Michael Bidollahkhani

E-mailmichael.bkhani@uni-goettingen.de

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

  • 2024, Track chair, The Eighteenth International Conference on Advanced Engineering Computing and Applications in Sciences; AISys, ICSEA, CENTRIC tracks

Journal review duties

  • 2023, The American Journal of Artificial Intelligence (AJAI)
  • 2020, IEEE Signal Processing Society

Teaching

Autumn Term 2024

Open Thesis Topics

An AI-Based Algorithm development for Early Fault Detection in Compute Continuum SystemsApply

In this thesis, student will explore the current methods in AI to create smart algorithm that can catch problems in computer systems before they turn serious. Think of it as developing a high-tech 'early warning system'. The journey will involve playing with data, crafting algorithms, and running simulations to see how well they work. Plus, you'll get to integrate your creations into real computing systems, making them more reliable and reducing downtimes.

Michael Bidollahkhani Artificial Intelligence

Implementing Edge Computing for Real-Time Predictive Maintenance in Compute Continuum SystemsApply

This research explores the potential of edge computing technologies in enabling real-time predictive maintenance within compute continuum systems. The objective is to develop a framework that utilizes edge computing for immediate data processing and decision-making, enhancing the overall efficiency and responsiveness of maintenance protocols. The thesis will involve both theoretical and practical aspects, including system design, implementation, and testing in real-world scenarios.

Michael Bidollahkhani Edge Computing

Scalability Challenges and Solutions in AI-Based Predictive Maintenance for Large-Scale Compute Continuum SystemsApply

To answer the question of how making AI-driven maintenance work smoothly in huge computing systems, we will need to find out what makes scaling up so tricky and come up with efficient ways to make it better. Student will investigate the scalability challenges associated with implementing AI-based predictive maintenance in large-scale compute continuum systems. They'll get to analyze existing systems, brainstorm new methods, and test how well they work in the real world of large-scale computing maintenance. The research will focus on identifying key scalability issues and developing innovative solutions to enhance the performance and effectiveness of predictive maintenance strategies. It also will include a thorough analysis of current systems, proposal of new methodologies, and evaluation of their impact on large-scale system maintenance.

Michael Bidollahkhani ScalabilityArtificial Intelligence

Theses

  • Implementation of a Liquid Neural Network Control System for Multi-Join 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

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
  • 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

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
  • Liquid Time-Constant Neural Networks (Michael Bidollahkhani), In Interdisciplinary Artificial Intelligence, Series: Nobel Scientific Works, Edition: 1, pp. 163 (Turkey), 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

All publications as BibTex

  • Impressum
  • Privacy
  • about/people/michael_bidollahkhani.txt
  • Last modified: 2023-10-06 11:21
  • by 127.0.0.1