Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.
Thad Starner is a Professor in the College of Computing at Georgia Institute of Technology and Technical Lead/Manager on Google's Glass. He directs the Contextual Computing Group (CCG), co-founded the Animal Computer Interaction Lab, and contributes to Georgia Tech's Ubicomp Group and Brainlab. A wearable computing pioneer since 1993, he has over 500 publications and 80 issued U.S. patents. Coined 'augmented reality' in 1990 Developed CopyCat for ASL learning in deaf children Invented Passive Haptic Learning for skill acquisition His research spans wearable interfaces for Deaf-hearing communication, dolphin interaction systems (CHAT), dog-handler communication (FIDO), and brain-computer interfaces for ALS patients. Current projects focus on optical aging simulation, XR input methods, and animal behavior telemetry. Recent publications (2023-2025) explore AR display ergonomics, AI-augmented reasoning, sign language recognition, and animal-computer interaction. His work has been featured in 60 Minutes, BBC, National Geographic, and Time Magazine. CHI Academy (2017) Lemelson-MIT Prize finalist White House Champions of Change finalist He advises graduate students in wearable systems and teaches AI and prototyping courses. His lab developed the Perceptive Workbench for gesture tracking and created early Eigenfaces research for face recognition.
Susan Sayehli is an Associate Professor at the Department of Swedish Language and Multilingualism , Stockholm University. She specializes in psycholinguistics and second language acquisition, with a focus on crosslinguistic influence and morphosyntactic processing. Research Interests: Neurocognitive aspects of language learning Focus intonation development in Swedish children Policy impact on SFL (Spanish/French/German) education ERP and eye-tracking methodologies for language processing Urban-rural educational disparities in language programs Current Projects: TAL – Alignment study on oral proficiency in Swedish schools Att lära sig fokusera – Intonation perception in Stockholm/Skåne children Prior Projects: SWOP2 – Swedish word order processing in L2 learners PSUII – Precursors of sign use in intersubjectivity
Stephen Alstrup is a Professor in the Algorithms and Complexity section at the Department of Computer Science (DIKU), University of Copenhagen, Faculty of Science. His research bridges theoretical computer science with practical applications in modern computational challenges. His primary research interests include: Algorithm design and analysis Graph algorithms and data structures Big Data processing techniques Streaming algorithms and Internet distribution Theoretical foundations with practical implementations Alstrup's work demonstrates how theoretical algorithm research can lead to real-world applications, as evidenced by his development of Octoshape technology for large-scale Internet streaming. His research spans from fundamental theoretical problems to applications in Big Data, cloud computing, and information retrieval systems. He has published extensively with 93 research outputs including journal articles, conference proceedings, and books. His recent work focuses on graph spanners, semantic hashing, recommendation systems, and universal graph structures, showing continued productivity in theoretical computer science. Alstrup actively engages with industry and media, contributing to discussions about Big Data applications, technology innovation, and how businesses can collaborate with universities to access cutting-edge knowledge and funding opportunities. His work has been featured in 10 media contributions discussing practical applications of algorithms in education, municipal IT projects, and business innovation.
Pamela Beach is the Associate Dean, Research and Associate Professor of Language and Literacy at Queen’s University’s Faculty of Education. She holds an MA and PhD in Child Study/Developmental Psychology from the University of Toronto. With nearly a decade of elementary teaching experience, her research focuses on leveraging online and multimedia resources to enhance teacher education and professional development. Key projects include SSHRC-funded studies on teachers’ self-directed online learning, analysis of web analytics for literacy PD platforms, and cross-cultural studies of literacy practices in Montessori and Reggio Emilia schools. Her expertise spans early literacy development, critical literacy integration, and the use of technologies like eye-tracking and virtual classrooms in teacher training. She leads the Literacy Education Research Team, collaborating on projects such as integrating music into literacy education and evaluating virtual classroom tools for teacher candidates. Beach is also the author of Promoting Language and Early Literacy Development (2024), a practical guide for educators and caregivers. Current roles include supervising graduate students in the Language and Literacy program and overseeing research initiatives as Associate Dean. Her work bridges research, practice, and policy to improve literacy instruction and teacher learning in diverse educational contexts.
Chun Ouyang is a Professor at Queensland University of Technology (QUT) in the School of Computer Science within the Faculty of Science. With an extensive publication record spanning over two decades from 2002 to 2025, Professor Ouyang has established themselves as a leading researcher in Business Process Management, Process Mining, and Explainable AI. Their work bridges theoretical foundations with practical applications across healthcare, finance, and industrial sectors. Professor Ouyang's research interests primarily focus on Business Process Management systems, Process Mining techniques, Explainable Artificial Intelligence, and Healthcare Process Analysis. Their work has evolved from foundational BPMN/BPEL translation research in the early 2000s to sophisticated process mining approaches in the 2010s, and most recently to cutting-edge Explainable AI applications in clinical and business contexts. They have developed novel methodologies for process querying, predictive process analytics, and XAI evaluation frameworks that have significantly advanced the field. Their research consistently emphasizes practical applicability while maintaining strong theoretical foundations, with publications in top-tier journals and conferences including IEEE Transactions, Springer journals, and major BPM conferences. Analysis of Professor Ouyang's recent publications (2023-2025) reveals a strategic research trajectory that integrates traditional process mining with modern AI techniques, particularly focusing on explainability and trustworthiness. Their work demonstrates a consistent pattern of addressing real-world challenges through rigorous methodological development, with increasing emphasis on healthcare applications, clinical decision support systems, and the ethical implications of AI deployment. The publications show strong interdisciplinary collaboration patterns, particularly with medical researchers and industry partners. Professor Ouyang has mentored numerous PhD students and early-career researchers who have gone on to establish themselves in the BPM and AI communities. Their research group at QUT has secured multiple competitive grants supporting innovative work in process analytics and AI. They maintain active collaborations with leading researchers globally, including Catarina Pinto Moreira, Arthur ter Hofstede, and Moe Wynn. Professor Ouyang leads the Process Analytics Research Group at QUT, which focuses on developing advanced techniques for business process analysis, prediction, and optimization. The group maintains strong industry connections with healthcare providers, financial institutions, and government agencies, ensuring their research has practical impact. Current projects include developing trustworthy AI systems for clinical decision support, cross-organizational process analysis frameworks, and next-generation process mining techniques for complex, distributed systems.
Prof. Dr. Enkelejda Kasneci is a Distinguished Professor at the Technical University of Munich (TUM), leading the Chair of Human-Centered Technologies for Learning. She holds dual affiliations within TUM School of Social Sciences and Technology and TUM School of Computation, Information and Technology. Her research integrates AI, eye-tracking, and immersive technologies to advance educational paradigms. She directs the TUM Center for Educational Technologies and chairs the MSc program 'AI in Society.' Education: PhD in Computer Science from University of Tübingen (2013), M.Sc. from University of Stuttgart (2007). Earlier roles include Assistant Professor and Dean of Studies at University of Tübingen. Research Focus: Human-centered AI applications in education, multimodal interaction design, and privacy-preserving eye-tracking. Her work bridges technology and pedagogy through projects like AI tutor PEER, VR Classroom, and Privacy-Preserving Eye-tracking. Key Projects: Leads EU-funded projects VIVA (€1.125M), DigiProMIN (€163K), and SARA Kids (€244.8K). Active in policy initiatives like Europe’s AI Imperative. Awards: TUM Heinz Maier-Leibnitz Medal (2024), Liesel Beckmann Distinguished Professorship (2022), and Südwestmetall Research Prize (2014). Grants & Advising: Over €5M in secured funding across 12+ projects. Supervises 14+ PhD researchers and mentors postdocs in AI education and HCI. Labs & Teams: IT-Stiftung EdTech Lab houses advanced VR/eye-tracking setups. Research group includes 20+ members spanning AI, HCI, and educational technology.
Kede Ma is an Associate Professor in the Department of Computer Science at City University of Hong Kong (CityUHK). He received his B.E. from the University of Science and Technology of China (USTC) in 2012, and MASc and Ph.D. degrees from the University of Waterloo in 2014 and 2017, respectively. From 2018 to 2019, he was a Research Associate with the Howard Hughes Medical Institute and New York University. Prof. Ma has been named to the Highly Cited Researchers list by Clarivate Analytics in 2024 and currently serves on the editorial boards of IEEE Transactions on Image Processing, IEEE Transactions on Information Forensics and Security, and IEEE Signal Processing Letters. Prof. Ma leads the Multimedia Analytics (MA) Laboratory, an interdisciplinary research group focused on computational vision, computational modeling of human visual perception, perceptual multimedia signal processing, quality assessment, and multimedia forensics. His research spans computational photography, high dynamic range imaging and rendering, omnidirectional video analysis, camera processing pipeline design, and artificial intelligence safety in multimedia systems. His work integrates machine learning techniques including reinforcement learning, generative modeling, self-supervised learning, and continual learning for multimedia signal processing applications. His recent publications demonstrate a strong focus on image quality assessment, deep learning for multimedia processing, and multimedia forensics. His work bridges theoretical computer vision principles with practical applications in multimedia systems. The research trends show increasing integration of foundation models with specialized multimedia processing tasks, particularly in quality assessment and security applications. Highly Cited Researchers list by Clarivate Analytics (2024) Best Paper Award at IEEE International Conference on Virtual Reality and Visualization (2021) Best Paper Runner-Up at International Joint Conference on Artificial Intelligence Workshop (2021) Top 10% Award at IEEE International Conference on Image Processing (2015) Finalist for the Governor General's Gold Medal, University of Waterloo (2017) Spotlight presentation at NeurIPS (2022) Highlight paper at ICCV (2025) Oral presentation at ICLR (2025) Prof. Ma advises numerous PhD students and postdoctoral fellows in the MA Laboratory. His research is supported by various grants enabling work in multimedia analytics, image processing, and computer vision. The laboratory maintains active collaborations with researchers at institutions including SUSTech, ZJU, and HIT. Current projects focus on advancing image quality assessment methodologies, developing more robust deep learning techniques for multimedia forensics, and exploring new approaches to HDR imaging and omnidirectional video processing. The Multimedia Analytics Laboratory maintains a strong focus on both theoretical foundations and practical applications of multimedia processing. Current research directions include integrating large language models with image quality assessment, developing more robust deepfake detection methods, and advancing techniques for continual learning in multimedia applications. The lab emphasizes rigorous evaluation methodologies and maintains multiple datasets for multimedia quality assessment research.
Lisa Beinborn is a Professor for Human-Centered Data Science at the University of Göttingen, leading the Human-Centered Data Science group. Her research bridges natural language processing with cognitive science, focusing on multilingual models and interpretability. PhD in Computer Science (2016), Technische Universität Darmstadt MSc in Computational Linguistics (2010), Saarland University & Bolzano, Italy BSc in Computational Linguistics (2008), Saarland University & Barcelona, Spain Her research explores cognitive plausibility in NLP, analyzing how language models process language differently from humans. Key areas include multilingual model interpretability, semantic drift, eye-tracking, and readability prediction. Recent work examines input representation stability in neural models, cross-lingual transfer of complexity, and aligning language models with human cognitive patterns. Her team has presented findings at EMNLP, CoNLL, ACL, and CoLING. VENI Grant for "Interpretability of Transfer in Multilingual Models" Early Career Partnership by Royal Dutch Academy of Science "Most Interesting Paper" Award at BabyLM Challenge "Best Project Award" by Network Institute She has taught courses like Language as Data and Advanced NLP at University of Göttingen, VU Amsterdam, and TU Darmstadt. Her group collaborates with institutions like Gemeente Amsterdam and NT2 on multilingual text simplification and learner correction.
Radu Timofte is an academic researcher specializing in computer vision and image processing. He completed his PhD in 2013 at Katholieke Universiteit Leuven, Belgium, with a thesis on sparse and collaborative representations for computer vision. His work focuses on advancing techniques such as image super-resolution, denoising, object detection, and deep learning-based image restoration. He has collaborated extensively with institutions like ETH Zurich and co-authored seminal papers in top-tier journals and conferences. His research bridges theoretical advancements with practical applications in areas like medical imaging, aerial scene analysis, and real-time visual tracking. Timofte’s contributions include developing efficient deep learning architectures for tasks like lightweight object detection (e.g., CH-YOLO-Lite), diffusion models for image-to-image translation (DiffI2I), and calibration-free raw image denoising. He has also contributed to the development of video restoration transformers (VRT) and frameworks for unsupervised real-time video enhancement. His work often emphasizes practicality and efficiency, addressing challenges such as small object detection in aerial imagery and underwater image super-resolution. Timofte’s collaborations span academia and industry, with notable co-authors including Luc Van Gool (ETH Zurich) and Kai Zhang (Nanjing University of Science and Technology). His research has been published in venues like IEEE Transactions on Pattern Analysis and Machine Intelligence, CVPR, ECCV, and the International Journal of Computer Vision.
Schea N Fissel, Ph.D., CCC-SLP, is an Associate Professor in the Department of Speech-Language Pathology at Midwestern University's College of Health Sciences, Glendale campus. She holds a Ph.D. from Kent State University (2018), an M.A. (2008), and B.S. (2006) from Western Michigan University. Her educational background includes: Ph.D., Kent State University, 2018 M.A., Western Michigan University, 2008 B.S., Western Michigan University, 2006 Dr. Fissel's research examines how attentional resources are dynamically recruited by complex visual stimuli like human faces and biological motion, with emphasis on neurodiverse populations. She investigates automatic attention processes in social communication and how these differ in autism spectrum disorder, focusing on autism, attention, and literacy dynamics. Her work applies complexity theory to understand information processing in social contexts. Her publications (2019-2025) reveal strong thematic continuity in autism research, attention mechanisms, and clinical education. Key trends include music's impact on attention, evidence-based practice critiques in autism services, second-language processing interactions, and reading comprehension in traumatic brain injury. The work consistently bridges cognitive theory with speech-language pathology applications. Dr. Fissel teaches core graduate courses including Child Language and Learning II/III (SLPPG 522/533) and Communication Disorders in Autism (SLPPG 623), alongside capstone sequences (SLPPG 505/506/606/607) and thesis supervision (SLPPG 670). Her teaching portfolio demonstrates active mentorship through evidence-based practice instruction and clinical research guidance, preparing students for professional practice in speech-language pathology.
Yu Huang is an Assistant Professor of Computer Science at Vanderbilt University with a secondary appointment in the Department of Teaching and Learning at the Peabody School of Education. She is affiliated with the Institute for Software Integrated Systems, the Frist Center for Autism and Innovation, the Vanderbilt Lab for Immersive AI Translation (VALIANT), and the Vanderbilt LIVE Learning Innovation Incubator. Her research focuses on human-centered AI for software engineering, combining human cognition with machine intelligence to enhance software development processes. Educated at the University of Michigan (PhD, 2021), University of Virginia (MS, 2015), and Harbin Institute of Technology (BS, 2011), her work spans software engineering, human factors, AI, and medical imaging. Key projects include the MIND Lab, studying programmer expertise and cognitive processes, and the HumanAISE workshop on Human-Centered AI for Software Engineering. Huang has received significant recognition, including the 2025 ICPC Vaclav Rajlich Early Career Achievement Award and three ACM SIGSOFT Distinguished Paper Awards. Her research is supported by NSF, GitHub, and Vanderbilt initiatives. She advises numerous graduate and undergraduate students, emphasizing diversity and innovation in programming education.
Zhu-Tian Chen is an Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities, where he leads research in data visualization, human-computer interaction, and augmented reality. Prior to this, he held postdoctoral positions at Harvard University and UC San Diego, working with leading researchers in visual computing and interactive design. Ph.D. in Computer Science, Hong Kong University of Science and Technology B.Eng. in Software Engineering, South China University of Technology His research focuses on augmenting human intelligence through hybrid human-AI systems, particularly in everyday and outdoor environments. He specializes in designing intelligent AR interfaces, embedded visualizations, and language-oriented interactions for applications in sports analytics, education, and data analysis. His work integrates human-centered design with applied machine learning to create intuitive and effective visualization tools. The recent trend in his publications shows a strong emphasis on intelligent AR systems for dynamic scenes, LLM-based code generation interfaces, and real-time augmentation of sports videos using natural language and gaze-based interactions. His work frequently appears in top-tier venues such as IEEE VIS, ACM CHI, and UIST. Best Paper Award, ACM CHI'23 Best Short Paper Honorable Mention, EuroVis'23 Best Paper Honorable Mention, IEEE VIS'22 (twice) Certificate of Distinction and Excellence in Teaching, Harvard University Hong Kong Ph.D. Fellowship Dr. Chen actively mentors undergraduate, master’s, and PhD students, as well as visiting scholars and interns, and is building a new research lab focused on visualization for intelligent AR systems. He has served on program committees for major conferences including ACM CHI, IEEE VIS, and EuroVis, and has been invited to speak at institutions such as Apple, JP Morgan, and multiple universities worldwide. He also contributes to the academic community through grant reviewing for NSF and the Department of Energy. He leads research projects in intelligent AR systems for sports, language-oriented interactions with LLMs, and immersive data visualization, often in collaboration with institutions like Harvard, UC San Diego, and HKUST. His lab welcomes students and collaborators interested in visualization, HCI, and applied AI.
Julien Diogo serves as Adjunct Professor at Polytechnic Institute of Viseu's School of Education of Viseu since 2020, teaching Market Analysis, Consumer Behavior, Strategic Communication, and Innovation/Creativity courses. He concurrently holds Visiting Professor positions at ISAG (Higher Institute of Administration and Management) since 2018 for Executive MBA programs and was Professor of Organizational Communication at ISCA-UA (2022-2023). His academic roles extend to Visiting Facilitator positions at Brazil's Personal Branding Academy and ISLA's Postgraduate Program in Innovation. His educational background includes: PhD in Communication Sciences (in progress, 2023-2026) at University of Coimbra Specialization in Teacher and Trainer Training (2022-2023) from Employment and Training Institute of Braga Marketing Specialist title (2019) from Polytechnic Institute of Viseu Master's in Communication and Marketing (2010-2012) from Polytechnic Institute of Viseu (Final Grade: 17/20) Bachelor's in Social Communication (2005-2008) from Polytechnic Institute of Viseu (Grade: 17/20) Diogo's research integrates neuromarketing with consumer behavior analysis, focusing on emotional responses in digital environments, Generation Z consumption patterns, and neuroscience applications in place branding. His work examines how cognitive processes influence purchasing decisions through physiological measurements and behavioral experiments, particularly investigating caffeine's neuropharmacological effects on consumer arousal and shop window design's attentional impact. He bridges theoretical neuroscience with practical marketing strategy development. His publication trajectory reveals increasing focus on digital-emotional consumer interfaces, with recent work analyzing pandemic-era behavior shifts and Gen Z's narrative processing. Key thematic clusters include neuromarketing validation in retail architecture, emotional sustainability in e-marketplaces, and neuroscientific foundations of territorial branding, demonstrating consistent application of cognitive neuroscience to contemporary marketing challenges. Scientific recognition includes: 2008 Academic Merit Award for Best Social Communication Student (Polytechnic Institute of Viseu) 2021 Nomination for Global Teacher Prize Portugal Diogo actively supervises master's research including thesis on territorial brand communication (2024), language informality in digital contexts (2024), and Apple's Lovemarks strategy for Generation Z (2024). He contributes to research projects like INOV C+ Intelligent Innovation Ecosystem (2024-present) and co-orientated neuromarketing studies on advertising reception decoding (2020). His academic service includes peer review for IGI Global and International Journal of Marketing. Through his dual leadership as CCO of ICN Agency (neuromarketing consultancy), co-director of PsicoSoma (publishing/training), and expertMind (LMS platform), Diogo maintains robust industry-academia integration, developing neuromarketing frameworks applied across retail, urban planning, and digital experience design contexts.
John M. Henderson is a Distinguished Professor at the University of California, Davis , affiliated with the Visual Cognition Lab . He holds additional roles at the Center for Mind and Brain , Center for Vision Science , Center for Neuroscience , and Plasticity and Memory Program . As an editor for Collabra: Psychology and associate editor for Journal of Experimental Psychology: General , he contributes to open science and cognitive research dissemination. Ph.D. in Cognitive Psychology, University of Massachusetts, Amherst (1988) M.S. in Cognitive Psychology, University of Massachusetts, Amherst (1986) B.S. in Psychology, University of Massachusetts, Amherst (1983) Professor Henderson’s research investigates how visual information is acquired, recognized, and integrated into cognitive systems to guide behavior. His work combines scene perception , reading processes , and visual memory using eye tracking , fMRI , brain stimulation , and computational modeling . Recent studies explore semantic guidance of attention in natural scenes, neural correlates of fixation duration, and developmental attentional patterns. His 15 most recent publications (2023-2025) reflect a focus on semantic processing in visual cognition , scene perception , and computational modeling of attention . Topics include meaning-based attentional guidance, deep learning applications in scene analysis, and neural mechanisms of memory-guided eye movements. Collaborations span cognitive neuroscience, developmental psychology, and AI-driven scene understanding. Scientific honors include: Google Scholar Classics recognition (2017) for groundbreaking 2006 paper Fellow of the Association for Psychological Science, American Psychological Association, and Psychonomic Society Grants from the National Eye Institute and National Institute on Aging support his work on visual cognition and aging. His lab trains students in cognitive methods and interdisciplinary research, bridging psychology, neuroscience, and computational modeling.
