Michael Bidollahkhani

E-mailmichael.bkhani@uni-goettingen.de
AddressRZGö, Burckhardtweg 4, Georg-August Universität Göttingen, room 2.102

Biography

Michael Bidollahkhani, under the supervision of Prof. Dr. Julian Martin Kunkel, is a dedicated computer engineer and machine intelligence researcher. His expertise in software engineering and automated systems is recognized by his Young Scientist award from the YSF of Iran in 2017 and 2023. As a member of the National Elites Foundation and the ACM, Michael is actively engaged in the development of advanced intelligent systems.
ORCID: 0000-0001-8122-4441
Google Scholar: https://scholar.google.com/citations?user=_rLezLYAAAAJ

Research Interests

  • Complex Systems
  • Computational Intelligence
  • Cognitive Modeling
  • Neural Information Processing
  • Neuroinformatics
  • Emergent Intelligence
  • Artificial Neural Networks
  • Cognitive Robotics

Projects

Advisory roles

  • 2026, Program Committee Member, The 40th Annual AAAI Conference on Artificial Intelligence -AI Alignment Track (AAAI-26-AIA)
  • 2025, Session Chair, The International Conferences on Digital Technology Driven Engineering 2025 (hosted by Jordan University of Science and Technology)
  • 2024, Track chair, The Eighteenth International Conference on Advanced Engineering Computing and Applications in Sciences; AISys, ICSEA, CENTRIC tracks

Journal review duties

  • 2025, FinTech and Sustainable Innovation (FSI)
  • 2023, The American Journal of Artificial Intelligence (AJAI)
  • 2020, IEEE Signal Processing Society

Teaching

Autumn Term 2027

Summer Term 2027

Autumn Term 2026

Summer Term 2026

Autumn Term 2025

Summer Term 2025

Autumn Term 2024

Open Thesis Topics

Designing an Environmental Sustainability Labeling System for AI Services Based on Resource UsageApply

This thesis addresses the need for transparency in the energy and resource consumption of AI services by proposing a standardized environmental sustainability labeling system. The project analyzes the energy consumption and computational load of different AI tasks—such as classification, generation, and scheduling—and translates them into simple, interpretable labels similar to those used for home appliances (e.g., A++ to E). The student will collect runtime and resource usage data for AI models, evaluate their environmental impact, and propose a standardized method to present this information to users and developers.

Michael Bidollahkhani AI SustainabilityEnergy EfficiencyEnvironmental LabelingResponsible AI

Meta Machine Intelligence (MMI) for Error Detection in High-Performance Computing SystemsApply

This project investigates how context-sensitive AI models can improve early fault detection in high-performance computing (HPC) environments. The objective is to implement an adaptive mechanism that selects among pre-trained machine learning models based on system state, workload behavior, and observed error patterns. The research includes defining relevant system contexts, integrating multiple detection models, and evaluating their effectiveness under different runtime conditions using real or simulated log datasets. The expected outcome is improved fault detection reliability while maintaining scalability across heterogeneous HPC architectures.

Michael Bidollahkhani Error DetectionHigh-Performance ComputingAdaptive AISystem MonitoringMeta-Machine Intelligence

Multi-Model Job Scheduling for Mixed Computing EnvironmentsApply

This thesis focuses on designing a context-aware job scheduling system powered by multiple AI models for heterogeneous computing environments, including cloud, edge, and HPC systems. The scheduler dynamically selects the most suitable scheduling model based on job characteristics, resource availability, and historical performance data. The study involves developing an adaptive AI-based scheduler that responds to varying resource types and infrastructure constraints, with the goal of improving overall scheduling efficiency in complex, multi-layered computing systems.

Michael Bidollahkhani Job SchedulingAI for ComputingHeterogeneous SystemsPerformance OptimizationMeta-Machine Intelligence

Lightweight AI for Detecting Irregular Behavior in Device LogsApply

This thesis aims to develop a minimal and efficient AI-based anomaly detection system for identifying irregular behavior in log files generated by small-scale devices or sensors. The system is optimized for environments with limited memory and computational resources, such as embedded systems and low-power devices. Contextual indicators—such as temperature readings, timestamp frequency, and error patterns—are incorporated to improve detection accuracy and relevance. The resulting solution targets real-world monitoring scenarios in edge computing and IoT deployments.

Michael Bidollahkhani Anomaly DetectionEmbedded SystemsDevice LogsLightweight AI

Interactive Dashboard for Monitoring AI Performance in System MaintenanceApply

This project involves the design and implementation of a web-based interactive dashboard for monitoring and visualizing AI behavior in system maintenance tasks. The dashboard presents information such as tool selection, confidence levels, warning predictions, and input variations over time in an interpretable and user-friendly manner. It supports AI models that can be configured with different data sources and toolsets, aiming to enhance transparency, trust, and usability in predictive maintenance systems operating in dynamic, multi-model environments.

Michael Bidollahkhani VisualizationAI MonitoringWeb DashboardSystem Maintenance

Theses

  • Implementation of a Liquid Neural Network Control System for Multi-Joint Cyber Physical ARM, Michael Bidollahkhani (Master's Thesis), Advisors: Ferhat Atasoy, Abdellatef Hamdan, 2023-06, BibTeX
  • Extract and mining government services, especially USO and their impacts on the development of rural communities using data mining algorithms and artificial intelligence, Michael Bidollahkhani (Bachelor's Thesis), Advisors: I. Soleimani, A. Shahbahrami, 2016, BibTeX

Publications

2026

  • AI-Powered Smart Cities (Michael Bidollahkhani), In From Smart Cities to the Metaverse, pp. 35-50, Taylor & Francis, 2026 BibTeX URL
  • Design and Implementation of Integrated AI Scheduler for Dynamic Cloud Workloads Allocation in Kubernetes Environments (Michael Bidollahkhani, Aasish K. Sharma, Sachin P. Nanavati, Mohsen Seyedkazemi Ardebili, Giorgi Mamulashvili, Mirac Aydin, Felix Stein, Mojtaba Akbari, Julian M. Kunkel), In Proceedings of the Future Technologies Conference (FTC) 2025, Volume 1, Lecture Notes in Networks and Systems, pp. 398-420, (Editors: Kohei Arai), Springer Nature Switzerland (Cham, Switzerland), Future Technologies Conference, FTC, ISBN: 978-3-032-07986-2, 2026 BibTeX DOI

2025

2024

  • Poster: Predictive Maintenance in Server Farms with Time Series Analysis (Michael Bidollahkhani, Julian Kunkel), 2nd NHRConference 2024 at NHR4CES@TUDarmstadt, Darmstadt, Germany, 2024-09-09 BibTeX URL PDF
  • HOSHMAND: Accelerated AI-Driven Scheduler Emulating Conventional Task Distribution Techniques for Cloud Workloads (Michael Bidollahkhani, Aasish Kumar Sharma, Julian Kunkel), IEEE Computers, Software, and Applications Conference, pp. 1-8, IEEE, IEEE, COMPSAC 2024, 2024-07 BibTeX
  • Winner – Best Presentation Award (IARIA Cloud Computing 2024) (Michael Bidollahkhani, Julian Kunkel), Award / Research Achievement, IARIA (Venice, Italy), ISBN: 978-1-68558-156-5, 2024-04 BibTeX
  • Distracted AI: Integrating Neuroscience-Inspired Attention and Distraction Learning in ANN (Michael Bidollahkhani, M. Raahemi, P. Haskul), 2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing, pp. 1-8, IEEE, IEEE, AISP, 2024-02 BibTeX DOI
  • D2.3 AI Scheduler Prototypes for Storage and Compute (Michael Bidollahkhani, Aasish Kumar Sharma), Project Deliverable (D2.3), Zenodo, 2024-01-04 BibTeX URL DOI
  • Comparing Fault-tolerance in Kubernetes and Slurm in HPC Infrastructure (Mirac Aydin, Michael Bidollahkhani, Julian Kunkel), Proceedings of the 18th International Conference on Advanced Computing (ADVCOMP 2024), pp. 40-49, Venice, Italy, ISSN: 2308-4499. ISBN: 978-1-68558-184-8, 2024 BibTeX URL
  • Revolutionizing system reliability: The role of AI in predictive maintenance strategies (Michael Bidollahkhani, Julian Kunkel), IARIA CloudComputing 2024 Conference, pp. 1-9, Venice, Italy, ISSN: 2308-4294. ISBN: 978-1-68558-156-5, 2024 BibTeX URL
  • Appreciated Team – Among Best Projects in IT for Law (ReMeP24 Hackathon): RAGdoll (Mohamed Reda Arsalan, Michael Bidollahkhani, Samanda Kortoçi, Mohammad Hamed Pakizehdel, Katja Breitenfelder, Nhu An Trinh), Hackathon / Research Achievement (ReMeP24), Ministry of Justice (Vienna, Austria), 2024 BibTeX URL
  • RAGdoll AI Risk Assessment System (Mohamed Reda Arsalan, Michael Bidollahkhani, Samanda Kortoçi, Mohammad Hamed Pakizehdel, Katja Breitenfelder, Nhu An Trinh), Project / Prototype (RAGdoll), Georg-August University of Göttingen (with LIPIT Program & GWDG) (Göttingen, Germany), 2024 BibTeX URL

2023

  • LTC-SE: Expanding the Potential of Liquid Time-Constant Neural Networks for Scalable AI and Embedded Systems (Michael Bidollahkhani, Ferhat Atasoy, Hamdan Abdellatef), In arXiv preprint arXiv:2304.08691, 2023-04-18 BibTeX
  • A Novel Approach for Muscle Fatigue Disorders Detection Using EMG Based Time-Constant Neural Networks (Michael Bidollahkhani, F. Atasoy), In Gazi Journal of Engineering Sciences (2), 2023 BibTeX URL
  • LoRaline: A Critical Message Passing Line of Communication for Anomaly Mapping in IoV Systems (Michael Bidollahkhani, O. Dakkak, A. S. M. Alajeeli, B. S. Kim), In IEEE Access (11), pp. 18107-18120, 2023 BibTeX
  • Real-Time Building Management System Visual Anomaly Detection Using Heat Points Motion Analysis Machine Learning Algorithm (Michael Bidollahkhani, Isa Avci), In Tehnički vjesnik (30), pp. 318–323, 2023 BibTeX
  • GENIE-NF-AI: Identifying Neurofibromatosis Tumors using Liquid Neural Network (LTC) trained on AACR GENIE Datasets (Michael Bidollahkhani, Ferhat Atasoy, Elnaz Abedini, Ali Davar, Omid Hamza, Fırat Sefaoğlu, Amin Jafari, Muhammed Nadir Yalçın, Hamdan Abdellatef), In arXiv preprint arXiv:2304.13429, 2023 BibTeX URL
  • Liquid Time-Constant Neural Networks (Michael Bidollahkhani), In Interdisciplinary Artificial Intelligence, Series: Nobel Scientific Works, Edition: 1, pp. 163 (Turkey), 2023 BibTeX URL
  • SIVI ZAMAN SABİTLİ SİNİR AĞI (Michael Bidollahkhani, Ferhat Atasoy), In DİSİPLİNLERARASI YAPAY ZEKÂ ARAŞTIRMALARI, Edition: 1, pp. 163-179, Nobel Yayıncılık (Ankara, Türkiye), ISBN: 978-625-398-981-1, 2023 BibTeX URL

2018

  • The Neural Connection Spot (Michael Bidollahkhani, S. Darbarpanah), The International Conference on New Horizons in the Engineering Science, Istanbul, Turkey, 2018 BibTeX
  • The RPAT Algorithm for Politician Assessment and Evaluation (Michael Bidollahkhani, F. Bidollahkhani), The International Conference on New Horizons in the Engineering Science, Istanbul, Turkey, 2018 BibTeX

2017

  • Parallel programming Application on Medical Image Processing: MRI contours matching algorithm based on GPU accelerated methods for Tumor differential Analysis (Michael Bidollahkhani), International Congress on Science and Engineering, Hamburg, Germany, 2017 BibTeX
  • Subjects Extraction and text data classification by CHERNOFF algorithm and implementation with Java general-purpose computer programming language (Michael Bidollahkhani, M. F. Masouleh), IEEE Second National and First International Conference on Soft Computing, IEEE, IEEE, Guilan, Iran, 2017 BibTeX

2015

  • Optimization of Artificial Retina implant's vision (Michael Bidollahkhani, S. Darbarpanah), International Conference on Science and Engineering of Istanbul Technical University (ITU), Istanbul Technical University, Istanbul, Iran, 2015 BibTeX

2014

  • A Survey on Different Strategies on Preparing Data for Data Mining (Michael Bidollahkhani, M. F. Masouleh), ISC National conference on Soft computing at Guilan technical university, Guilan technical university, Guilan, Iran, 2014 BibTeX

University of Göttingen (eCampus)
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Scalable AI for Predictive Maintenance & System Reliability
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GitLab (GWDG)

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All publications as BibTex

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  • Last modified: 2025-08-07 13:02
  • by Michael B. Khani