Tobias Hallmen is a Researcher at the University of Augsburg 's Chair of Human-Centered Artificial Intelligence within the Faculty of Applied Computer Science . His work focuses on multimodal conversation analysis using machine learning and artificial intelligence in psychotherapy and medical/educational training contexts. Research interests include: Automated evaluation of conversational quality through multimodal data (audio, video, text) AI-based assessment systems for therapy sessions and parent-teacher interviews Development of real-time feedback mechanisms for skill improvement Integration of behavioral signal processing and empathy modeling Recent publications demonstrate expertise in vocal burst analysis , emotional mimicry prediction , and multimodal foundation models for behavioral annotation. Key technical domains: deep learning architectures , cross-modal data correlation , and computer vision applications . The Chair team under Prof. Dr. Elisabeth André currently includes 23 members with 10 projects active, including TherapAI (psychotherapy analysis) and KodiLL (medical training systems). Tobias Hallmen's work particularly addresses speaker classification , reception signal analysis , and remote physiological measurement techniques like video-based heart rate detection .
Juan Jesús García Domínguez is a Professor in the Department of Electronics at the Universidad de Alcalá. His primary research focuses on sensor systems, wearable technology, and their applications in healthcare and smart environments. He leads the GEINTRA research group, dedicated to Electronic Engineering Applied to Intelligent Spaces and Transport. Dr. García Domínguez holds a PhD from Universidad de Alcalá (2006) with a thesis on infrared obstacle detection in railway environments. His research interests include biomedical sensors, inertial measurement units (IMUs), indoor localization systems (BLE/UWB), and elderly care technology. He has pioneered work on non-invasive behavioral monitoring, gait analysis, and wearable devices for vulnerable populations. Recent projects involve developing systems for activity recognition, fall detection, and telemedicine applications. Key contributions include the FrailWear wearable IoT device, BLE-based behavioral analytics frameworks, and multi-sensory systems for long-term patient monitoring. His work bridges electronic engineering with healthcare, emphasizing practical solutions for aging-in-place and smart transportation safety. Teaching includes digital electronics and active learning methodologies in engineering education. Dr. García Domínguez has published extensively on topics like acoustic positioning systems, IMU calibration algorithms, and NILM techniques for energy management. His lab focuses on translating sensor data into actionable insights for medical and environmental applications.
Syed Muhammad Anwar serves as an Associate Professor in Software Engineering at the University of Engineering and Technology (UET) Taxila, Pakistan. He maintains a significant dual affiliation with the Sheikh Zayed Institute at Children's National Hospital in Washington, DC, USA. Additionally, he holds leadership roles as Co-founder and CTO of Sense Digital PVT. Ltd. and Director of both the Virtual Reality and Machine Learning Lab and the Signal Image Multimedia Processing and Learning (SIMPLE) Group at UET Taxila. Dr. Anwar's research spans multiple cutting-edge domains at the intersection of signal processing, machine learning, and medical applications. His primary research interests include: Multimedia Communication and Signal Processing Image and Video Coding and Quality Assessment Biomedical Signal Processing and Brain-Computer Interfaces Medical Imaging including Segmentation, Detection, and Diagnosis Deep Learning applications in healthcare diagnostics Emotion Classification and Human Behavior Modeling His recent scholarly output demonstrates a strong emphasis on applying deep learning techniques to medical image analysis challenges, particularly in brain tumor segmentation, liver tumor detection, and Alzheimer's disease classification. There's also significant work in EEG-based applications including emotion recognition, stress quantification, and game expertise classification. His research effectively bridges theoretical machine learning advances with practical healthcare applications, showing particular strength in adapting deep learning architectures to medical imaging challenges across multiple organ systems. Dr. Anwar actively mentors the next generation of researchers through his leadership of the SIMPLE research group. His current advisees include: PhD Students: Sanay Muhammad Umar Saeed (Quantification of human stress), Romana Farhan (Security in body area networks), Nosheen Sohail (Medical Image Analysis), Amin Ullah (Knowledge extraction), and Saqib Mehboob (Structural health monitoring) MS Students: Haseeb Iftikhar (Doctor recommender system), Faizah Malik (Sentiment analysis), Samreena Aslam (Fashion image retrieval), Huma Shabbir (Fashion image tagging), Khola Rafiq (Ischemic stroke detection), and Saba Naseem (Blood vessel segmentation) As Director of the Virtual Reality and Machine Learning Lab and the SIMPLE research group, Dr. Anwar oversees a dynamic research environment focused on advancing signal processing, multimedia analysis, and machine learning applications, particularly in healthcare contexts. His lab maintains strong collaborations between UET Taxila and international institutions, including Children's National Hospital in Washington DC, facilitating technology transfer between academic research and clinical practice.
Dr. Hongwei Tan serves as Group Leader at the Department of Molecular Electronics, Max Planck Institute for Polymer Research (MPIP) in Mainz since November 2024. Previously, he was Research Fellow (2022-2024) and Postdoctoral Fellow (2017-2022) at Aalto University's Department of Applied Physics, following postdoctoral work at the University of Massachusetts Amherst on neurointerface devices. His academic credentials include: Bachelor of Physics, Nankai University (2011) Doctorate in Materials Physics and Chemistry, University of Chinese Academy of Sciences (2016), focusing on neuromorphic photomemristors Tan's research pioneers neuromorphic biointerfaces—integrating brain-inspired electronics with biological systems to enable energy-efficient computation and seamless biointeraction. His work targets transformative applications in brain-machine interfaces, prosthetics, and diagnostics through interdisciplinary convergence of neuroscience, materials science, and engineering. His publication portfolio (2015-2024) reveals a clear evolution from foundational optoelectronic memristor research toward complex neuromorphic systems for sensory processing. Key thematic trajectories include photomemristor-based vision systems, tactile coding with spiking nerves, and nanoionic human-machine interfaces, demonstrating consistent innovation in bioinspired hardware. As MPIP Group Leader, Tan directs research strategy and team mentorship within his neuromorphic electronics program, though specific advisees and grant details remain unlisted in available materials. His laboratory focuses on developing integrated neuromorphic systems—from novel materials and devices to algorithms—that facilitate bidirectional communication between electronic and biological neural networks for next-generation neurotechnologies.
Max Hort is a researcher at Simula Research Laboratory in Norway specializing in Machine Learning for Software Engineering, Defect Detection, and Software Fairness. He has established himself as an active contributor to the software engineering research community through numerous publications and program committee roles at major conferences. His research focuses on: Machine Learning for Software Engineering Defect Detection Software Fairness Program Repair Log Parsing Hort's recent work demonstrates a strong emphasis on applying large language models to software engineering challenges, particularly in program repair and defect detection. His publications reveal a methodical approach to evaluating AI techniques in software engineering contexts, with special attention to reliability concerns like non-determinism in language models. He has also made significant contributions to the understanding of software fairness and bias mitigation. Through his active participation in program committees for ASE, ICSE, ESEC/FSE, and SANER conferences, Hort has earned recognition as a knowledgeable contributor to the field. His work bridges theoretical machine learning advancements with practical software engineering challenges, addressing critical issues in modern software development as AI-assisted programming becomes increasingly prevalent.
Brigitt Antonia Egloff serves as Lecturer and Deputy Head of Textile Design at Lucerne University of Applied Sciences and Arts (HSLU) within the School of Design, Film and Art. With continuous teaching since 1999 in the BA Textile Design program and from 2012-2018 in the Master's program, she maintains active research and teaching roles while leading international collaborations with institutions including National Institute of Design (India), Beijing Institute of Fashion Technology, and Sichuan School of Fine Arts (China). Her research centers on sustainable material strategies, specializing in textile recycling systems, natural fiber applications, and innovative color systems for solar photovoltaics. She pioneers the cross-disciplinary integration of textile design methodologies into non-textile domains—particularly solar energy systems—while emphasizing the symbiotic relationship between research and pedagogy. Current projects explore circular economy frameworks, sustainable medical textiles, and architectural solar integration. Recent publications reveal strong thematic focus on circular textile design (33% of recent output), solar-textile integration (27%), and sustainable manufacturing processes (20%), with growing emphasis on medical applications and cross-cultural design pedagogy. Her work consistently bridges theoretical research with industrial implementation through Innosuisse projects. Egloff's artistic contributions have earned significant recognition: Berlin Studio Fellowship for Artistic Work (2017) Chicago Studio Fellowship for Artistic Work (2017) Artistic Work Grant (2008) Artistic Work Grant (1998) She directs multiple interdisciplinary research initiatives including CIMProW (Circular Innovation: Medical Protective Wear), Solar Design Tools, and Textile Recycling 4.0, collaborating with EMPA, SUPSI, and textile industry partners. Her leadership extends to the Textile Design program's Examination Board and supervision of student research in sustainable material innovation. Current projects like Circle Up Textiles 2 and SpinnLab demonstrate her commitment to closing textile waste loops through industrial-academic partnerships. As Deputy Head of Textile Design since 2010, Egloff shapes curriculum development and research strategy within HSLU's Textile Design Lab, fostering student engagement with real-world sustainability challenges through projects like Viscosilab and TESS (Textile Eco System Sleep). Her 'Design and Sustainability' framework informs both pedagogy and industry collaborations.
Allel Hadjali is a Full Professor in Computer Science specializing in Data Engineering at ISAE-ENSMA (École Nationale Supérieure de Mécanique et d'Aérotechnique) in Poitiers, France. He is affiliated with the Laboratory LIAS (Laboratoire d'Ingénierie des Applications de la Connaissance et des Systèmes) at ISAE-ENSMA. His academic career includes progression from Associate Professor to his current Full Professor position, with extensive teaching experience across multiple computer science domains. Professor Hadjali's research falls within the data science domain, with particular focus on Exploitation, Extraction, and Recommendation (E2R). His work applies Computational Intelligence and Soft Computing techniques to massive data exploitation and analysis, including flexible querying approaches (Skyline, Gradual, and Bipolar queries), modeling and querying uncertain/incomplete data, cooperative answering techniques, and data reduction through linguistic summaries. He also conducts research in recommendation systems (learning-based and group recommendation) and extraction techniques (mining gradual patterns), along with related interests in data quality, intelligent systems, and crowdsourced data management. His publication record demonstrates consistent contributions to top-tier journals and conferences, with recent work focusing on skyline query processing, uncertain data management, RDF knowledge bases, and explainable AI. His research shows a clear trajectory from foundational work in fuzzy logic and uncertain databases toward more applied research in semantic web technologies and machine learning explainability. Professor Hadjali serves on the editorial boards of several prestigious journals including the Journal of Smart Environments and Green Computing, Sensors Journal, and the Universal Journal of Aeronautics and Aerospace Research. He has also organized special issues on topics such as uncertainty in cloud computing and managing uncertain data. At ISAE-ENSMA, Professor Hadjali teaches courses including Formal aspects of software engineering, Language interpretations and compilation, Programming languages, and Data management and exploitation. Previously as an Associate Professor, he taught courses on object modeling, distributed algorithms, operating systems, and advanced databases focusing on preferences and uncertainty. He leads the Data Engineering team within the Laboratory LIAS, which focuses on developing computational intelligence approaches for modern data challenges. His current projects include work on data quality (QDoSSI project funded by CNRS Mastodons 2016-2018) and research actions in GDR MADICS 2018 related to scientific data quality.
Hani Ragab Hassen is an Associate Professor leading the Cybersecurity with AI Research Group at the School of Mathematical & Computer Sciences, Heriot-Watt University. He specializes in cybersecurity, particularly in applying machine learning and NLP to address challenges such as malware analysis, intrusion detection, and access control systems. His roles include Associate Director of PGR Studies, Academic Integrity Officer, and former leadership in Learning & Teaching and Research programs. Research interests focus on cybersecurity fundamentals, including deep learning for malware detection, NLP-driven vulnerability assessment, and P2P systems. He has supervised numerous PhD students, currently advising three candidates on airport cybersecurity, NLP-based vulnerability detection, and network intrusion systems. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure security. Publications span malware detection frameworks (e.g., ActDroid, Droiddissector), network security tools (NTFA), and machine learning optimization (D2TS). He has developed datasets like SI22 for DDoS analysis and contributed to open-source tools. His research emphasizes practical applications of AI in securing networks and cloud environments. Notable contributions include advancing Android malware detection via control flow graphs and text analysis, and pioneering cloud security protocols like Stick for social platforms. His interdisciplinary work bridges cybersecurity with machine learning and data science, addressing real-world threats through innovative solutions.
Benjamin Auder is a statistical researcher affiliated with the Institut de Mathématiques d'Orsay (IMO) at Université Paris-Saclay , specializing in the probabilités-statistiques team. He holds an engineering position focused on applied statistics projects and computational infrastructure. Education: PhD in statistical modeling from CEA Cadarache (2008-2011), MSc in Sherbrooke (2006-2007), and engineering studies at ENSIMAG (Grenoble). Key roles include: Research collaboration at Lokad (2011-2012) optimizing forecasting engines Co-administration of laboratory computing clusters Teaching: Database Systems (Paris-Sud), C++ (ENSTA), Probability (Polytech) Research interests span statistical methodologies, clustering algorithms, computational tools, and database systems. Notable projects include mixmod classification library and epclust energy load classification. Active in open-source software development for statistical computing.
Chen-Yi Lu is a Graduate Research Assistant at Purdue University's Department of Agricultural & Biological Engineering under Major Professor Chaterji. His research bridges digital agriculture and data science. University: Purdue University Department: Agricultural & Biological Engineering Advisor: Professor Somali Chaterji His work focuses on applying artificial intelligence, machine learning, and Internet-of-Things (IoT) to agricultural systems. Key areas include automated pest monitoring, anomaly detection in animal behavior, and AI-driven decision-support tools for sustainable food production. Recent publications highlight his expertise in deep learning for real-time agricultural monitoring systems, generative adversarial networks (GANs) for data augmentation, and AIoT-based solutions for pest management. His research spans interdisciplinary domains like computational modeling, sensor networks, and digital ecosystem design. Chen-Yi Lu's contributions emphasize integrating scalable databases and machine learning with agricultural safety, automation, and environmental stewardship. Current projects involve optimizing data flow for precision agriculture and developing cloud/edge computing platforms to enhance rural disaster response systems.
Lin Qika is a Research Fellow at the Saw Swee Hock School of Public Health, National University of Singapore (NUS). His research focuses on advancing natural language processing (NLP) and AI applications in healthcare, particularly leveraging large language models (LLMs) for robust healthcare solutions. He holds a PhD from Xi’an Jiaotong University (2023), an M.S. from Beijing Institute of Technology (2019), and a B.S. from the same institution (2016). His expertise spans multi-modal representation learning, neuro-symbolic systems, and logical reasoning applied to healthcare challenges. Notable research includes developing frameworks like TECHS for explainable extrapolation reasoning and integrating knowledge graphs with LLMs. His work emphasizes practical healthcare applications, such as depression detection and medical diagnostics. Lin Qika’s recent publications (2022–2025) explore cutting-edge topics like contrastive graph representations, knowledge graph completion, and adversarial attacks on knowledge embeddings. He actively contributes to conferences like ACL, SIGIR, and KDD, showcasing innovations in AI-driven healthcare and multimodal reasoning. No scientific awards or funded grants are explicitly mentioned in the provided information.
John R. Kender is a Professor in the Department of Computer Science at Columbia University, affiliated with the Fu Foundation School of Engineering and Applied Science. He previously served as former Vice Dean of the same school and has been recognized as a Great Teacher by the Society of Columbia Graduates and a Distinguished Faculty Teacher by the Columbia Engineering School Alumni Association. His research focuses on computer vision, video understanding, gesture recognition, and artificial intelligence, supported by grants from DARPA, NSF, and industry partners like IBM and Microsoft. He has advised over 25 doctoral students and co-chaired the 2016 ACM International Conference on Multimedia Retrieval (ICMR). His teaching spans courses in object-oriented design, visual interfaces, and visual databases, with a strong emphasis on project-based learning. Kender's work includes pioneering contributions to semantic video indexing, multimodal analysis, and transfer learning techniques. Education: Advanced degrees in Computer Science (details not explicitly stated in texts). His research interests bridge computational methods and human-centric applications, including gesture-audience engagement correlations using EEG, cultural differences in news video production, and visual meme tracking in social media. His projects, such as the NSF-funded 'Tagging and Browsing Videos According to Affinity Groups,' highlight user-centered multimedia systems. Kender's work has been published in over 100 peer-reviewed articles and patents, addressing challenges in video segmentation, motion anticipation, and neuroimage alignment. Recent articles explore geometric approaches to transfer learning, 3D motion prediction, and cultural analysis through computational aesthetics. These reflect a trend toward cross-disciplinary methods combining AI, computer vision, and social sciences. Awards: Multiple teaching recognitions, including Columbia's top faculty teaching honors. Advising highlights include mentoring students across decades, with notable alumni contributing to academia and industry. His grants total over $10M from federal and industrial sources. Kender's lab focuses on real-world applications like educational video analysis, medical imaging, and event-driven multimedia systems. Current projects include developing explainable AI for video clustering and probabilistic GAN-based human motion prediction.
Diogo Caetano is an Invited Assistant Professor at the Department of Electrical and Computer Engineering, Higher Technical Institute (University of Lisbon). His research focuses on advanced sensor systems, magnetoresistive technologies, and biomedical applications. Key areas include integrated circuits for magnetic sensors, non-destructive testing methodologies, neural networks for defect detection, and miniature diagnostic tools. He is affiliated with INESC MN, a prominent research unit in applied engineering. His work spans interdisciplinary projects such as magnetic flow cytometry for bacteria detection, eddy-current based industrial inspection systems, and low-power biomedical sensor interfaces. Recent contributions include innovations in sigma-delta modulators for organ-on-chip systems and high-resolution nondestructive test probes using magnetoresistive arrays. Caetano has co-authored over 30 technical publications and holds patents in sensor interface design and signal processing. His research emphasizes practical applications in healthcare, industrial automation, and structural monitoring, leveraging both hardware innovation and machine learning techniques.
Jiří Kosinka is an Associate Professor (Tenure Track) at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Bernoulli Institute. He leads the Scientific Visualization and Computer Graphics research group. His roles include coordinating and lecturing in Computer Graphics and Advanced Computer Graphics courses, as well as serving as an editor for journals like Computer-Aided Design and Graphical Models . Academic Position: Associate Professor, Tenure Track Affiliations: Bernoulli Institute, Faculty of Science and Engineering Research Group: Scientific Visualization and Computer Graphics Education PhD in Mathematics (2006), Charles University, Prague MSc in Mathematics (2002), Charles University, Prague Research Interests Kosinka's work focuses on geometric modeling, computer graphics, and image processing. He develops algorithms for subdivision surfaces, numerical quadrature, and fluid simulation, with applications in surgical planning and medical visualization. His research bridges theoretical contributions with practical implementations in CAD systems and real-time rendering. Conference Contributions Co-organizer of DGMM 2025 (Discrete Geometry and Mathematical Morphology) in Groningen Program Chair for AniNex 2022/2023 (Next Generation Computer Animation) IPC member for SGP, SPM, Pacific Graphics, and other key conferences Editorial Roles He serves on the editorial boards of Computer-Aided Design and Graphical Models , and has guest-edited special issues in Computer Aided Geometric Design . Labs & Teams He leads the Scientific Visualization and Computer Graphics group, collaborating on projects like BoneStory (3D surgical planning) and fluid dynamics simulations. His lab focuses on advancing geometric algorithms and their real-world applications.
Professor Longbing Cao is the Distinguished Chair in Artificial Intelligence and Director of the Frontier AI Research Centre at Macquarie University. He holds dual PhDs in Artificial Intelligence and Computing Science, an MSc in Communication, and a Bachelor's degree in Electrical Engineering. His research spans AI, data science, behavior informatics, and enterprise innovation, with over 400 publications and significant industry collaborations. Education: PhD in Pattern Recognition and Intelligent Systems, Graduate University of Chinese Academy of Sciences PhD in Computing Science, University of Technology Sydney MSc in Communication Bachelor's in Electrical Engineering Research Interests: Focuses on AI systems, federated learning, time series forecasting, and humanoid robotics. His work integrates theoretical advancements with real-world applications in finance, healthcare, and smart cities. Recent Articles: Explores cutting-edge topics like generative AI (ProgDiffusion), federated learning (FedSI), and pandemic modeling (waning effect of interventions). These contributions highlight advancements in both foundational AI and applied data science. Awards: Eureka Prize (2019), ACM Distinguished Scientist (2019) Grants & Projects: Leads major initiatives such as the Federated Omniverse Facilities for Smart Digital Futures (2024-2025) and Data Complexity and Uncertainty-Resilient Deep Variational Learning (2024-2027). Prior roles include Founding Director of UTS Advanced Analytics Institute (2011-2014). Labs & Teams: Directs the Frontier AI Research Centre and collaborates with global institutions like Microsoft, Commonwealth Bank, and Shanghai Stock Exchange.
