Find Academic Supervisors

• Neural Computation
• Cognitive Neuroscience
• Consciousness Studies
• +4 more

Prof. Lucia Melloni is a Research Professor at New York University (NYU) School of Medicine's Department of Neurology and a W2 Group Leader (Tenure) at the Max Planck Institute for Empirical Aesthetics, leading the Research Group Neural Circuits Consciousness and Cognition. She holds a PhD in Psychology from the Catholic University of Chile and the Max Planck Institute for Brain Research. Her work focuses on neural mechanisms underlying consciousness, predictive processing, and brain plasticity. She has held positions at Columbia University, the Max Planck Institute for Brain Research, and Goethe University. Key achievements include developing methods for analyzing neural correlates of consciousness and advancing theories on predictive coding in sensory and cognitive systems. Education : PhD, Psychology (2006); BA in Psychology (2000) and Social Sciences (1995) from Catholic University of Chile. Affiliations : NYU School of Medicine (since 2020), Max Planck Institute for Empirical Aesthetics (since 2017). Research Interests : Domain-general cortical computations, neural mechanisms of perception and language comprehension, consciousness theories, and neuroplasticity. Her lab investigates how the brain uses predictions to control sensory processing and cognition, with implications for understanding disorders like minimally conscious states. Recent Work : Recent articles explore perceptual biases, auditory predictive processing, and adversarial testing of consciousness theories. Her work bridges clinical neurology and cognitive neuroscience, emphasizing methodological rigor and interdisciplinary collaboration. Awards : Israel Institute for Advanced Studies Fellow (2019), AcademiaNet member (2018). Grants : Over €10M secured from Templeton World Charity Foundation, NIH, and ONR for projects on consciousness, neural correlates, and cognitive neuroscience. Labs/Teams : Leads the Neural Circuits, Consciousness, and Cognition group at Max Planck, collaborating with institutions globally on projects like the Consciousness Theories Studies (ConTraSt) database.

Markus DiesmannView profile

Professor

• Computational Neuroscience
• Neuronal Network Modeling
• Supercomputing
• +3 more

Prof. Markus Diesmann is a Director of the Institute for Advanced Simulation (IAS-6) and the Institute for Neuroscience and Medicine (INM-10) at Forschungszentrum Jülich. He leads the Computational Neurophysics group, focusing on understanding neuronal network dynamics through computational models and supercomputing. His work integrates theoretical neuroscience with high-performance computing to simulate brain-scale networks and explore neuromorphic computing foundations. Research interests include correlation structures in neuronal networks, simulation technologies, brain-scale modeling, and software engineering for neuroscience. He has pioneered efforts in scaling neuronal network simulations to exascale systems and improving computational efficiency. Key contributions include the NEST simulation software and studies on synaptic plasticity, sequence learning, and LFP dynamics. Publications emphasize advancements in simulation algorithms, network connectivity analysis, and bridging experimental data with computational models. His work addresses challenges in real-time cortical microcircuit simulation, neuromorphic hardware integration, and the role of noise in neural computation. Prof. Diesmann collaborates internationally on neuroscience initiatives and high-performance computing applications. He advocates for robust scientific software practices and contributes to infrastructure for open science in computational neuroscience.

Corinna CoupetteView profile

Assistant Professor

• Legal Network Science
• Computational Legal Theory
• Graph Theory
• +5 more

Corinna Coupette is an Assistant Professor of Computer Science at Aalto University's School of Science, leading the Telos Lab. She holds dual PhDs in Law and Computer Science from the Max Planck Institutes. Her research focuses on computational legal theory, integrating code, data, and law to model complex systems like information societies. She serves as Program Director for the interdisciplinary Master’s Program in Information Networks and is a Research Affiliate at the Max Planck Institute for Tax Law and Public Finance. Education: Dr. iur. (Law, 2018, summa cum laude) and Dr. rer. nat. (Computer Science, 2023, summa cum laude). Awards include the Otto Hahn Medal and Caroline von Humboldt Prize. Her work spans legal network science, graph theory applications, and ethical AI regulation. Active in teaching, she leads courses like 'Algorithms and Society' and 'Information Visualization', and collaborates internationally through ELLIS and the Max Planck Society.

• Neuroscience
• Neural Coding
• Information Theory
• +3 more

Stefano Panzeri is a Professor at the University of Hamburg's Medical Faculty, affiliated with the Department of Neuroscience. His research focuses on neural coding, information theory, and computational neuroscience, particularly in sensory systems and decision-making processes. He contributes to the Zentrum für Molekulare Neurobiologie Hamburg (ZMNH), advancing understanding of neural network dynamics and their implications for cognition. Key areas: Neural information processing, sensory systems, decision-making. Publications span topics like cortical coding, neuroimaging, and computational models. His work integrates experimental and theoretical approaches, with recent emphasis on decoding neural signals and their behavioral relevance. Collaborations include developing tools like MINT for neural data analysis.

• Industrial Automation Systems
• Distributed Control Systems
• IEC 61499 Standard
• +6 more

Valeriy Vyatkin is a Professor at Aalto University's Department of Electrical Engineering and Automation within the School of Engineering. He also maintains an affiliation with Luleå University of Technology in Sweden. His research spans industrial automation, distributed control systems, and digital transformation of manufacturing processes. Vyatkin leads a significant research group focusing on next-generation industrial control architectures and has established himself as a leading authority in the IEC 61499 standard for distributed industrial automation systems. Professor Vyatkin's research interests center around industrial automation systems with particular emphasis on distributed control architectures, formal methods for verification of industrial control systems, and digital twin technologies. His work bridges theoretical computer science with practical industrial applications, developing methods for model checking, virtual commissioning, and formal verification of industrial automation systems. He has pioneered approaches for applying reinforcement learning to industrial process control, particularly in steel manufacturing and energy systems. His recent work explores the integration of generative AI with industrial control systems, focusing on rapid prototyping and code generation for IEC 61499 applications. The analysis of Professor Vyatkin's recent publications (2023-2025) reveals a strong research trajectory focused on advancing industrial automation through multiple complementary approaches. His work demonstrates a consistent emphasis on the IEC 61499 standard as a foundation for distributed control systems, with growing integration of AI techniques, particularly generative models and reinforcement learning. A notable trend is the progression from theoretical foundations toward practical implementation frameworks, with increasing attention to security, privacy, and human factors in industrial settings. His research group has expanded into new application domains including energy systems, horticulture, and nuclear instrumentation while maintaining core expertise in manufacturing automation. Professor Vyatkin has made significant contributions to the field through his editorial work, including serving as guest editor for special issues in IEEE Transactions on Industrial Informatics. His research has been supported by multiple grants from national and international funding agencies focusing on industrial digitalization and smart manufacturing initiatives. Professor Vyatkin actively supervises numerous PhD and Master's students, with a consistent research group of 8-10 students and postdoctoral researchers. His advising approach emphasizes both theoretical rigor and industrial relevance, with many students collaborating directly with industry partners. His research group has established collaborations with major industrial players in manufacturing, energy, and automation sectors across Europe. The research activities are centered around the Industrial Automation research group at Aalto University, which maintains strong connections with the international IEC 61499 community. The team operates a dedicated laboratory for industrial automation research with capabilities for virtual commissioning, digital twin development, and formal verification of control systems. The group participates in multiple European research projects focused on Industry 4.0 and 5.0 technologies, with particular emphasis on human-centric automation and secure industrial systems.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +5 more

Hui Fang is a researcher at Loughborough University , Department of Computer Science, UK. Their work spans Artificial Intelligence , Machine Learning , and Remote Sensing , with recent focus on Medical Imaging , Natural Language Processing , and Urban Mobility . Publications highlight innovations in MRI Reconstruction , Legal Judgment Prediction , and Video SAR Analysis . Research Interests include Graph Neural Networks , Transformer Models , and Data Mining applied to domains like Agricultural Technology , Healthcare Systems , and Smart Cities . Methodologies emphasize Interpretable AI , Federated Learning , and Multimodal Integration . Key 2024-2025 Trends involve Transformer-Based Architectures for Medical Imaging , Information Bottleneck in Spatio-Temporal Networks , and Bootstrapping Techniques for Information Extraction . Collaborative projects with Dongdong Weng , Zhu Xiao , and Yong He demonstrate interdisciplinary applications in Transportation , Healthcare , and Agriculture . Recent Co-authored Publications span IEEE Transactions , Neurocomputing , and Remote Sensing Journals , addressing Dynamic Phasor Modeling , 4D Facial Capture , and Virtual Reality Usability .

• Wireless Communications
• Deep Learning
• Signal Processing
• +7 more

Thien Huynh-The is a Professor in the Department of Electrical Engineering at Chungnam National University's College of Engineering, where they lead research in wireless communications, deep learning applications, and IoT systems. With over 150 publications spanning from 2014 to 2025, their work demonstrates sustained academic productivity with significant contributions to 5G/6G networks, spectrum sensing, and federated learning architectures. Research interests focus on the intersection of deep learning and wireless communications, particularly in automatic modulation classification, semantic segmentation for remote sensing, and energy-efficient communication protocols. Their innovative approaches include developing specialized CNN architectures like SRNet for spectrum sensing and CosPoint Transformer for 3D semantic segmentation, addressing critical challenges in 5G/6G systems and metaverse infrastructure. Recent work explores STAR-RIS-aided networks, waveform classification for integrated radar-communication systems, and privacy-preserving federated learning for healthcare applications. Analysis of publication trends reveals increasing focus on metaverse technologies and 6G communications since 2022, with significant contributions to IEEE Communications Surveys & Tutorials and IEEE Internet of Things Journal. Key research areas include federated learning optimization, channel estimation techniques using attention networks, and semantic communication frameworks for edge-assisted metaverse applications. Collaborative research spans multiple institutions with frequent co-authorship with Dong-Seong Kim, Quoc-Viet Pham, and Won-Joo Hwang. Current projects address critical challenges in wireless power transfer, spectrum efficiency, and computational resource allocation in next-generation networks.

• Wireless Communications
• MIMO Systems
• Signal Processing
• +5 more

Jihoon Choi is a Professor in the Department of Electrical Engineering at Korea University's College of Engineering. With an extensive publication record spanning over two decades from 2000 to 2025, he has established himself as a leading researcher in wireless communications and signal processing. His work primarily focuses on MIMO systems, space-time coding techniques, and channel estimation methodologies. Choi's research interests center around advanced wireless communication techniques, particularly in next-generation wireless systems. His work spans multiple specialized areas including Space-Time Line Coding, mmWave communications, physical layer security, and intelligent reflecting surface technologies. He has made significant contributions to the development of novel precoding and combining strategies for multiuser MIMO systems, with special emphasis on rate balancing and interference management techniques. Analysis of his recent publications (2021-2025) reveals a strong research trajectory focused on space-time line coding applications across diverse scenarios including UAV systems, SAR imaging, and physical layer security. His work demonstrates a consistent pattern of innovation in wireless communications, with increasing emphasis on practical implementations for 5G/6G systems. The publications show a clear evolution from fundamental signal processing techniques to more complex system-level implementations addressing real-world challenges in wireless communications. Throughout his career, Professor Choi has maintained a highly productive research output with over 70 publications in prestigious IEEE journals and conferences. His work demonstrates strong collaboration patterns, particularly with researchers like Jingon Joung, Wonjun Lee, and Yong Hoon Lee, suggesting active participation in research groups or laboratories focused on wireless communications. His recent publications indicate ongoing research activity with multiple 2025 publications currently in process.

• Wireless Communication
• Machine Learning
• Signal Processing
• +3 more

Michel Kulhandjian is a researcher specializing in Wireless Communication and Machine Learning applications. His work spans NOMA Systems , RF Fingerprinting , and Drone-Assisted Sensing across academic institutions. Key collaborations with Carleton University , Carleton University , and University of Ottawa researchers Active in 5G/6G technologies and IoT Security since 2018 His recent articles focus on: 2024 : Pedestrian detection, drone-based tree health monitoring, and industrial IoT security 2025 : AI-powered agricultural robotics Scientific contributions include: Code design for OTFS-NOMA systems Low-complexity detection algorithms 3D CNN frameworks for signal analysis RF Fingerprinting under impaired channels

• Healthcare Applications
• ECG Signal Processing
• Sleep Apnea Detection
• +5 more

Nuno Gonçalo Coelho Costa Pombo is an active researcher and faculty member at the University of Beira Interior, Portugal, where he works in the School of Technology and Management within the Department of Computer Science. His academic career spans over a decade with consistent publication output from 2014 through 2025, demonstrating sustained research productivity and evolving expertise across multiple domains. Dr. Pombo's research interests center on the intersection of computer science and healthcare applications. His work prominently features ECG signal processing for medical diagnostics, with significant contributions to sleep apnea detection systems and clinical decision support systems . He has pioneered approaches in activities of daily living recognition using sensor data from mobile devices, which has important applications in elderly care and chronic disease management. His research methodology often combines machine learning techniques with Internet of Things (IoT) frameworks to create healthcare monitoring solutions that balance effectiveness with user privacy concerns. Recent work has expanded into educational technology applications, including augmented reality for programming instruction and futuristic thinking development in education. An analysis of Dr. Pombo's publication trends reveals a natural progression from foundational research in activity recognition to increasingly sophisticated healthcare applications. His early work focused on basic activity recognition frameworks, while more recent publications demonstrate advanced applications in specific medical conditions including diabetes management, mental health support, and cardiovascular diagnostics. The interdisciplinary nature of his research is evident in his extensive collaborations across engineering, medical, and social science domains, reflecting a holistic approach to healthcare technology development. Dr. Pombo maintains an active supervision role, with numerous publications featuring co-authors who appear to be students or early-career researchers. His work has been supported by research initiatives focused on healthcare technology innovation, though specific grant details are not evident from publication records. The consistent output across reputable venues including IEEE Access, Sensors, and international conferences demonstrates sustained research impact in his field.

• Internet of Things
• Edge Computing
• Smart Agriculture
• +5 more

Professor Yi-Bing Lin is a distinguished faculty member in the Department of Computer Science at National Yang Ming Chiao Tung University, Taiwan, where he leads pioneering research in Internet of Things (IoT) systems and applications. His work primarily focuses on developing the IoTtalk platform and its numerous derivatives across various domains including smart agriculture, smart homes, environmental monitoring, and creative applications. His research interests span Internet of Things, Edge Computing, Smart Agriculture, Sensor Networks, AI Integration, Wireless Networking, and Smart Home Systems. Professor Lin has developed the IoTtalk framework that enables rapid development of IoT applications with numerous specialized implementations including VoiceTalk, SensorTalk, AgriTalk, and many others that address specific domain challenges. His work emphasizes practical implementations with real-world impact, particularly in precision agriculture where his team has developed systems for orchid disease detection, rice blast monitoring, turmeric farming, and watermelon ripeness prediction. Analysis of his recent publications (2023-2025) reveals a strong trend toward integrating AI with IoT systems, particularly for agricultural applications and smart environments. His work increasingly incorporates advanced techniques like continuous wavelet transform, deep learning, and computer vision to solve practical problems in precision farming and environmental monitoring. The publications also show growing interest in creative applications of IoT technology for performing arts, interactive experiences, and educational contexts. Professor Lin has received recognition through consistent high-volume publication output in top-tier venues including IEEE Internet of Things Journal, IEEE Access, and Sensors. His collaborative network is extensive, with frequent co-authorship with researchers like Yun-Wei Lin, Wen-Liang Chen, and Min-Zheng Shieh. His advising has produced numerous researchers who continue to work in IoT and related fields, with many former students maintaining collaborative relationships. Professor Lin's research has been supported by multiple grants enabling the development of practical IoT systems with real-world implementations. His lab has developed numerous specialized IoT applications through the IoTtalk framework, creating a cohesive research ecosystem. Current work shows expansion into new application domains including interactive miniature worlds, simultaneous performance across locations using IoT-based motion capture, and IoT-based musical instruments like piano playing robots and violin robots, demonstrating the versatility of his research approach.

• Cloud Computing
• Blockchain Technology
• Data Security
• +5 more

Xiaojun Zhang is a Professor actively contributing to cloud computing, blockchain technology, data security, and educational technology. His work spans cybersecurity, signal processing, and wireless systems. Key Research Areas: Privacy-preserving data aggregation, machine learning for biomedical imaging, blockchain-based integrity auditing, and educational metacognition studies. Recent Article Trends (2022–2025): Focus on secure federated learning, data denoising algorithms, and blockchain applications in smart grids, healthcare, and education. Collaborations include institutions in China and international researchers.

• Machine Learning
• Neural Networks
• Computer Vision
• +4 more

Xiao Wu is a researcher affiliated with multiple academic institutions, including Southwest Jiaotong University (School of Information Science and Technology), University of Michigan (Department of Electrical Engineering and Computer Science), and Chinese Academy of Sciences (Institute of Computing Technology). His work spans diverse areas such as machine learning, computer vision, remote sensing, and medical image analysis. Research interests include transformer architectures , deep learning for time series and image processing , multi-agent control systems , and industrial optimization . Recent publications focus on novel neural network designs for applications in drilling process optimization, brain-computer interfaces, and multispectral/hyperspectral image fusion. His work integrates advanced algorithms like tensor decomposition , multiscale temporal convolution , and adaptive recalibration modules to address challenges in engineering and biomedical domains. Collaborations involve institutions like Harvard University, Jiangnan University, and Hong Kong Polytechnic University.

• Biomedical Informatics
• Machine Learning
• Graph Neural Networks
• +4 more

Yi-Jia Zhang is a Professor at the School of Computer Science and Technology, Dalian University of Technology. They hold affiliations with multiple institutions, including Zhejiang Sci-Tech University and Jilin University. Their research focuses on biomedical informatics, machine learning, and healthcare applications, with significant contributions to medical knowledge fusion, drug recommendation systems, and multimodal analysis. Over 140 publications since 2011 reflect expertise in areas like graph neural networks, natural language processing, and medical image analysis. Key collaborations include work with Hongfei Lin, Mingyu Lu, and Jian Wang on projects such as drug-repositioning models and radiology report generation frameworks. A notable emphasis is placed on applying AI techniques to solve biomedical challenges, including ICD code classification and sentiment analysis in healthcare contexts. No formal awards are listed, but their extensive publication record underscores academic impact.

• Machine Learning
• Artificial Intelligence
• Data Mining
• +4 more

Feng Jiang is a Professor at Nanjing University's School of Computer Science and Technology, Department of Computer Science and Technology. With an extensive publication record spanning from 1999 to 2025, Professor Jiang has established himself as a prominent researcher in multiple computer science domains. His research interests include Machine Learning, Artificial Intelligence, Data Mining, Software Engineering, and Computer Vision. Professor Jiang's work demonstrates significant interdisciplinary applications, ranging from healthcare and bioinformatics to industrial manufacturing and agricultural technology. His research approach often combines theoretical advancements with practical implementations, addressing real-world challenges across diverse sectors. Analysis of his recent publications reveals a strong focus on deep learning applications, graph neural networks, and ensemble methods. His work shows consistent contributions to both theoretical frameworks and practical implementations, with particular emphasis on software engineering applications, computer vision systems, and biomedical informatics. The breadth of his research indicates active collaborations across multiple institutions and disciplines. Professor Jiang has supervised numerous research projects resulting in publications across top-tier journals including IEEE Access, Expert Systems with Applications, and Knowledge-Based Systems. His work has been consistently published in high-impact venues, demonstrating sustained research productivity and relevance in the field.