Ali Beikmohammadi Phd Student

• Michaela Hörnfeldt, Safe Exploration in Reinforcement Learning: Safe Q-learning in a Grid-World,Master's Thesis in Computer and Systems Sciences, Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (June 2022).
• Omar Zia Toor, A Deep Reinforcement Learning Framework for Optimizing Fuel Economy of Vehicles,Master's Thesis in Artificial Intelligence, Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (June 2022).
• Simon Carlén, A Statistical and Machine Learning Approach to Air Pollution Forecasts,Master's Thesis in Artificial Intelligence, Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (September 2022).
• Mohammed Luqman, A comparative study of nature-inspired metaheuristic algorithms on sustainable road network planning,Master's Thesis in Artificial Intelligence, Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (September 2022).
• Bowen Meng, Deep reinforcement learning to the active control of ultra-low Reynolds number flows,Master's Thesis in Artificial Intelligence, Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (December 2022).
• Oscar Montilla Tabares, Predict failures and minimize costs based on sensor readings using deep learning,Master's Thesis in Computer and Systems Sciences, Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (June 2023).
• Daniella Blomberg, Claudio Bolzani, Reward machines for cooperative multi-agent reinforcement learning,Master's Thesis in Computer and Systems Sciences, Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (June 2023).
• Jingwen Zhao, Machine Learning-Based Treatment Recommendation for Patients with Obstructive Sleep Apnea,Joint Master's Thesis in Health Informatics, Karolinska Institutet & Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (July 2023).
• Laura Zubeidat, Towards gender fairness in machine learning algorithms,Joint Master's Thesis in Health Informatics, Karolinska Institutet & Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (July 2023).
• (Majid Hassanabadi), Bennet Voss, Collective AI Intelligence: From Single to Multi Agent Reinforcement Learning,Master's Thesis in Computer and Systems Sciences, Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (November 2023).
• (Biwen Zhu, Pedro Diniz), Fredrik Hammar, Benchmarking and Environment Design for Multi Agent Reinforcement Learning Algorithms,Master's Thesis in Computer and Systems Sciences, Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (February 2024).
• Ruchi Gupta, Danish Hashmi, Leveraging Machine Learning for optimal Order Lead Time prediction in Supply Chain Management,Master's Thesis in Computer and Systems Sciences, Stockholm University, Sweden, Main supervisor: Ali Beikmohammadi (May 2024).
• Yanjun Wang, Drug Discovery and Development by Reinforcement Learning,Joint Master's Thesis in Health Informatics, Karolinska Institutet & Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (May 2024).
• Michel Laji, Developing and Evaluating Naïve Transformer model for Seizure Detection on EEG,Joint Master's Thesis in Health Informatics, Karolinska Institutet & Stockholm University, Sweden, Main supervisor: Ali Beikmohammadi (May 2024).
• América Castrejón, Leaf Disease Detection Using Vision Transformers (ViT),Master's Thesis in Artificial Intelligence, Stockholm University, Sweden, Main supervisor: Ali Beikmohammadi (June 2024).
• Alfreds Lapkovskis, Natalia Nefedova, Advancements in Agriculture: Multimodal Deep Learning for Enhanced Plant Identification,Master's Thesis in Artificial Intelligence, Stockholm University, Sweden, Main supervisor: Ali Beikmohammadi (June 2024).
• Qingyu Huang, André Granberg, Federated Learning Empowers Agriculture: Collaborative Intelligence for Decentralized Crop Analysis and Management,Master's Thesis in Artificial Intelligence, Stockholm University, Sweden, Main supervisor: Ali Beikmohammadi (June 2024).
• Eric Hallberg, AI Integration: Exploring Technology and Human Challenges,Master's Thesis in Artificial Intelligence, Stockholm University, Sweden, Main supervisor: Ali Beikmohammadi (June 2024).
• Nikolaos Karampatzakis, Network routing using hierarchical reinforcement learning,Master's Thesis in Computer and Systems Sciences, Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (June 2024).
• Mahtab Babamohammadi, Accelerating Learning in Double Q-Learning: A Study on Speedy Double Q-learning for Efficient Reinforcement Learning,Master's Thesis in Artificial Intelligence, Stockholm University, Sweden, Main supervisor: Ali Beikmohammadi (September 2024).
• Mehdi Imani, Evaluating Classification and Sampling Methods for Customer Churn Prediction on Highly Imbalanced Data,Master's Thesis in Computer and Systems Sciences, Stockholm University, Sweden, Main supervisor: Ali Beikmohammadi (December 2024).
• Viggo Runsten, Churn Prediction with Customer Segmentation: A Machine Learning Approach for User Retention in Online Gaming,Master's Thesis in Computer and Systems Sciences, Stockholm University, Sweden, Main supervisor: Ali Beikmohammadi (March 2025).
• Lucas Villarroel, Sequential/Cyclic Federated Deep Learning for Precision Agriculture,Master's Thesis in Computer and Systems Sciences, Stockholm University, Sweden, Main supervisor: Ali Beikmohammadi (ongoing).
• Andrea Di Napoli, Prediction of Real Estate Prices Using Data Science - A Comparison of Selected Machine Learning Techniques,Master's Thesis in Computer and Systems Sciences, Stockholm University, Sweden, Main supervisor: Ali Beikmohammadi (ongoing).
• Yulia Ryanova, Deep learning for classification of acute vestibular disorders,Master's Thesis in Computer and Systems Sciences, Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (ongoing).
• Wendy Mcrae, Multi Agent Reinforcement Learning: The Value of Cooperation and Communication,Master's Thesis in Computer and Systems Sciences, Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (ongoing).
• Juan Roijals Miras, fMRI correlates of drowsy brain states with implications to regime modeling, altered brain function and clinical prognosis,Joint Master's Thesis in Health Informatics, Karolinska Institutet & Stockholm University, Sweden, Main supervisor: Sindri Magnússon, Ph.D (ongoing).

• Current Research and Trends in Health Informatics
  • Fall 2024
  • Lecturer, M. Sc. Course, Joint Programme in Health Informatics, Karolinska Institutet & Stockholm University, Sweden.
• Current Research and Trends in Health Informatics
  • Fall 2023
  • Lecturer, M. Sc. Course, Joint Programme in Health Informatics, Karolinska Institutet & Stockholm University, Sweden.
• Current Research and Trends in Health Informatics
  • Fall 2022
  • Lecturer, M. Sc. Course, Joint Programme in Health Informatics, Karolinska Institutet & Stockholm University, Sweden.
• Machine Learning
  • Spring 2020
  • Teaching Assistant, M. Sc. Course, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran, under Sanaz Seyedin , Ph.D.
• Logical Circuits
  • Spring 2020
  • Teaching Assistant, M. Sc. Course, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran, under Professor Karim Faez.
• Digital Signal Processing
  • Fall 2019
  • Teaching Assistant, M. Sc. Course, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran, under Sanaz Seyedin, Ph.D.
• Microprocessors
  • Spring 2017
  • Teaching Assistant, B.Sc. Course, Department of Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran, under Hamidreza Karami, Ph.D.
• Electronics I
  • Spring 2017
  • Teaching Assistant, B.Sc. Course, Department of Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran, under Manouchehr Hosseini, Ph.D.
• Electronics II
  • Fall 2016
  • Teaching Assistant, B.Sc. Course, Department of Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran, under Manouchehr Hosseini, Ph.D.
• Communication Systems I
  • Fall 2016
  • Teaching Assistant, B.Sc. Course, Department of Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran, under Ali Kalantarnia, Ph.D.
• Electrical Circuits II
  • Spring 2016, Fall 2016
  • Teaching Assistant, B.Sc. Course, Department of Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran, under Mohamadmahdi Shahbazi, Ph.D.
• Electromagnetism
  • Spring 2016
  • Teaching Assistant, B.Sc. Course, Department of Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran, under Hamidreza Karami, Ph.D.
• Electrical Circuits I
  • Fall 2015
  • Teaching Assistant, B.Sc. Course, Department of Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran, under Mohamadmahdi Shahbazi, Ph.D.

Collaborations

• Visiting PhD student at Artificial Intelligence and Machine Learning research group, Universitat Pompeu Fabra (UPF), Barcelona, Spain
• Partnerships involving the Reliable Adaptive Predictive Maintenance and Intelligent Decision Support research project with SCANIA CV AB and Linköping University
• Partnerships involving the Smart Converters for Climate-neutral Society: Artificial Intelligence-based Control and Coordination research project with Hitachi Energy and KTH University
• Partnerships involving the Data-driven Control and Coordination of Smart Converters for Sustainable Power System Using Deep Reinforcement Learning research project with KTH University and the University of California

Community Service

Technical Program Committee Membership

• Organizing Team Member of 2025 IEEE World Congress on SERVICES (SERVICES 2025)
• Local Chair of Symposium on Intelligent Data Analysis (IDA 2024)
• Organizing Team Member of 4th Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2018

Journal and Conference Reviewer

• IEEE/ACM Transactions on Networking
• IEEE Communications Letters
• Information Sciences
• Expert Systems with Applicationss
• Neural Networks
• Pattern Analysis and Applications
• Journal of Big Data
• Artificial Intelligence Review
• Mobile Networks and Applications
• Frontiers in Plant Science
• The Imaging Science Journal
• The Journal of Supercomputing
• Engineering Applications of Artificial Intelligence
• BMC Medical Imaging
• Cluster Computing: the Journal of Networks, Software Tools and Applications
• European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
• Complex Systems Informatics and Modeling Quarterly European
• Conference on Artificial Intelligence (ECAI)
• Neural Information Processing Systems (NeurIPS)
• International Conference on Learning Representations (ICLR)
• International Joint Conference on Neural Networks (IJCNN)
• European Control Conference (ECC)

Publications

Journal Publications and Preprints

• Ali Beikmohammadi, Mohammad Hosein Hamian, Neda Khoeyniha, Tony Lindgren, Olof Steinert, Sindri Magnússon. "A Cost-Sensitive Transformer Model for Prognostics Under Highly Imbalanced Industrial Data." Under Review. (link)
• Mohsen Amiri, Ali Beikmohammadi, Yihao Wan, Ali Tayyebi, Qianwen Xu, Sindri Magnússon. "Power Converter Control via Model Predictive Control-Guided Fast Reinforcement Learning." Under Review.
• Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon. "Parallel Momentum Methods Under Biased Gradient Estimations." IEEE Transactions on Control of Network Systems, 2025. (link)
• Ali Beikmohammadi, Sindri Magnússon. "Human-inspired framework to accelerate reinforcement learning." The Journal of Supercomputing, 2025. (link)
• Najmeh Zahabi, Ioannis Petsagkourakis, Nicolas Rolland, Ali Beikmohammadi, Xianjie Liu, Mats Fahlman, Eleni Pavlopoulou, Igor Zozoulenko. "Deciphering conductivity in PEDOT guided by machine learning: From solvent baths to charge paths." Physical Review Materials, 2025. (link)
• Alfreds Lapkovskis, Natalia Nefedova, Ali Beikmohammadi. "Automatic Fused Multimodal Deep Learning for Plant Identification." Frontiers in Plant Science, 2025. (link)
• Mehdi Imani, Majid Joudaki, Ali Beikmohammadi, Hamid Reza Arabnia

  . "

  Customer Churn Prediction: A Systematic Review of Recent Advances, Trends, and Challenges in Machine Learning and Deep Learning." Machine Learning and Knowledge Extraction, 2025. (link)
• Mehdi Imani, Ali Beikmohammadi, Hamid Reza Arabnia

  . "

  Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Under Varying Imbalance Levels." Technologies, 2025. (link)
• Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon. "On the Convergence of Federated Learning Algorithms without Data Similarity." IEEE Transactions on Big Data, 2024. (link)
• Ali Beikmohammadi, Sindri Magnússon. "Accelerating actor-critic-based algorithms via pseudo-labels derived from prior knowledge." Information Sciences, 2024. (link)
• Ali Beikmohammadi, Karim Faez, Ali Motallebi. "SWP-LeafNET: A novel multistage approach for plant leaf identification based on deep CNN." Expert Systems with Applications, 2022. (link)
• Mohammad Hosein Mahmoodian, Hassan Taheri Gazvini, Ali Beikmohammadi. "A Novel Approach to Reducing Energy Consumption, Economic Savings, Service Quality Enhancement, and Resource Utilization in Cloud Data Centers." Transactions on Machine Intelligence, 2020. (link)

Books

• Beikmohammadi, Ali, et al. "Statistical Pattern Recognition." Amirkabir University of Technology Press, (in preparing). [in Persian]
• Beikmohammadi, Ali, et al. "Introduction to Deep Learning." Naghoos Press, 2020 [Translated in Persian from Charniak, E. Introduction to Deep Learning, The MIT Press, 2019].

Peer-Reviewed Conference Publications

• Guilherme Dinis Junior, Ali Beikmohammadi, Sindri Magnússon. "Temporal Difference Learning with Function Approximation for Policy Control Under Delayed and Aggregated Rewards." Under Review.
• Patrick Hammer, Peter Isaev, Andre N. Costa, Ali Beikmohammadi, and Sindri Magnússon. "Rule-Based Grid World Exploration under Uncertainty." Under Review.
• Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon. "Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysis." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD). 2025. (link) [Selected in the top 24% of submissions]
• Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon. "Compressed Federated Reinforcement Learning with a Generative Model." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD). 2024. (link) [Selected in the top 24% of submissions]
• Mehdi Imani, Zahra Ghaderpour, Majid Joudaki, Ali Beikmohammadi. "The Impact of SMOTE and ADASYN on Random Forest and Advanced Gradient Boosting Techniques in Telecom Customer Churn Prediction." 10th International Conference on Web Research (ICWR). IEEE, 2024. (link)
• Ali Beikmohammadi, Sindri Magnússon. "TA-Explore: Teacher-assisted exploration for facilitating fast reinforcement learning." Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (AAMAS). 2023. (link)
• Ali Beikmohammadi, Sindri Magnússon. "Comparing NARS and Reinforcement Learning: An Analysis of ONA and Q-Learning Algorithms." International Conference on Artificial General Intelligence (AGI-23). 2023. (link)
• Ali Beikmohammadi, Najmeh Zahabi. "A Hierarchical Method for Kannada-MNIST Classification Based on Convolutional Neural Networks." 26th International Computer Conference, Computer Society of Iran (CSICC). IEEE, 2021. (link)
• Mohammad Hosein Hamian, Ali Beikmohammadi, Ali Ahmadi, Babak Nasersharif. "Semantic Segmentation of Autonomous Driving Images by the Combination of Deep Learning and Classical Segmentation." 26th International Computer Conference, Computer Society of Iran (CSICC). IEEE, 2021. (link)
• Ali Beikmohammadi, Karim Faez, Mohammad Hosein Mahmoodian, Mohammad Hosein Hamian. "Mixture of Deep-based Representation and Shallow Classifiers to Recognize Human Activities." 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS). IEEE, 2019. (link) [Outstanding Student Paper Award]
• Ali Beikmohammadi, Karim Faez. "Leaf Classification for Plant Recognition with Deep Transfer Learning." 4th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS). IEEE, 2018. (link)
• Mohammad Hosein Mahmoodian, Hassan Taheri Gazvini, Ali Beikmohammadi. "A Novel Solution to Reduce Energy Consumption and Economic Revenue and, Increase Quality of Service and Resource Utilization in Cloud Data Centers." 7th National Congress of New Findings of Iranian Electrical Engineering (IEEEC7). 2020. (link) [in Persian]
• Ali Beikmohammadi, Hamidreza Karami. "A Review of License Plate Segmentation and Recognition Methods." The First International Conference of Electronic and Computer Engineering. 2016. (link) [in Persian]
• Ali Beikmohammadi, Hamidreza Karami. "A Review of License Plate Detection Methods‏." The First International Conference of Electronic and Computer Engineering. 2016. (link) [in Persian]
• Ali Beikmohammadi. "Gate Fringe Capacitance Modeling for FinFETs Considering RSD and Metal Contact in Its Structure, By Using 3-D Model." YREC First National Conference on Electrical Engineering. 2016. (indexed link) [in Persian]

Selected Projects (2017-2021)

• Facial expression classification using facial landmarks extraction [Team Work]
• 3D MRI brain tumor segmentation using deep learning. [Team Work]
• Using DL to improve the surface material classification utilizing EEG signals. [Team Work]
• Human action recognition using transfer learning with deep representations. [course project - Deep Learning]
• A bimodal learning approach to assist multi-sensory effects synchronization. [course project - Neural Networks]
• Learning deep CNN denoiser prior for image restoration. [course project - Statistical Pattern Recognition]
• Speech enhancement with DL using kernel decomposition techniques. [Team Work]
• Improvement of separable non-local means algorithm for image denoising. [course project - Machine Vision]
• Modeling and elucidation of housing price using Time-aware Latent Hierarchical Model. [course project - Machine Learning]
• Implementation of machine learning algorithms (decision tree, Bayes, Naïve Bayes, SVM, …). [course project - Machine Learning]
• Optimal allocation of resources using deep reinforcement learning in cognitive femtocell radio networks. [Team Work]
• Random access techniques for data transmission over packet-switched radio channels (Aloha & CSMA). [course project - Data Communication Networks]
• An enhanced and secured RSA key generation scheme (ESRKGS). [course project - Advanced Communication Networks]
• A fast-iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems. [Team Work]
• Peripheral component interconnects express. [course project - Micro Processors]
• Enter the Pentium CPU protected mode and perform processing operations with FPU and MMX units. [course project - Micro Processors]
• Design of smart sensors to control  the greenhouse environments by ESP8285 (IoT) [Team Work]

Please refer to my complete list of publications under the Research section.