Research Interests
Areas I am actively exploring and hope to contribute to as a graduate student and future researcher.
IoT & Edge Computing Systems
Designing reliable architectures for resource-constrained edge devices, edge-to-cloud data pipelines, and distributed sensing systems. Particular interest in latency-aware protocol design and edge inference optimization for real-world deployments.
Software Engineering for Distributed Systems
Building large-scale distributed systems with a focus on correctness, fault tolerance, and operational maintainability. Interested in microservice design patterns, event-driven architectures, and distributed tracing.
Cloud Computing & DevOps/MLOps
Bridging software engineering and machine learning through reproducible, observable deployment pipelines. Interested in containerized ML serving, model versioning, CI/CD for ML systems, and infrastructure-as-code practices.
Applied Machine Learning for Systems
Applying ML to systems engineering problems: anomaly detection in distributed traces, intelligent resource scheduling, predictive infrastructure management, and applied deep learning for domain-specific tasks.
Research Statement
My research interests sit at the intersection of distributed systems and applied machine learning — particularly IoT & edge computing, software engineering for large-scale systems, and MLOps. I am drawn to systems problems where reliability, observability, and intelligent automation converge.
Background & Context
Industry Experience
My research interests are grounded in real production challenges: distributed system failures at Medical Toxicology, edge latency optimization in FaceCheckIn, and AI API orchestration in MedSpeech.
Academic Preparation
Strong coursework in AI fundamentals (19.25/20), IoT systems (20/20), network security, and software engineering provides the theoretical foundation for systems research.
Current Trajectory
Currently pursuing an M.Sc. in Software Engineering at Islamic Azad University while serving as Teaching Assistant for the Machine Learning course — deepening research methodology and ML theory.
Long-Term Goal
Seeking a Ph.D. position at a research university focused on distributed systems or applied ML. Particularly interested in research groups working on reliable intelligent systems at scale.