Find Academic Supervisors

• Speech Processing
• Audio Segmentation
• Speaker Verification
• +4 more

Alfonso Ortega Giménez is a Professor affiliated with the University of Zaragoza, Spain. His primary research focuses on speech and audio processing, including speaker verification, audio segmentation, and machine learning methodologies. He collaborates extensively with institutions like the Autonomous University of Madrid and the University of Arizona, contributing to projects such as the ViVoLab system for diarization and emotion recognition challenges. Key research interests include disentanglement learning, explainable AI, and robust speaker verification under varying conditions. He has authored over 130 publications in venues like ICASSP, INTERSPEECH, and IEEE journals, emphasizing advancements in deep learning for audio applications. His work often addresses challenges like domain adaptation, unsupervised learning, and anomaly detection in audio signals. Ortega’s contributions span system design for NIST challenges, emotion recognition, and broadcast domain analysis. Collaborations with co-authors such as Antonio Miguel and Eduardo Lleida highlight his role in multidisciplinary projects. His research bridges theoretical machine learning with practical audio applications, addressing real-world problems in speech enhancement and robustness.

• Cybersecurity
• Machine Learning
• Biometric Authentication
• +4 more

Chan Yeob Yeun is a Professor at United Arab Emirates University in the College of Information Technology, with prior affiliation at Qatar University's Department of Computer Science. His research focuses on cybersecurity, machine learning, and biometric authentication systems, particularly in industrial IoT and digital twin environments. Current Affiliation: United Arab Emirates University, College of Information Technology Prior Affiliation: Qatar University, Department of Computer Science His work spans multiple domains including: Explainable AI for healthcare and security Biometric authentication using EEG and ECG signals Blockchain applications in UAV networks and industrial systems Federated learning security and data poisoning defense Digital twin threat modeling Recent publications demonstrate technical innovation in: YOLOv3-based safety monitoring Physics-informed neural networks Hybrid TESLA protocol security Shadow AI cybersecurity Metaverse threat intelligence

• Machine Learning
• Healthcare Informatics
• Explainable AI
• +6 more

Kai-Chih Pai is an Associate Professor at China Medical University's College of Information Science and Technology, Department of Computer Science, with a distinguished research career spanning over 14 years. His work bridges computer science and healthcare, focusing on practical AI applications that address critical medical challenges. Dr. Pai's research interests center around Machine Learning applications in healthcare , Explainable AI systems , Natural Language Processing for Chinese language , and Educational Technology . His work demonstrates a strong commitment to developing AI solutions that are not only technically sophisticated but also interpretable and clinically useful. His research trajectory shows a clear evolution from educational technology applications to increasingly sophisticated healthcare AI systems. His publication record reveals significant contributions to medical decision support systems , particularly in acute kidney injury prediction , pneumonia diagnosis , and mortality prediction in critical care settings. More recently, he has been exploring cutting-edge applications of large language models for industrial knowledge management. His work consistently emphasizes the importance of model interpretability in medical contexts. Federated machine learning approaches for multi-institutional medical research Privacy-preserving healthcare technologies using homomorphic encryption Adaptive learning systems for Chinese language education Predictive analytics for critical care medicine Dr. Pai has established a productive research program with consistent publication output in high-impact venues, demonstrating expertise that spans both theoretical AI development and practical healthcare applications. His collaborative work with medical professionals across Taiwan highlights the interdisciplinary nature of his research and its real-world impact.

• Machine Learning
• Process Systems Engineering
• Industry 4.0
• +5 more

János Abonyi is a Professor at the Department of Process Engineering, Faculty of Engineering, University of Pannonia, Hungary. With over 118 publications spanning from 2000 to 2024, he has established himself as a leading researcher in process systems engineering, machine learning applications, and Industry 4.0 technologies. His work bridges theoretical computational intelligence with practical industrial applications, particularly in process optimization, fault detection, and smart manufacturing systems. Abonyi's research interests focus on applying machine learning and data mining techniques to industrial process systems. His work spans several key areas including soft sensor development, fault detection and diagnosis, optimization algorithms for manufacturing, and Industry 4.0/5.0 applications. He has pioneered approaches combining sequence mining with deep learning for alarm management, reinforcement learning for disassembly line optimization, and explainable AI for industrial applications. His research demonstrates a consistent trajectory from fundamental computational intelligence methods to their practical implementation in real industrial settings. His recent publications (2020-2024) show a strong emphasis on Industry 4.0 and 5.0 applications, with particular focus on human-machine collaboration, real-time locating systems, digital twins, and explainable AI for industrial applications. The interdisciplinary nature of his work is evident through collaborations across computer science, electrical engineering, and chemical engineering domains. His research group has produced significant contributions in optimization algorithms, particularly in reinforcement learning applications for manufacturing and process systems. Abonyi has supervised numerous PhD students who have become active researchers in their own right, including Tamás Ruppert, Ágnes Vathy-Fogarassy, and Balazs Feil. His collaborative network extends internationally, with publications in top journals including IEEE Access, Sensors, and Computers & Chemical Engineering. His work demonstrates strong industry relevance with practical implementations in manufacturing, process control, and industrial automation contexts.

• Human-Centered AI
• Psychology and Technology
• Machine Learning
• +5 more

Chao Zhang is an Assistant Professor in Human-Centered AI at Eindhoven University of Technology (TU/e), specializing in the intersection of psychology and technology. He holds an MSc and PhD from TU/e, with a focus on using data-driven methods and psychological theories to design AI systems that positively influence human behavior. His research spans behavior change interventions, AI-driven health persuasion, and the social implications of AI systems. He coordinates the HUMAN-AI Alliance program and co-organized workshops on human-centered AI. Education: MSc (Human-Technology Interaction, TU/e), PhD (Intelligent Systems for Lifestyle Behavior Change, TU/e). Research interests include machine learning applications in psychology, computational modeling for behavior change, and ethical AI design. Awards include a 2021 PhD Thesis Award nomination. Key collaborations include work with Utrecht University and projects like ENFIELD (European Lighthouse for Trustworthy AI). He teaches courses on behavioral research methods and human-AI interaction. His lab explores topics like trust in XAI systems and human-robot bonding mechanisms.

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

Ilya Makarov is a Professor at the Department of Applied Mathematics and Informatics within the Faculty of Computer Science at the National Research University Higher School of Economics in Moscow. His research spans multiple domains of artificial intelligence with particular emphasis on practical applications of machine learning techniques. His research interests focus on Graph Neural Networks, computer vision systems, self-supervised learning methodologies, and industrial AI applications. Makarov's work demonstrates strong interdisciplinary connections between theoretical machine learning and practical implementations in areas ranging from manga colorization for augmented reality to fault diagnosis in industrial systems and financial trading algorithms. Analysis of his recent publications reveals a strong trend toward developing robust, interpretable AI systems with practical industrial applications. His work on SensorSCAN for fault diagnosis, GNN-AID for graph neural network analysis, and manga colorization techniques shows consistent innovation in adapting deep learning to domain-specific challenges while addressing issues of domain adaptation and robustness. Makarov actively mentors junior researchers as evidenced by consistent collaboration patterns, with Nikita Severin, Maksim Golyadkin, and Vitaliy Pozdnyakov appearing as frequent co-authors across multiple publications where Makarov serves as senior author. His research group appears to maintain strong collaborations with both academic and industrial partners, tackling challenges in autonomous driving, industrial monitoring systems, and scientific computing applications. The breadth of application domains covered in his recent work suggests a highly adaptable research program with significant real-world impact potential.

• Computer Science
• Artificial Intelligence
• Cybersecurity
• +5 more

Kaushik Roy is an academic researcher affiliated with multiple institutions including North Carolina State University, Purdue University, and North Carolina A&T State University. His primary focus is on computer science, cybersecurity, and AI-driven solutions for real-world challenges. He has extensive experience in machine learning applications for intrusion detection, deepfake detection, biometrics, and federated learning frameworks. His work often bridges theoretical advancements with practical implementations in domains like IoT security, healthcare analytics, and cyber-physical systems. Affiliations: North Carolina State University, Purdue University, North Carolina A&T State University, and others Research Interests: Cybersecurity, Deep Learning, Biometric Authentication, Federated Learning, and AI Ethics He has co-authored over 105 publications, focusing on topics like anomaly-based intrusion detection, malware traffic classification, and privacy-preserving techniques. His recent work emphasizes federated learning architectures for healthcare and transportation IoT systems. Roy collaborates with industry partners to translate research into deployable solutions, particularly in edge computing and sensor networks.

• Bioinformatics
• Computational Biology
• Machine Learning
• +3 more

Hong Qin is a Professor affiliated with the University of Tennessee at Chattanooga, USA, and previously with Tuskegee University's Department of Agricultural and Environmental Sciences. His research spans computational biology, machine learning, environmental informatics, and healthcare data science. He has co-authored over 20 papers since 2007, focusing on topics like viral fitness modeling, optimization algorithms, and AI applications in healthcare diagnostics. Research interests emphasize interdisciplinary approaches, including bioinformatics for viral mutation analysis, swarm intelligence for optimization problems, and explainable AI for medical imaging. Recent work includes federated learning for healthcare privacy, blockchain interoperability, and environmental monitoring via satellite imagery. His publications reflect a strong focus on AI-driven solutions for global health challenges, such as SARS-CoV-2 variant analysis and equitable AI frameworks for public health. He has contributed as an editor for journals like IEEE ACM Transactions on Computational Biology and Bioinformatics, highlighting his role in shaping bioinformatics research.

• Machine Learning
• Time Series Forecasting
• Credit Card Fraud Detection
• +3 more

Gianluca Bontempi is a Professor in the Department of Computer Science at the Université Libre de Bruxelles (ULB), Faculty of Sciences. His research spans machine learning, time series forecasting, and big data analytics, with significant applications in credit card fraud detection, biomedical engineering, and causal inference. He leads projects on adaptive learning systems, financial cybersecurity, and high-dimensional data modeling. Research Focus: Bontempi's work integrates theoretical machine learning with real-world challenges. Key areas include: Developing dynamic factor models for multivariate time series forecasting Designing incremental learning frameworks for fraud detection in evolving data streams Advancing causal inference methods for personalized decision-making Creating interpretable AI systems for biomedical and financial applications Publication Trends: His recent articles (2019–2026) show a strong emphasis on combating concept drift in financial fraud systems, improving fairness in feature selection, and enhancing cross-domain adaptation techniques. Methodological innovations in multi-task learning and adversarial modeling are recurrent themes. Awards & Recognition: No awards explicitly mentioned in the source text. Research Infrastructure: Bontempi collaborates with interdisciplinary teams across Europe on large-scale projects involving biomedical data (TCGAbiolinks), urban mobility (traffic forecasting), and AI ethics (CLAIRE COVID-19 initiative).

• Artificial Intelligence
• Causal Inference
• Machine Learning
• +4 more

Emre Kiciman is a leading researcher in artificial intelligence, causal inference, and social computing. His work spans machine learning, ethics in AI, and applications in healthcare and cybersecurity. He has authored over 135 publications in top venues such as ICWSM, KDD, and NeurIPS, focusing on causal discovery, LLM security, and human-AI collaboration. His research bridges theoretical advancements with real-world impacts, including improving mental health monitoring via social media and developing causal tools for robust decision-making. He collaborates widely with institutions and tech companies, contributing to open-source causal AI frameworks and ethical guidelines for AI systems.

Ullrich KötheView profile

Associate Professor

• Machine Learning
• Neural Networks
• Explainable AI
• +6 more

Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .

Oskar von StrykView profile

Professor

• Robotics
• Simulation
• System Optimization
• +3 more

Oskar von Stryk is a Professor in the Department of Computer Science at the Technical University of Darmstadt, leading the Simulation, System Optimization and Robotics research group. He serves as a founding member of hessian.AI (Hessian Center for Artificial Intelligence) and AI.DA (Artificial Intelligence@TU Darmstadt), and previously held leadership roles including Dean of Computer Science (2011-2013) and Founding Dean of Computational Engineering (2001-2007). His research integrates robotics, simulation, and system optimization with applications in rescue robotics, rehabilitation exoskeletons, and human-robot interaction. Current work focuses on autonomous systems for disaster response, medical rehabilitation devices, and AI-assisted navigation in hazardous environments. He develops advanced algorithms for mobile robots including SLAM, path planning, and human-robot collaboration frameworks. Recent publications (2023-2025) demonstrate strong emphasis on radiation hazard mitigation using AI-robot systems, exoskeleton development for temporomandibular disorders, and mobile robot navigation in rough terrain. His work bridges theoretical robotics with practical emergency response applications through the German Rescue Robotics Center. He leads the Simulation, System Optimization and Robotics group while serving as spokesperson for the Graduate School on Cooperative, Adaptive and Responsive Monitoring in Mixed Mode Environments. His team actively contributes to hessian.AI and the German Rescue Robotics Center, developing field-deployable robotic systems for disaster response and industrial inspection.

Alina GeigerView profile

Researcher

• Genetic Programming
• Genetic Improvement
• Machine Learning
• +3 more

Alina Geiger is a Researcher at the Chair of Business Informatics within the Faculty of Law and Economics at Johannes Gutenberg University Mainz. She currently serves as a research assistant and supervises final theses and seminar papers. Her expertise lies in Genetic Programming (GP), Genetic Improvement (GI), Machine Learning, and Deep Learning. Education: Master of Science in Management (2020-2023, JGU Mainz) Bachelor of Science in Economics (2017-2020, JGU Mainz) Research Interests: Alina’s work focuses on advancing evolutionary algorithms like Genetic Programming and Genetic Improvement, particularly leveraging machine learning techniques. She explores applications in symbolic regression, software evolution, and algorithmic optimization. Her recent studies investigate the integration of large language models (LLMs) to enhance mutation operators in genetic improvement and evaluate explainability in AI-generated software patches. Teaching: Since 2022/23, she has taught 'Introduction to IT' courses and oversees student research projects. Her academic contributions reflect a strong emphasis on bridging theoretical advancements with practical software engineering challenges.

Dominik SobaniaView profile

Researcher

• Large Language Models in Software Development
• Genetic Programming for Program Synthesis
• Genetic Improvement
• +3 more

Dr. Dominik Sobania is a researcher at the Department of Business Informatics at Johannes Gutenberg University Mainz. His work focuses on the intersection of artificial intelligence and software development, particularly in the areas of Large Language Models (LLM), Genetic Programming (GP), and Genetic Improvement (GI). He explores how evolutionary computation can enhance program synthesis and improve software systems. His research interests include applying genetic algorithms to solve complex programming challenges, integrating LLMs with traditional GP techniques, and optimizing selection methods for better performance in symbolic regression and program analysis. Notable projects include ImageBreeder (combining diffusion models with evolutionary methods) and ComfyGI (automated image workflow improvement). Dr. Sobania's publications emphasize efficient algorithm design, such as down-sampled lexicase selection for GP, and critical assessments of LLM-generated software patches. He actively contributes to advancing AI-driven software development through empirical studies and comparative analyses of machine learning techniques. No scientific awards or formal advising records are mentioned in the provided texts.

Stefan HillmannView profile

Researcher

• Dialogue Systems
• Multimodal Human-Computer Interaction
• Usability Evaluation
• +3 more

Dr.-Ing. Stefan Hillmann is a researcher at the Quality and Usability Lab at Technische Universität Berlin, focusing on usability evaluation of spoken and multimodal dialogue systems. He holds a Doctor of Engineering degree and has been involved in various research projects since 2010, including the DFG-funded 'User Model' project and the Universal Home Control Interface@Connected Usability initiative. His work emphasizes dialogue management, machine learning, and reinforcement learning applications in conversational interfaces. He coordinates MOOCs on Communication Acoustics and teaches courses on topics like neural networks in language technology and multimodal interaction. Education: Diplom-Informatiker (2006), PhD candidate (since 2010) at TU Berlin. Research Projects: Smart home usability evaluation, reinforcement learning for dialogue systems, and medical dialogue system explainability. Research Interests: Prioritizes human-computer interaction, automatic usability prediction, and context-aware dialogue systems. His contributions span theoretical frameworks and applied systems, with a focus on practical usability in healthcare and education. Teaching & Mentorship: Coordinates edX courses and supervises study projects like 'Smart Picture Frame' and 'BC Stories VR.' Engages students in applied research through seminars and practical exercises. Publications: Over 15+ peer-reviewed papers since 2019, focusing on dialogue systems, explainability in AI, and crowdsourcing evaluation techniques. Recent work includes chatbot usability studies and multimodal interaction frameworks.