Find Academic Supervisors

• Natural Language Processing
• Machine Learning
• Logical Reasoning
• +7 more

Zhenyun Deng is a Researcher in the Department of Computer Science and Technology at the University of Cambridge, specializing in Natural Language Processing (NLP) and Machine Learning. His work focuses on advancing automated fact-checking systems, logical reasoning in large language models, and graph-based reasoning techniques. Key research themes include document-level claim extraction, logic-driven data augmentation, and multi-hop question answering. His recent contributions span automated verification of textual claims , abstract meaning representation-based data augmentation , and robust node classification on graph data . He collaborates on initiatives like the FEVER workshop and develops datasets such as TaKG for table-to-text generation enhanced with knowledge graphs. Notable trends in his publications include: Integration of logical reasoning into NLP systems Improving model interpretability for multi-step reasoning tasks Handling noisy data in graph neural networks His research bridges foundational machine learning theory with real-world applications in information retrieval and causal inference. No awards or grants are explicitly listed in the provided materials.

Semih YumusakView profile

Research Fellow

• Data Science
• Machine Learning
• Cybersecurity
• +5 more

Dr. Semih Yumusak is a Research Fellow at the University of Southampton, focusing on interdisciplinary research at the intersection of data science, cybersecurity, and network systems. His current work emphasizes data marketplace architecture, privacy-preserving techniques, and applications of machine learning in finance and social analysis. He contributes to projects like the Universal Platform Components for Safe Data Exchange (UPCAST), aiming to enhance interoperability and security in data monetization. His research interests include modular data systems, maritime migration pathways analysis through cellular data, and academic collaboration networks. Recent publications highlight innovations in privacy negotiation frameworks, financial text mining, and network topology optimization. Dr. Yumusak collaborates with international teams on topics ranging from knowledge graph construction to sentiment analysis in product reviews. While no formal awards are listed, his active involvement in funded research projects underscores his academic impact. His work bridges technical challenges in data governance with societal applications, such as leveraging big data for refugee migration safety and improving stock market speculation analysis through social media insights.

• Edge Computing
• 5G/6G Networks
• Multi-access Edge Computing (MEC)
• +3 more

Estefanía Coronado is a researcher at the i2CAT Foundation in Spain, specializing in edge computing, 5G/6G networks, and AI-driven networking. Her work focuses on optimizing resource allocation, energy efficiency, and multi-agent collaboration in edge environments. She has published extensively in top-tier journals and conferences, including IEEE Communications Magazine and NOMS. Key contributions include frameworks like MEO (MEC Orchestrator) and MintEDGE (energy-aware edge simulator). Collaborations with institutions like Universitat Politècnica de Catalunya and industry partners highlight her impact in both academia and industry. Her research addresses challenges in industrial IoT, autonomous systems, and network automation.

Manuel Gil PerezView profile

Associate Professor

• Cybersecurity
• Intrusion Detection Systems
• Trust Management
• +5 more

Manuel Gil Pérez is an Associate Professor in the Department of Information and Communication Engineering at the University of Murcia, Spain. As member of the Faculty of Informatics, he focuses on cybersecurity research areas including intrusion detection systems, trust management, privacy-preserving data sharing, and security operations in dynamic environments. PhD in Computer Science from University of Murcia (2015) Co-supervised by Dr. Gregorio Martínez Pérez and Dr. Antonio Skarmeta Gómez Active researcher with 70+ scientific publications His research spans multiple cutting-edge domains: 5G/6G network security and trust frameworks Decentralized federated learning systems (Fedstellar platform) Knowledge graph applications for cyber threat intelligence Device behavior fingerprinting for IoT security Healthcare data protection through blockchain and SSI Botnet detection and mitigation strategies Recent publications demonstrate trends in: Antifragile cybersecurity architectures Semantic inference for threat profiling Reputation systems in multi-stakeholder environments Edge computing security solutions Integration of AI/ML in network defense As part of the Intelligent Systems and Telematics research group, he collaborates on major EU-funded projects including 5GBarcelona, 5GZORRO, and SoftFIRE. His work addresses critical challenges in network security, trust management, and decentralized learning systems through practical implementations and benchmarking frameworks like LwHBench.

• Autonomous Systems
• Control Systems
• Robotics
• +7 more

Professor Antonios Tsourdos is a leading academic in autonomous systems and control engineering at Cranfield University, where he serves as Professor of Autonomous Systems and Control and Director of the Centre for Autonomous and Cyber-physical Systems. His career spans over two decades with significant contributions to guidance, navigation, and control systems for unmanned vehicles. He has held leadership positions including Head of the Autonomous Systems Group (2007), Head of the Centre of Autonomous and Cyber-Physical Systems (2012), and Director of Research for Aerospace, Transport and Manufacturing (2015). Professor Tsourdos earned his academic qualifications from prestigious UK institutions: MEng in Electronic, Control and Systems Engineering from the University of Sheffield (1995), MSc in Systems Engineering from Cardiff University (1996), and PhD in Nonlinear Robust Flight Control Design and Analysis from Cranfield University (1999). His research focuses on autonomy for single and multiple unmanned vehicles, with particular expertise in guidance and control systems, mission planning, multi-agent coordination, and UAS traffic management. Professor Tsourdos has published extensively in top-tier journals, with over 100 publications in the last five years alone, demonstrating his active research program and leadership in the field. His work bridges theoretical control systems with practical aerospace applications. His research portfolio includes significant projects such as CHEDDAR (Communications Hub for Empowering Distributed ClouD Computing), Air Mobility Ecosystem Consortium, Project BLUEPRINT for UTM-enabled drone operations, and the UKRI Trustworthy Autonomous Systems Node in Security. These projects are funded by UKRI, ESA, Horizon Europe, and major industry partners including BAE Systems, Lockheed Martin, Boeing, Airbus, and Thales. Professor Tsourdos has received notable recognition including Fellowship in the Royal Aeronautical Society, membership in the winning team for the UK MoD Grand Challenge (2008), and the IET Innovation Award (2009). He served as chair of the IFAC Technical Committee on Aerospace Control (2017-2023) and has been involved with the BSI Future Flight Standards Hub and AIAA Advanced Aerial Mobility Certification Task Force. He serves on the editorial boards of numerous prestigious journals including Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering; Aerospace Science and Technology; and IEEE Transactions on Aerospace and Electronic Systems. His work has significant industrial impact through collaborations with major aerospace and defense companies.

Zhengdao WangView profile

Professor

• Machine Learning
• Cybersecurity
• Communications
• +2 more

Zhengdao Wang is a Professor in the Department of Cyber Security Engineering at George Mason University (GMU). Previously, he held a professorship at Iowa State University (2002–2023) and served as a Program Director at the National Science Foundation’s Division of Electrical, Communications, and Cyber Systems (2020–2022). He earned a PhD in Electrical Engineering from the University of Minnesota (2002), an MS from the University of Virginia (1999), and a BEng from the University of Science and Technology of China (1996). His research focuses on machine learning, cybersecurity, communications, signal processing, and information theory. Notable contributions include advancements in reinforcement learning architectures, energy-harvesting systems, and secure communication protocols. His work has been recognized with awards such as the IEEE Signal Processing Society Best Magazine Paper Award (2003), IEEE Marconi Paper Prize (2004), and election as an IEEE Fellow (2016). Key research themes include optimizing wireless networks, designing robust learning algorithms, and exploring theoretical limits of communication systems. Recent publications (2018–2021) highlight innovations in reinforcement learning frameworks, secure key generation, and nonconvex optimization techniques. Professional contributions include editorial roles in top IEEE journals and leadership in international conferences. His interdisciplinary expertise bridges theoretical foundations and practical applications in cyber-physical systems.

• High-Performance Computing
• Energy Efficiency
• Parallel Computing
• +3 more

Ananta Tiwari is a researcher specializing in High-Performance Computing (HPC), energy efficiency, and parallel system optimization. His work focuses on optimizing HPC applications, workload management, and resource allocation strategies to enhance both performance and energy efficiency. Tiwari has collaborated extensively with institutions like the University of Maryland, UC San Diego, and Lawrence Livermore National Laboratory through his research activities. Education: PhD in Computer Science, University of Maryland, College Park (2011) Research Interests: Energy-efficient HPC systems Parallel application auto-tuning frameworks Workload characterization and extrapolation Node-sharing and resource pricing models ARM architecture optimization for HPC Key Contributions: Tiwari's research spans energy optimization techniques for large-scale MPI applications, colocation strategies for HPC workloads, and binary instrumentation tools for program analysis. His work on auto-tuning frameworks and multi-objective modeling with machine learning addresses critical challenges in balancing performance, power consumption, and scalability in modern HPC environments.

• dynamics of neuronal networks
• mechanisms of neuronal information processing
• physics of machine learning
• +1 more

Prof. Moritz Helias is a Professor and Group Leader of the Theory of Multi-Scale Neuronal Networks at the Institute for Advanced Simulation (IAS-6), Computational and Systems Neuroscience department at Forschungszentrum Jülich. His research focuses on the dynamics of neuronal networks, mechanisms of neuronal information processing, and the physics of machine learning, with a particular emphasis on applying statistical physics methods to these areas. His work bridges theoretical neuroscience and machine learning, exploring how principles from statistical physics can elucidate neural computation and improve neural network models. He investigates transient recurrent dynamics, feature learning in deep networks, and renormalization group techniques to link network and neuron-level correlations. Key contributions include studies on criticality in neural networks, the role of synaptic heterogeneity, and the application of field-theoretic methods to analyze network dynamics and learning processes. His research also addresses challenges in computational neuroscience, such as modeling high-frequency neural activity and optimizing reservoir computing architectures.

Simon MusallView profile

Assistant Professor

• Neural Network Function
• Decision-making
• Multisensory Processing
• +3 more

Prof. Simon Musall is an Assistant Professor of Neuromodulation at RWTH Aachen University and Head of the in-vivo Neurophysiology Lab at Forschungszentrum Jülich. His research focuses on understanding how multisensory information is integrated in neural networks to guide behavior, particularly in mice performing cognitive tasks. He employs advanced techniques like high-density electrophysiology and functional imaging to study cortical networks, pathway-specific information transfer, and neuromodulation effects. Key research interests include neural network function, decision-making mechanisms, and neuromodulation's role in information processing. His lab develops cutting-edge neurotechnology, such as flexible neural probes and organic neurohybrid systems, to advance in vivo recordings and closed-loop control. Collaborative projects integrate computational neuroscience with experimental approaches to dissect cortical circuit dynamics and their behavioral correlates. Recent work highlights contributions to spike sorting algorithms (UnitRefine), network dynamics modeling (Riemannian multi-scale decomposition), and enzyme-mediated neurohybrid interfaces. His interdisciplinary approach bridges basic neuroscience with translational tools for studying neurodevelopmental and psychiatric conditions.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Qian WANG is a Lecturer at the Faculty of Law of Université de Montréal, contributing to both teaching and interdisciplinary research. Her work bridges legal education with advanced computational methods, though her recent publications primarily focus on artificial intelligence and computer science. Research interests span a broad spectrum including artificial intelligence, machine learning, natural language processing, and computer vision. Her work often intersects with systems-level challenges such as operating system control and cognitive modeling, demonstrating a unique interdisciplinary approach. Publications reflect a trajectory from foundational contributions in graph theory and saliency detection (2020-2021) to cutting-edge advancements in large language models and reasoning frameworks (2023-2025). Key themes include improving AI capabilities through dynamic reasoning mechanisms and exploring the ethical dimensions of autonomous systems. No academic awards or grants are explicitly noted in the provided materials, though her prolific publication record indicates active research engagement. She currently holds no listed advisees or doctoral supervision roles. Labs or collaborative teams associated with her research are not detailed in the available information.

Chenhui ShaoView profile

Associate Professor

• Smart Manufacturing
• Machine Learning
• Big Data Analytics in Manufacturing
• +8 more

Chenhui Shao is an Associate Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign (UIUC), part of the Grainger College of Engineering. He holds a Ph.D. in Mechanical Engineering from the University of Michigan (2016), with prior degrees in Statistics, Industrial and Operations Engineering, and Automation. His academic roles include serving as an Associate Editor for multiple journals and organizing conferences such as the ASME International Manufacturing Science and Engineering Conference. Education: Ph.D., Mechanical Engineering, University of Michigan, 2016 (Advisors: S. Jack Hu and Jionghua Jin) M.A., Statistics, University of Michigan, 2013 M.S.E., Industrial and Operations Engineering, University of Michigan, 2013 B.E., Automation, University of Science and Technology of China, 2009 Research Interests: Smart Manufacturing Machine Learning in Manufacturing Big Data Analytics and Materials Joining Manufacturing Systems Control and Automation Applications in Dynamics, Energy, Environment, and Healthcare Recent Research Trends: His work focuses on federated learning for distributed manufacturing, physics-informed machine learning for process optimization, and sensor fusion for quality prediction. Key areas include ultrasonic metal welding, additive manufacturing, and two-photon lithography. Awards: 2022 Dean’s Award for Excellence in Research 2021 ASME Chao and Trigger Young Manufacturing Engineer Award 2020 NSF CAREER Award Multiple teaching excellence recognitions from UIUC students Advising & Grants: Advised over 10 Ph.D. and Master’s students, with notable projects funded by NSF, NCSA, and industry partnerships like CRRC Corporation. Collaborates on interdisciplinary initiatives such as the Center for Advanced Research in Drying (CARD) and the NSF nanoMFG Node. Labs & Teams: Leads the Automation and Digital Manufacturing Lab (ADML), focusing on machine learning, control systems, and sustainable manufacturing. Active in national and international conferences, organizing sessions on topics like additive manufacturing and smart manufacturing networks.

• Civil Engineering
• Materials Science
• Construction Technology
• +5 more

Dr. Ciaran McNally is an Associate Professor in the School of Civil Engineering at University College Dublin (UCD). He holds a PhD in Civil Engineering from UCD (2001) and a Professional Diploma in Teaching & Learning (2015). His research focuses on advanced materials, including concrete durability, asphalt technology, fiber-reinforced polymers (FRP), and digital design methods like 3D printing and BIM. He has secured over €2 million in research funding, leading EU projects such as the Marie Curie Initial Training Network TEAM (€3.2M). Education: BE, PhD (Civil Engineering, UCD); Prof Dip University Teaching & Learning (UCD) Affiliations: Irish Concrete Society (Treasurer), Engineering Graduates Association Committees: College Executive Board, Safety Committee (School/College), UCD Covid-19 Consultative Committee His research interests span low-carbon concrete, eco-engineering for marine structures, and infrastructure asset management. Notable contributions include studies on reclaimed asphalt in pavements, bio-inspired optimization, and durability of GGBS blends. He coordinates modules like Advanced Materials and Group Design Projects, emphasizing practical engineering and sustainability. Recent articles highlight innovations in 3D-printed concrete, low-carbon materials, and steel stud behavior. McNally’s work bridges academic research with industry applications, promoting sustainable construction practices and lifecycle thinking.

• Plastic Surgery
• Reconstructive Surgery
• Microsurgery
• +3 more

Dr. David A Daar is an Assistant Professor of Clinical Surgery at the University of Southern California (USC), specializing in reconstructive plastic surgery and microsurgery. He serves as co-director of the USC Lymphedema Center, focusing on advanced microsurgical techniques like lymphovenous bypass and lymph node transfer. Dr. Daar completed his undergraduate studies at USC, earned his medical degree at UC Irvine (with emphasis on Latino healthcare disparities), and underwent plastic surgery residency at NYU Langone Health, followed by a microsurgery fellowship at USC. His clinical expertise includes breast reconstruction (implant-based and autologous techniques like DIEP flap), lower extremity reconstruction, head and neck reconstruction, and lymphedema management. His research interests span microsurgical outcomes, flap survival, and leveraging technology such as 3D printing and ultrasound imaging. He has published over 60 peer-reviewed articles, contributing to topics like postoperative complication prediction, surgical technique optimization, and disparities in reconstructive care. Dr. Daar emphasizes patient-centered care, believing that listening to patients’ goals enhances surgical success. His work bridges clinical practice with innovation, aiming to advance reconstructive surgery while addressing systemic healthcare challenges. Education: Bachelor’s degree: University of Southern California (USC) Medical Degree: UC Irvine School of Medicine (PRIME-LC Program) Training: Plastic Surgery Residency: NYU Langone Health’s Hansjörg Wyss Department of Plastic Surgery Microsurgery Fellowship: USC Labs/Teams: Co-director of the USC Lymphedema Center, part of Keck Medicine of USC.

Liang-Liang XieView profile

Professor

• Network Information Theory
• Wireless Relay Networks
• Adaptive Control
• +6 more

Liang-Liang Xie is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research focuses on network information theory, wireless relay networks, adaptive control systems, and systems biology. He holds a B.S. in Mathematics from Shandong University (1995) and a Ph.D. in Control Theory from the Chinese Academy of Sciences (1999). He completed postdoctoral research at Linköping University (Sweden) and the University of Illinois at Urbana-Champaign (USA). His work spans theoretical advancements in wireless communication protocols, including relay frameworks, compression techniques, and network coding. Recent articles emphasize omnidirectional relaying, throughput optimization in ad hoc networks, and information-theoretic scaling laws. He has advised over 25 graduate students, many co-supervised with senior faculty members. Current students include Shuyue Wang and Yuxi Zhang. Teaching responsibilities include courses in signals and systems, probability theory, and network information theory. His research has contributed to foundational understanding of transport capacity, channel coding, and multi-user wireless systems.

• Pattern Recognition
• Applied Machine Learning
• Document Analysis
• +4 more

Andreas Fischer is an Ordentlicher Professor at the Fribourg School of Engineering and Architecture (HES-SO), specializing in Pattern Recognition, Machine Learning, and Document Analysis. His research focuses on handwriting recognition, graph-based methods, and applications in cultural heritage preservation and medical imaging. Education: BSc in Computer Science from Fribourg School of Engineering and Architecture Research Interests: Graph Neural Networks for automata universality analysis Hybrid systems for Vietnamese stele keyword spotting Medical image analysis (colorectal cancer, tumor budding) Large language models for post-OCR correction Key Projects: TAINA Technology (handwriting validation for tax forms) Swisscom (Swiss German to High German translation) Hasler Foundation (Vietnamese stele graph-based analysis) Publications: Over 30 peer-reviewed articles in top journals/conferences (IEEE Access, Medical Image Analysis, Pattern Recognition, etc.), with focus on graph-based methods, handwriting recognition, and medical applications. Grants & Roles: Principal Applicant for multiple industry-funded projects (TAINA, Swisscom) Co-developer of DIVA-DAF deep learning framework