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Mohsen KaboliView profile

Assistant Professor

• Embodied Robotics
• Tactile Intelligence
• Machine Learning
• +3 more

Mohsen Kaboli is an Assistant Professor (Part-time) at Eindhoven University of Technology (TU/e) in the Netherlands, affiliated with the Department of Electrical Engineering and the Electronic Systems group. Since 2018, he has served as head of the AI, Robotics & Cognitive Vehicle research lab at BMW Group’s Center of Invention in Munich, Germany. Previously, he held positions at Radboud University’s Institute for Brain and Cognition (2019-2022) and Technical University of Munich (TUM) as a postdoctoral research fellow. His research focuses on Embodied Robotics, Visuo-Tactile Interactive Perception, and Machine Learning, with applications in Robotic Grasping, Human-Robot Collaboration, and Neuromorphic Computing. He leads European-funded projects like PHASTRAC (Oscillatory Neural Networks for AI Edge Computing) and INTUITIVE (Tactile User Interfaces). Keywords: Tactile Intelligence, AI, Robotics Application Domains: Mobile Robotics, Medical Instrumentation, Neuroprosthetics His recent publications (e.g., ViTract, Shared Visuo-Tactile Perception) emphasize robust object pose estimation and shape reconstruction using Bayesian filtering and Graph Neural Networks. He has authored ~40 academic works and contributed to 20 patents. Scientific Awards : IEEE Senior Member (2018) Georges Giralt Ph.D. Award (Finalist) IEEE ICRA Outstanding Paper Award (Finalist, 2023) IEEE FLEPS Outstanding Student Award (Winner, 2022) IEEE ROSE Outstanding Paper Award (Winner, 2024) Mohsen Kaboli serves as Editor/Associate Editor for IEEE ICRA, IROS, R-AL, T-RO, and IJRR. His work bridges tactile perception, AI, and robotics, with impacts in wearables, medical devices, and autonomous systems.

• Reinforcement learning
• Motivational biases
• Decision-making processes
• +5 more

Dr. Mojtaba Rostami Kandroodi is a University Lecturer at Tilburg University's Tilburg School of Humanities and Digital Sciences (TSHD) within the Department of Intelligent Systems. His academic profile demonstrates active engagement in both teaching and research, with courses including Machine Learning, Mathematics for Premasters DSS, and supervision of Master's theses through the Data Science in Action program. Dr. Rostami Kandroodi's research centers on the intersection of cognitive neuroscience, computational modeling, and psychiatric disorders. His work investigates how motivational biases influence decision-making processes, with particular focus on reinforcement learning mechanisms in both healthy individuals and those with psychiatric conditions. He examines how neurochemical factors like dopamine and substances such as LSD modulate learning processes, seeking to understand the neurocognitive basis of conditions including depression, anxiety, and addiction. His research employs sophisticated computational models to analyze behavioral data from probabilistic reversal learning tasks, revealing nuanced insights into reward-punishment asymmetries and cognitive control mechanisms. Analysis of his publication record shows a consistent focus on reinforcement learning dynamics across multiple contexts. His work spans from theoretical computational models of asymmetric updating in volatile environments to clinical investigations of learning biases in psychiatric populations and pharmacological studies examining how substances alter learning processes. This research trajectory demonstrates both methodological sophistication and clinical relevance, bridging computational neuroscience with practical applications for understanding and potentially treating psychiatric disorders. Dr. Rostami Kandroodi maintains active collaborations with researchers across institutions, as evidenced by his co-authorship with prominent figures like Hanneke den Ouden. His teaching responsibilities in Machine Learning and data science methodologies complement his research focus, creating a cohesive academic profile centered on quantitative approaches to understanding cognition and behavior.

Noura Al MoubayedView profile

Associate Professor

• Explainable Machine Learning
• Natural Language Processing
• Optimisation
• +6 more

Dr. Noura Al Moubayed serves as an Associate Professor in the Department of Computer Science at Durham University, specializing in Explainable Machine Learning, Natural Language Processing, and Optimisation for high-dimensional data challenges. Education PhD from Robert Gordon University Post-doctoral positions at University of Glasgow and Durham University Research Focus Her work develops machine learning solutions for healthcare, social signal processing, cyber-security, and Brain-Computer Interfaces, specifically targeting noisy and imbalanced datasets through advanced NLP and optimization techniques. Current projects integrate deep learning with explainability frameworks to enhance real-world applicability. Publication Trends Recent publications (2020-2022) demonstrate consistent focus on improving question answering systems via bilinear pooling and paraphrasing techniques, alongside novel deep learning approaches for probabilistic topic modeling. These works bridge computer vision, NLP, and multimodal learning to address data imbalance challenges across healthcare and social computing domains. Academic Service PeerJ Computer Science Editorial Board Member 335 authorship points and 30 reviewer points on PeerJ platform