholarly work aligned with computer architecture–aware optimization, secure distributed computing, and advanced ML system design.

Master’s Research – Predictive Modeling and Cloud Systems Optimization

Athabasca University — 2016–2018

• Developed predictive modeling and distributed decision frameworks for cloud workload balancing, contributing directly to virtualization-aware optimization and scalable cloud engineering systems.
• Created analytical models and computational methods related to performance prediction, resource scheduling, and reliability of distributed cloud services.
• Gained experience with system-level modeling relevant to computer engineering, cloud orchestration, and real-time system optimization.

Foundational Research Experience – Algorithms, Systems, and Computational Modeling

• Completed foundational research in algorithms, discrete mathematics, and systems modeling that support modern computer engineering topics such as embedded system computation, optimization frameworks, and architectural analysis.
• Developed strong programming fundamentals enabling implementation of distributed ML systems, embedded intelligence, and cloud-native engineering workflows.

LLM-Driven Code Transformation and Distributed System Validation

• Designed and fine-tuned transformer-based models for structured code transformation tasks, with applications to autonomous systems, intelligent computation, and secure distributed execution.
• Developed validation workflows for functional-equivalence checking, applying system-level verification principles relevant to secure computing and reliability across cloud and embedded platforms.
• Implemented cloud-native training and inference pipelines using containerized environments, enabling scalable experimentation and reproducible engineering workflows.

Cloud-Integrated Machine Learning Pipelines for Edge and IoT Devices

• Developed distributed ML pipelines that operate across cloud and edge devices, supporting embedded intelligence, constrained hardware execution, and virtualization-aware deployment strategies.
• Built real-time processing workflows for IoT environments, integrating model optimization, resource-efficient computation, and secure data pathways aligned with modern computer engineering practices.
• Created monitoring and orchestration workflows using Linux and containerized systems to ensure reliability, performance, and system integrity in cloud-edge deployments.

Autonomous System Modeling Using Reinforcement Learning and Temporal Transformers

• Designed reinforcement learning and transformer-based models for decision-making, trajectory prediction, and temporal behavior analysis in autonomous and robotics-oriented systems.
• Developed simulation workflows and sequence-modeling architectures that support real-time computation, system-level adaptation, and intelligent system behavior.
• Integrated computational models with embedded system constraints to support hardware-aware autonomy and efficient deployment in resource-limited environments.

Secure Distributed Computing and Privacy-Preserving Architecture Design

• Built cryptographic verification components and consistency-preservation mechanisms that enhance reliability, privacy, and trust in distributed learning and cloud-integrated systems.
• Developed secure update aggregation workflows, adversarial robustness checks, and integrity-verification strategies aligned with secure computing and modern distributed architecture requirements.
• Engineered reproducible experimentation environments for evaluating system performance, distributed model behavior, and fault-tolerant engineering design principles.