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

  • 2026, Frontiers in Computational Neuroscience
  • 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

Monitoring and Controlling Irregular Behavior in Agentic AI Systems with Access to Digital and Physical ResourcesApply

In this thesis, researchers works on a new class of risks emerging from agentic AI systems that are able to perform actions, use tools, access resources, execute software, control devices, or make operational decisions. As AI systems move from passive recommendation toward automated decision-making and action execution, it becomes important to monitor whether an agent behaves within its allowed boundaries. The main objective is to design a lightweight monitoring and control framework that can detect irregular, unauthorized, or suspicious behavior in AI agents. Such behavior may include executing unexpected commands, accessing restricted files, modifying system settings, using tools outside the assigned task, consuming abnormal resources, or making decisions that conflict with predefined rules and human intentions. The project may focus on software agents operating on desktop or operating-system-level environments, AI assistants executing automated tasks, business-process agents managing resources, or simulated cyber-physical agents interacting with actuators. The student will define allowed and disallowed behavior patterns, collect or simulate agent activity logs, and develop a monitoring mechanism that can detect deviations from expected behavior. A Master thesis may focus on implementing and evaluating a prototype supervisory system that monitors agent actions and detects rule violations. A PhD-level thesis may extend the work by developing a more general framework for runtime agent governance, combining rule-based monitoring, machine learning, anomaly detection, policy checking, formal constraints, and human-in-the-loop approval mechanisms. A possible motivating example is a business or resource-management scenario in which an autonomous assistant supervises financial or operational decisions and prevents risky or unauthorized actions by another actor. This illustrates the broader need for trusted third-party AI supervisors that monitor agents and enforce operational boundaries.

Michael Bidollahkhani Agentic AIAI SafetyAutonomous AgentsBehavior MonitoringAnomaly DetectionPolicy EnforcementHuman-in-the-Loop AIResponsible AIResource Access Control

Environmental Sustainability Labeling for AI Services Based on Energy, Runtime, and Resource UsageApply

This thesis focuses on designing a transparent labeling system for evaluating the environmental impact of AI services. The main idea is to measure how much computational effort, runtime, memory, CPU, GPU, and energy an AI service requires, and then translate these measurements into a simple and understandable label. The labeling concept can be inspired by energy labels used for household appliances, such as A, B, C, D, and E. However, the student has freedom to define the exact scoring method, evaluation metrics, and visualization style. The AI services may include classification models, generative AI models, anomaly detection tools, scheduling systems, or other selected AI applications. A Bachelor thesis can focus on implementing a prototype and evaluating a small number of AI models. A Master thesis can extend the work by designing a more formal scoring model, comparing multiple infrastructures, or including sustainability indicators such as estimated carbon impact.

Michael Bidollahkhani AI SustainabilityGreen AIEnergy EfficiencyEnvironmental LabelingResponsible AI

Meta Machine Intelligence for Adaptive Error Detection in High-Performance Computing SystemsApply

This thesis investigates how multiple AI models can be used together for detecting errors, failures, and abnormal behavior in high-performance computing systems. Instead of relying on one fixed detection model, the system should analyze the current situation and select the most suitable model based on system context. The context may include workload behavior, node status, resource usage, error patterns, log messages, or historical system behavior. The student can explore different strategies such as model selection, ensemble learning, rule-based routing, machine learning-based routing, or LLM-assisted log interpretation. A Bachelor thesis may compare several anomaly detection models on HPC or server logs. A Master thesis may design an adaptive Meta Machine Intelligence layer that selects the best model depending on the current system condition.

Michael Bidollahkhani High-Performance ComputingError DetectionAnomaly DetectionAdaptive AIMeta-Machine IntelligenceSystem Reliability

Lightweight Edge AI for Detecting Irregular Behavior in Device and Sensor LogsApply

This thesis aims to develop a lightweight anomaly detection system for device logs, sensor data, or small-scale monitoring environments. The focus is on AI methods that can operate under limited computational resources, such as embedded systems, Raspberry Pi devices, IoT nodes, or edge computing environments. The system should detect irregular behavior using indicators such as timestamp frequency, error messages, temperature changes, signal variations, or resource usage. Students can freely choose the application domain, for example smart devices, environmental sensors, robotics, small server nodes, or industrial monitoring. A Bachelor thesis may compare lightweight machine learning models for anomaly detection. A Master thesis may investigate TinyML, online learning, model compression, or hybrid signal-processing and AI-based detection methods.

Michael Bidollahkhani Edge AITinyMLAnomaly DetectionEmbedded SystemsDevice LogsIoT Monitoring

Benchmarking AI Models for Resource-Aware System MonitoringApply

This thesis focuses on benchmarking different AI models for monitoring tasks under resource constraints. The student will compare models not only based on accuracy, but also based on runtime, memory usage, energy consumption, inference latency, and deployment complexity. The monitoring task may involve anomaly detection, failure prediction, log classification, sensor analysis, or workload prediction. The project gives students freedom to select the models, datasets, and evaluation environment. A Bachelor thesis may compare a small number of models for one monitoring task. A Master thesis may design a more systematic benchmarking framework and propose guidelines for selecting models based on system constraints.

Michael Bidollahkhani BenchmarkingResource-Aware AISystem MonitoringModel EvaluationEfficient AI

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
  • 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

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