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Julia SchlüterView profile

Professor

• Corpus Linguistics
• Language Variation and Change
• Historical Linguistics
• +7 more

Professor Julia Schlüter is a distinguished academic in the Chair of English Linguistics within the Humanities Faculty at the University of Bamberg, Germany. She has served as Senior Lecturer at the Chair of English Linguistics and Language History under Prof. Manfred Krug since June 2008, holding the title of Professor following her habilitation in 2008. Her institutional profile demonstrates deep commitment to both research and teaching innovation, particularly through her leadership of the KorPLUS project and development of open educational resources for corpus linguistics. Her research interests span corpus linguistics for English language learners, empirical methods for studying language variation and change, and the application of corpus methods to teaching. She specializes in examining grammatical, phonological, and lexical differences between British and American English across historical periods from Middle English to present-day usage. Her work investigates phonological variation (particularly phonotactically controlled alternations), morphological change, and syntactic variation through corpus analysis, with special attention to functional grammar and grammaticalization theory. Professor Schlüter's recent publications (2022-2025) reveal a strategic evolution in her research focus, with increasing emphasis on the intersection of corpus linguistics and digital education. While maintaining her foundational work in historical English linguistics, she has developed significant expertise in applying corpus methods to language teacher education and evaluating AI writing tools. Her work demonstrates consistent methodological innovation, moving from traditional corpus analysis to blended learning approaches and digital educational resource development. Her scientific recognition includes: Winner of the 2025 Teaching Innovation Prize from the International Society for the Linguistics of English for the KorPLUS project University of Bamberg Prize for outstanding habilitation (2009) Lise Meitner Programme post-doctoral scholarship (2005-2006) Rectorate Prize from University of Paderborn for outstanding Ph.D. thesis (2005) Multiple DAAD scholarships for international study Professor Schlüter actively supervises doctoral research, currently guiding three Ph.D. candidates (Katharina Deckert, Aklima Nahar, and Nikolai Beland) while having successfully completed supervision for several others. She leads the KorPLUS project (2021-2025), funded by the Stiftung Innovation in der Hochschullehre, which develops open educational resources for corpus linguistics. Her research has been consistently supported by the German Research Foundation (DFG) and other funding bodies throughout her career. She heads the KorPLUS team (with Carina Großmann and Katharina Deckert) which develops the interactive Open Educational Resource platform for corpus linguistics. Her YouTube channel offers video tutorials on corpus basics, and she has created the Video Podcast Series "How to Update your Grammar" for English teachers. She regularly organizes in-service teacher trainings and collaborates with the Virtual Linguistics Campus at RWTH Aachen University to deliver her educational materials globally.

Alina RoitbergView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Human Activity Recognition
• +7 more

Alina Roitberg is a Junior Professor (Assistant Professor) at the University of Stuttgart , affiliated with the Faculty of Computer Science, Electrical Engineering and Information Technology . Her research focuses on advancing computer vision, machine learning, and robotics applications, particularly in human activity recognition, domain adaptation, and synthetic data generation. She explores challenges in action understanding, cross-domain generalization, and real-world deployment of AI systems in fields like healthcare, autonomous vehicles, and industrial automation. Her work emphasizes robust learning under noisy conditions, multimodal data fusion, and ethical AI applications. Recent projects include foundational studies on large language models in construction (AEC), video-based muscle group estimation, and improving driver activity recognition for autonomous vehicles. She also investigates circular factory design through uncertainty-aware process optimization and human-robot interaction. Dr. Roitberg's contributions span academic publications and industrial collaborations, addressing both theoretical advancements and practical implementations. Her research bridges computer vision techniques with real-world problems, emphasizing scalability and ethical considerations in AI deployment.

• Wireless Communications
• Signal Processing
• MIMO Systems
• +3 more

Konstantinos Nikitopoulos is a Professor at the University of Surrey , UK, specializing in Wireless Communications and Signal Processing . His research focuses on MIMO Systems , Open-RAN , and Non-Linear Processing for next-generation wireless networks. His recent work explores Analogue Processing for Tbps Wireless Systems and Neuromorphic Computing in MU-MIMO detection. He has developed frameworks like MIMO-SoftiPHY and SACCESS for software-based radio acceleration and power-efficient network design. Key Publications : Power-Efficient RIC, NL-COMM, NeuroMIMO Collaborators : Rahim Tafazolli, George Katsaros, Marcin Filo His research impacts 6G Network Development through innovations in Beamforming , Channel Estimation , and Software-Defined Radios .

Gabriele BavotaView profile

Associate Professor

• Software Quality
• Empirical Software Engineering
• Mining Software Repositories
• +3 more

Gabriele Bavota is an Associate Professor at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. He leads the SEART (Software Engineering Advanced Research Team) group and serves as Principal Investigator for the DEVINTA ERC starting grant focused on developer intelligence through mining software artifacts. Dr. Bavota's research spans Software Quality, Empirical Software Engineering, and Mining Software Repositories. His work has evolved from foundational studies on code smells and technical debt to cutting-edge research at the intersection of artificial intelligence and software development. He has made significant contributions to understanding API usage patterns, software quality metrics, and developer behavior through empirical studies of large software repositories. His recent publications reveal a strong focus on AI-assisted software development, with extensive research examining code generation, code summarization, and code review automation using large language models. He has also expanded his research to include quality assurance in game development (detecting game stuttering and low engagement events) and voice user interface testing. His work consistently bridges theoretical insights with practical applications for software developers. ACM SIGSOFT Distinguished Paper Award for API compatibility research (MSR 2019) ACM SIGSOFT Distinguished Paper Award for Hugging Face model documentation study (ICPC 2024) ACM SIGSOFT Distinguished Artifact Award for deep learning fault taxonomy (ICSE 2020) As an active member of the software engineering research community, Dr. Bavota serves on program committees for major conferences including ICSE, ASE, FSE, and MSR. He has held leadership roles such as Program Co-Chair for ICSME 2023 and Vision/Reflection Track Co-Chair for ICSE. His SEART research group develops practical tools like the SEART Data Hub that streamline large-scale source code mining and preprocessing for empirical software engineering research.

Yan ZhangView profile

Lecturer

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

Yan Zhang is a scientific leader at Meshcapade and a guest lecturer at ETH Zurich's Computer Vision and Learning Group (VLG). He previously served as a postdoctoral researcher at ETH Zurich (2020-2023) and research intern at Max Planck Institute for Intelligent Systems (2018-2020). His research focuses on generative human foundation models, human motion and behavior synthesis, 3D human perception, and applications in AR/VR, embodied AI, and interactive avatars. He has pioneered methods for scene-conditioned motion generation, contact-aware reconstruction, and egocentric interaction modeling. His recent publications (2025-2020) span Real-time motor models for avatars (PRIMAL, ICCV'25) Diffusion architectures for motion (RoHM, CVPR'24) Scene-population algorithms (Odysseus, CVPR'22) Physics-aware reconstruction (EgoHMR, ICCV'23) Whole-body grasping models (SAGA, ECCV'22) Multi-modal datasets (EgoBody, ECCV'22) Scientific recognition includes the Qualcomm Innovative Fellowship Europe 2023 . He organized workshops at CVPR'25, ECCV'24, and ECCV'22, and served on senior program committees (AAAI'26) and area chairs (CVPR'25). As co-supervisor, he mentored student projects on diffusion-based hand motion capture, 3D pose estimation, body-scene interaction, and mixed reality navigation at ETH Zurich (2020-2023). His work bridges computer vision, machine learning, and computer graphics to advance human-centric AI systems.

Almut Sophia KoepkeView profile

Researcher

• Multi-Modal Learning
• Computer Vision
• Audio-Visual Processing
• +3 more

Dr. Almut Sophia Koepke is a junior research group leader and TUM Junior Fellow at the Technical University of Munich (TUM) and University of Tübingen. She leads the multi-modal learning research group focusing on video understanding through sound, vision, and text integration. University: Technical University of Munich School: TUM School of Computation, Information and Technology Department: Informatics 9 Academic Rank: Researcher Her research spans multi-modal learning, audio-visual foundation models, and cross-modal attention mechanisms. Key themes include: Advancing zero-shot learning through language-guided audio-visual models Developing explainable AI systems via attention pattern translation in VQA Exploring temporal understanding in video-adverb retrieval Building robust multi-modal representations for self-driving applications Recent publications analyze foundation model capabilities in audio-visual tasks (ICCV 2025), temporal reasoning (ACMMM 2024), and cross-modal attention frameworks (ECCV 2022). She co-organizes CVPR workshops on foundation model evaluations and serves as area chair/reviewer for major conferences.

Hans UszkoreitView profile

Professor

• Artificial Intelligence
• Natural Language Processing
• Information Extraction
• +3 more

Hans Uszkoreit is a German AI researcher and Honorary Professor at Technische Universität Berlin, specializing in language and knowledge technologies. He serves as Scientific Director at the German Research Center for Artificial Intelligence (DFKI) Berlin and co-founder of the Artificial Intelligence Technology Center (AITC) in Beijing. He is also Chief AI Advisor at Lenovo Corporation. His roles include leading initiatives like the Berlin Big Data Center and the German Smart Data Forum, funded by German federal ministries. Uszkoreit’s research focuses on natural language processing, machine translation, and knowledge-based systems, with contributions to projects such as the Multilingual Europe Technology Alliance and spin-off companies like Sematell and Acrolinx. He has supervised over 40 PhD theses, emphasizing computational linguistics and computer science. His work bridges academic research and industry applications, fostering innovation in AI and language technologies.

• Computer Vision
• Machine Learning
• Deep Learning
• +3 more

Prof. Margret Keuper is a Professor in the Department of Computer Vision and Machine Learning at the Max Planck Institute for Informatics. Her research focuses on advancing machine learning and computer vision techniques, with an emphasis on model fairness, adversarial robustness, and multimodal interactions. She leads interdisciplinary projects exploring topics such as dataset analysis, generative models, and climate action through visual narrative analysis. Research Interests: Her work bridges theoretical foundations and practical applications in domains like adversarial training, image classification robustness, and robotics perception. She explores how vision-language models can be steered to align with human biases and develops methods for data-efficient learning and interpretability. Recent Contributions: Recent work includes FAIR-TAT (model fairness via adversarial training), VSTAR (video synthesis), and TikZero (zero-shot graphics program generation). Her publications in top venues like CVPR, ICCV, and ICLR highlight contributions to both methodological innovation and real-world impact. Collaborations: Works closely with researchers across Max Planck and academic partners, focusing on projects such as sensor layout optimization, climate discourse analysis via social media imagery, and domain-aware foundation model fine-tuning.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +3 more

Li Wei is a distinguished academic affiliated with Tsinghua University, with a focus on interdisciplinary research spanning artificial intelligence, machine learning, and computer vision. His work often intersects with medical informatics, remote sensing, and signal processing, demonstrating a commitment to advancing technological solutions in healthcare, environmental monitoring, and engineering systems. Research interests include deep learning applications in clinical diagnostics, satellite data analysis for climate modeling, and optimization of energy storage systems. He has contributed to innovative solutions in areas such as UAV-enabled edge computing, privacy-preserving blockchain protocols, and thermal-based surveillance systems. His collaborative projects often involve multidisciplinary teams across institutions. Publications reflect a strong emphasis on practical applications, such as mobile health tools for tumor recognition, transformer-based super-resolution techniques for oceanography, and AI-driven risk classification models for respiratory diseases. While no specific awards or grants are listed, his prolific output across top-tier journals indicates sustained research impact. Professional activities include contributions to conferences like RecSys, MICCAI, and AAAI, and editorial roles are implied through his extensive publication record. Collaborations with industry partners (e.g., in energy systems and medical imaging) suggest engagement with real-world problem-solving.

Guillermo GallegoView profile

Professor

• Robotics
• Computer Vision
• Event-based Vision
• +5 more

Guillermo Gallego is a Professor of Robotic Interactive Perception at the Faculty of Electrical Engineering and Computer Science , Technische Universität Berlin , holding the Einstein Center Digital Future (ECDF) Professorship since 2019. His research bridges robotics , computer vision , and applied mathematics , focusing on optimization methods for interdisciplinary imaging and control problems. Education : PhD in Electrical and Computer Engineering (Georgia Tech, 2011), MS in Mathematics (Georgia Tech, 2009), MS in Electrical Engineering (Georgia Tech, 2007), MS in Mathematical Engineering (Universidad Complutense de Madrid, 2005). Gallego's work explores event-based vision to enhance robot perception through low-latency sensing and real-time 3D reconstruction . He previously held postdoctoral positions at the Institute of Neuroinformatics (University of Zurich/ETH Zurich) and Technical University of Madrid (Marie Curie Experienced Researcher). His interdisciplinary projects span applications in ocean remote sensing , autonomous driving , and space exploration . Key scientific awards include the Fulbright Fellowship (2005-2010) and Marie Curie Experienced Researcher (2011-2014). His recent publications focus on event camera algorithms for optical flow , SLAM , and noise estimation , reflecting his leadership in event-based vision research. Collaborations include institutions like University of Zurich , Georgia Tech , and University of Pennsylvania . Research Grants : Funded through ECDF and Marie Curie programs. Labs : Affiliated with the Einstein Center Digital Future and Institute of Neuroinformatics (Zurich/ETH Zurich).

Leif KobbeltView profile

Professor

• Computer Graphics
• Geometry Processing
• Interactive Visualization
• +3 more

Leif Kobbelt serves as a University Professor at RWTH Aachen University, leading the Computer Graphics Group within the Department of Computer Science (Informatik 8). His research focuses on advancing geometry processing, interactive visualization, and computer graphics through innovative algorithmic solutions and interdisciplinary collaborations. Professor Kobbelt's research program centers on geometry acquisition and processing, with significant contributions to mesh generation, surface reconstruction, and neural rendering techniques. His work bridges theoretical geometry with practical applications in computer vision, photo-realistic image synthesis, and multimedia data transmission, often involving collaborations with industry partners and international research teams funded by DFG and EU sources. Recent publications (2023-2025) reveal a strategic integration of deep learning with traditional geometry processing, particularly in Gaussian splatting for real-time rendering, NeRF-based 4D content generation, and robust mesh Boolean operations. His group maintains leadership in quad mesh optimization and surface mapping while expanding into immersive visualization techniques for complex data analysis. The group has earned recognition through prestigious awards: Günter Enderle Best Paper Award at Eurographics 2023 Best Paper Award (1st place) at Symposium on Geometry Processing 2022 Honorable Mention for Best Paper at ACM Symposium on Virtual Reality Software and Technology Funding from Deutsche Forschungsgemeinschaft and European Union programs supports the group's research infrastructure and international collaborations. The team actively supervises graduate theses while developing open-source software tools that translate theoretical advances into practical industry applications, particularly in digital fabrication and immersive visualization systems. The Computer Graphics Group operates as a central hub for visual computing research at RWTH Aachen, maintaining strong ties with both academic institutions and technology companies. Their recent work on virtual reality educational tools and high-fidelity 3D reconstruction systems demonstrates commitment to knowledge transfer and real-world impact beyond traditional publication venues.

Michael BronsteinView profile

Professor

• Machine Learning
• Computer Vision
• Pattern Recognition
• +6 more

Michael Bronstein is a Professor at Università della Svizzera italiana (USI Lugano) in Switzerland and Imperial College London in the UK, where he holds the Chair in Machine Learning and Pattern Recognition. He serves as Head of Graph Learning Research at Twitter following the acquisition of his startup Fabula AI, and maintains a principal engineer position at Intel Perceptual Computing. His research focuses on the interplay between geometry, machine learning, and computer vision, with particular emphasis on non-Euclidean structured data. Professor Bronstein received his Ph.D. with distinction in Computer Science from the Technion in 2007. He has held visiting appointments at Stanford University, MIT, Harvard University (as a Radcliffe Fellow), and Tel Aviv University, and has been affiliated with multiple Institutes for Advanced Study including TUM-IAS where he was a Rudolf Diesel Industry Fellow (2017). He is a Fellow of IAPR, Senior Member of the IEEE, and a member of the Young Academy of Europe. His research program centers on theoretical and computational methods in spectral and metric geometry applied to computer vision, pattern recognition, and machine learning. He pioneered the field of geometric deep learning, developing novel neural network architectures that process non-Euclidean data structures like graphs and manifolds. His work spans from theoretical foundations to practical applications, with over 100 publications in top scientific journals and conferences, and has been featured in international media including CNN. Analysis of his recent publications reveals a strong trajectory in geometric deep learning with applications spanning computer vision, 3D shape analysis, social network analysis, and bioinformatics. His research consistently bridges theoretical innovation with real-world applications, developing novel neural architectures for processing complex data structures. The work demonstrates increasing interdisciplinary reach, connecting machine learning with fields from particle physics to molecular biology. Dalle Molle Prize (2018) Royal Society Wolfson Research Merit Award (2018) ERC Proof of Concept Grant (2018) Amazon AWS Machine Learning Research Award (2018) Fellow, International Association for Pattern Recognition (IAPR) Google Faculty Research Award (2017) Radcliffe fellowship, Harvard University (2017) Rudolf Diesel industrial fellowship, TU Munich (2017) ERC Consolidator Grant (2016) World Economic Forum Young Scientist (2014) Professor Bronstein has secured multiple ERC grants (Starting Grant 2012, Proof of Concept Grants 2016 and 2018, Consolidator Grant 2016) and has mentored numerous students who have contributed to over 30 granted patents. He has chaired more than a dozen conferences and workshops in his field and served as area chair at major computer vision conferences including ECCV 2016 and ICCV 2017. His research group at USI Lugano collaborates extensively with industry partners including Intel and Twitter. As a serial entrepreneur, Professor Bronstein co-founded Novafora (2005-2009) developing large-scale video analysis, Invision (2009-2012) which created low-cost 3D sensors and was acquired by Intel, and Fabula AI (2018-2019) focused on fake news detection which was acquired by Twitter. His work bridges theoretical research with commercial applications, with his technology contributing to Intel RealSense and Twitter's graph learning infrastructure.

• Accessibility Research
• Web Accessibility
• Mobile Accessibility
• +5 more

Shari Trewin is a prominent researcher in accessibility and human-computer interaction at IBM Research with over 25 years of scholarly contributions. Her work focuses on making digital technologies accessible to people with disabilities, particularly in web and mobile contexts. She has published extensively in top-tier venues including ACM SIGACCESS conferences (ASSETS, W4A) and journals, CHI, and other leading HCI publications. Dr. Trewin's research interests span web accessibility, mobile accessibility for users with physical and cognitive disabilities, inclusive design methodologies, and the application of artificial intelligence to improve accessibility. Her work addresses both theoretical foundations and practical implementations of accessible technologies, with particular emphasis on user-centered design approaches and evaluation methodologies. She has made significant contributions to understanding how people with disabilities interact with digital interfaces and how to design systems that accommodate diverse user needs. Her publication record shows a clear progression from foundational work on input devices and keyboard accessibility in the 1990s to contemporary research on AI fairness for people with disabilities. Recent publications demonstrate her leadership in addressing emerging challenges at the intersection of AI and accessibility, particularly around algorithmic fairness and inclusive design practices for AI systems. Among her notable contributions are editorial work for ACM Transactions on Accessible Computing and co-editing conference proceedings for the ASSETS conference. She has collaborated extensively with leading researchers in the field including Vicki L. Hanson, Gregg Vanderheiden, and Calvin Swart. Dr. Trewin has advised junior researchers including Jessica J. Tran, and her work has influenced both academic research and industry practices in accessibility. Her research has practical implications for web developers, designers, and policy makers working to create more inclusive digital experiences.

• Network and system measurements
• Performance analysis
• Security
• +12 more

Simone Ferlin is an Adjunct Senior Lecturer at Karlstad University working with 5G and Internet evolution. She completed her PhD in computer science in 2017 at the Simula Research Lab and Universitetet i Oslo under the supervision of Dr. Ozgu Alay and Prof. Michael Welzl. Her PhD dissertation focused on increasing robustness in multipath transport with MPTCP. Dr. Ferlin's educational background includes a PhD in Computer Science from the Simula Research Lab and Universitetet i Oslo (2017). Her doctoral research centered on enhancing robustness in multipath transport protocols, specifically focusing on MPTCP (Multipath TCP). She also completed undergraduate work that contributed to a book project with Prof. Friedrich Oehme on electronics and circuit technology. Dr. Ferlin's research spans multiple domains at the intersection of networking, systems, and performance engineering. Her primary interests include network and system measurements, performance analysis, security, and congestion control. She investigates how networks like the Internet evolve, examining technology development, adoption patterns, and their impacts on various entities. Additionally, she explores ways to harmonize security and privacy while making them more usable and assessable. Her work particularly focuses on transport layer and multipath transport protocols, examining their performance and security aspects. She also investigates application and transport layer performance, automation, and monitoring. Her research extends to network programming in both Linux kernel and user space, mobile broadband networks from 2G to 5G, and their intersection with the Internet. She is deeply engaged in observability, distributed and system performance monitoring, and automation. Analysis of Dr. Ferlin's recent publications reveals a strong focus on next-generation networking technologies. Her work spans multiple domains including 5G/6G networks, transport protocols (particularly QUIC and MPTCP), network virtualization, container orchestration, and the application of machine learning to networking problems. She has increasingly incorporated large language models into network configuration and automation research. Her publications demonstrate a consistent emphasis on performance measurement, optimization, and security across diverse networking environments from the edge to the cloud. Dr. Ferlin has received notable recognition for her research contributions: Best paper award at IEEE ICIN'21 for 'Learning-based Incast Performance Inference in Software-Defined Data Centers' Applied Networking Research Prize (ANRP)'25 winner for 'NetConfEval: Can LLMs Facilitate Network Configuration?' Dr. Ferlin is actively involved in mentoring the next generation of networking researchers. She has co-supervised numerous Master's and PhD students across multiple institutions including Karlstad University, KTH, TU Berlin, University of Oslo, and universities in Brazil. Her students have worked on diverse topics including NAT64 performance comparison, system tracing visualization, network observability, ML applications to multipath transport, FEC integration with QUIC, high-performance networking for 5G, congestion control, shared bottleneck detection, multipath IoT applications, and container runtime performance. She is also involved in several significant research projects including Vinnova's SEMLA (Securing Enterprises via Machine-Learning-based Automation), Horizon Europe's CODECO (Cognitive Decentralised Edge Cloud Orchestration), and the Knowledge Foundation of Sweden's DRIVE (Data-driven Latency-Sensitive Mobile Services for a Digitized Society). Dr. Ferlin serves as Workshop Chair for ACM SIGCOMM '25, is a member of the ACM/IRTF Applied Networking Research Workshop (ANRW) steering committee, and co-chairs the Internet Congestion Control Research Group (ICCRG) at the IRTF. She previously served as Associate Technical Editor for IEEE Communications Magazine and has been active on numerous program committees for major networking conferences including SIGCOMM, CoNEXT, IMC, and PAM.

Tim SchwartzView profile

Research Professor

• Human-Robot Interaction
• Multi-modal Interaction
• Industry 4.0
• +3 more

Dr. Tim Schwartz is an Associated Member at the German Research Center for Artificial Intelligence (DFKI) located at the Saarland Informatics Campus in Saarbrücken, Germany. He is affiliated with the Ubiquitous Media Technology Lab (UMTL) where he conducts research at the intersection of human-robot interaction, multimodal interfaces, and industrial applications. His work spans over two decades with significant contributions to the fields of robotics, augmented reality, and Industry 4.0 implementations. Dr. Schwartz's research interests focus primarily on Human-Robot Interaction , Multi-modal Interaction , and Industry 4.0 applications. His work explores how humans and robots can effectively collaborate in industrial settings, with particular attention to communication modalities, task division, and intuitive interfaces. Recent projects include human-robot collaboration in assembly cells, social cognitive robots for warehouse environments, and augmented reality applications for aircraft manufacturing. Analysis of his publication history reveals a clear trajectory from foundational work in multimodal interfaces and context-aware computing (2005-2015) toward increasingly applied research in industrial robotics and human-robot collaboration (2016-2024). His most recent publications demonstrate a strong emphasis on practical implementations in manufacturing and warehouse environments, with a particular focus on optimizing work dynamics between humans and robots. Dr. Schwartz actively engages in academic supervision, offering thesis opportunities such as the user-study on optimal work dynamics in human-robot collaboration at the Power4Production Hall in Saarbrücken. His collaborations extend to institutions including ZeMA (Zentrum für Mechatronik und Automatisierungstechnik gGmbH), indicating strong industry-academia partnerships. At DFKI, he works within a vibrant research community that includes numerous colleagues in the Ubiquitous Media Technology Lab, contributing to a collaborative environment focused on cutting-edge research in human-robot interaction, multimodal systems, and industrial applications of artificial intelligence.