Sepehr Mahmoodian
| s.mahmoodian@gwdg.de | |
| Address | RZGö, Burckhardtweg 4, Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG), room 1.207 |
Biography
Sepher is a PhD student with a background in computer architecture and embedded systems. He holds a Master's degree from Razi University of Kermanshah and has several years of experience in IoT engineering and university-level teaching. He pursues a PhD under the supervision of Prof. Dr. Julian Kunkel, with a general research focus on the convergence of high-performance computing (HPC) and cloud technologies and is particularly interested in how concepts and technologies from both domains are blending in modern scientific workflows, and the implications this has for compute, storage, and system management.
ORCID: https://orcid.org/0009-0000-0569-8047
LinkedIn: https://www.linkedin.com/in/sepehr-mahmoodian
Research Interests
- HPC-cloud convergence
- High-Performance Data Ingestion
- Hybrid workflows and system integration
- Embedded systems and IoT
- AI/ML in smart systems
- Metaheuristic optimization techniques
Teaching
Open Thesis Topics
Containerized Edge AI: Deploying AI Services with Lightweight KubernetesApply
This thesis investigates how AI inference services can be deployed, managed, and evaluated in edge computing environments using lightweight container orchestration. As AI applications move closer to sensors, users, and embedded devices, edge platforms must support reliable execution under limited compute, memory, storage, and network conditions. Container technologies and lightweight Kubernetes distributions such as K3s or MicroK8s provide a promising approach for packaging, scaling, updating, and monitoring AI services outside traditional cloud data centers. The project focuses on building a reproducible edge AI deployment environment in which one or more selected AI inference services are containerized and deployed on a small edge setup or simulated edge cluster. The student will study the practical trade-offs between simple container-based deployment and Kubernetes-based orchestration, including how orchestration affects startup time, inference latency, resource consumption, scalability, service monitoring, and update mechanisms. The thesis will evaluate whether lightweight Kubernetes provides practical benefits for operating AI services at the edge compared to simpler deployment approaches. The expected outcome is a working prototype, a reproducible deployment workflow, and a practical analysis of the advantages and limitations of container orchestration for edge AI workloads.
Deployment and Evaluation of Lightweight AI Models on Edge and Embedded SystemsApply
This thesis investigates how lightweight AI models can be implemented, optimized, and evaluated on resource-constrained edge and embedded systems. Many practical AI applications require local processing close to sensors, devices, or users in order to reduce latency, limit data transfer, improve privacy, or operate without continuous cloud connectivity. However, embedded and edge platforms often provide limited memory, compute power, energy availability, and hardware acceleration compared to conventional servers. The project focuses on selecting a representative AI task, such as image classification, sensor-data analysis, anomaly detection, or simple signal processing, and deploying one or more suitable models on an edge or embedded platform. The student may investigate techniques such as quantization, pruning, TinyML, lightweight neural networks, classical machine learning baselines, or hardware-aware inference optimization. The implementation will be evaluated with respect to accuracy, inference latency, memory usage, resource consumption, deployment complexity, and robustness under constrained runtime conditions. The thesis will compare different model and deployment choices to identify practical trade-offs for AI processing on small devices. The expected outcome is a reproducible prototype and a structured evaluation that provides guidance on selecting and deploying AI models for edge and embedded environments.