David A. Smith is an Associate Professor at the Khoury College of Computer Sciences, Northeastern University. His research focuses on Natural Language Processing (NLP) and computational linguistics, with applications in machine translation, information retrieval, digital humanities, and social sciences. He is a founding member of the NULab for Texts, Maps, and Networks, a research center focused on digital humanities and computational social sciences. Smith's work has been funded by grants from the Mellon Foundation, NEH, and IMLS, supporting projects such as the Viral Texts initiative analyzing 19th-century newspaper networks and the Oceanic Exchanges project tracking transnational information flows. He has contributed to advancements in OCR for historical texts, text reuse detection, and computational analysis of classical languages. He has advised numerous PhD students, including Shijia Liu, Si Wu, and Ryan Muther, and teaches courses like Natural Language Processing and Information Retrieval. His research has been featured in outlets like Wired and the Economist .
Roberto Manduchi is a Professor of Computer Science and Engineering at the University of California, Santa Cruz, within the Baskin School of Engineering. His primary affiliation is with the Computer Science and Engineering department where he leads research in assistive technology for visual impairments. He holds a Dottorato di ricerca in Electrical Engineering from the University of Padova, Italy, and previously worked at Apple and NASA JPL before joining UCSC in 2001. His research focuses on mobile computer vision, inertial sensors, and location-aware systems to enhance spatial awareness and information access for blind and low-vision individuals. Key research areas include indoor navigation systems, screen magnification for low-vision readers, obstacle detection using augmented reality, and text accessibility assessment through specialized OCR pipelines. His work bridges computer vision, human-computer interaction, and accessibility design. Analysis of his recent publications (2022-2025) reveals strong emphasis on inertial-based indoor navigation (e.g., PALMS localization system, backtracking algorithms), screen magnification usability studies, and novel approaches to scene text access for blind users. His research consistently targets practical applications for visual impairment, with significant contributions to pedestrian dead reckoning, magnetic signature localization, and gaze-contingent interfaces. Manduchi serves on the scientific advisory board of Aira and is a board member of the Vista Center for the Blind and Visually Impaired. He leads the UCSC Computer Vision Lab where his team develops accessible computing solutions. His work includes both theoretical contributions to computer vision and tangible assistive applications, with recent projects focusing on smartphone-based inertial odometry, multi-scale tactile maps, and real-time obstacle cueing systems.
Morteza Fayazi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Utah, with an adjunct position in the Kahlert School of Computing. His research focuses on Electronic Design Automation (EDA), applying machine learning to automate analog and mixed-signal circuit design, and developing high-performance computing systems. He holds a B.Sc. from Sharif University of Technology, and M.S.E./Ph.D. degrees from the University of Michigan. His research interests include AI-driven EDA, RF/circuit automation, and energy-efficient processors. Key achievements include the MEDAL lab’s work on terahertz radars, systolic-array processors (e.g., DAP and Versa), and open-source frameworks like FASCINET and Tablext. He has received awards such as the 2024 College of Engineering Dean’s ETR Fund and the 2017 Outstanding Undergraduate Thesis Award. Teaching responsibilities include multiple iterations of the Digital System Design course (ECE/CS 3700). His work spans over 15 peer-reviewed articles in IEEE Transactions, ACM, and top conferences like ICCAD and VLSI-SOC, emphasizing automation, efficiency, and AI integration in hardware design.
Elaine M. Huang is an Associate Professor of Human-Computer Interaction at the Department of Informatics, University of Zurich, where she has served since 2010. She also leads the People and Computing Lab, focusing on the dynamic interplay between human practices and technological advancements. Her academic background includes: PhD in Computer Science, Georgia Institute of Technology (2006) Dr. Huang's research centers on human-computer interaction, examining the bidirectional relationship between technology and human practices. She is particularly known for her work on gender equality in technology, challenging assumptions about innate gender differences and investigating how AI systems may perpetuate biases. Her research also spans sustainable interaction design, mental health technologies, and chronic disease management, always emphasizing empirical data over intuition. Analysis of her recent publications (2023-2025) reveals a strong trajectory toward socially impactful HCI, with significant focus on health technologies (diabetes management, mental health), cultural sensitivity in design, and the ethical challenges of AI. Her methodology often involves field studies to understand real-world technology use, countering the industry's reliance on intuition. As head of the People and Computing Lab, Dr. Huang oversees a research group dedicated to designing and evaluating technologies that address complex human needs. The lab's work frequently involves co-design with diverse user communities to ensure relevance and inclusivity in technological solutions.
Marc Erich Latoschik is a Professor in the Department of Human-Computer Interaction at the University of Würzburg, Germany. He previously held roles at Bayreuth University, Bielefeld University, and FHTW Berlin. His research focuses on virtual and augmented reality, embodiment, human-computer interaction, and applications in health, education, and social systems. Education: Completed his PhD in 2001 at Bielefeld University with a thesis on multimodal interaction in virtual reality. Research Interests: Embodied interaction, virtual embodiment, presence and plausibility in VR/AR, avatar design, social virtual reality, health applications (e.g., VR therapy for body image issues), and XR security/privacy. Active in developing frameworks like Reality Stack I/O and MAIL for VR/AR research. Key Projects: ViTraS study on body image exercises in VR, avatars for mass use via smartphone reconstruction, and motion-based biometrics in XR. Collaborates with medical teams on cybersickness detection and emergency training simulations. Labs/Teams: Leads research groups on immersive technologies and social VR applications. Involved in interdisciplinary projects combining HCI, AI, and healthcare.
Dr. He Wang is an Associate Professor in the Department of Computer Science at University College London (UCL), affiliated with the Virtual Environment and Computer Graphics (VECG) group and the UCL Centre for Artificial Intelligence. He holds a Visiting Professorship at the University of Leeds and previously served as an Associate Professor and Lecturer there, as well as a Senior Research Associate at Disney Research Los Angeles. His research focuses on computer graphics, vision, and machine learning, with notable contributions to crowd simulation, generative models, and physics-informed neural networks. Dr. Wang earned his BEng from Zhejiang University and his PhD from the University of Edinburgh, followed by postdoctoral work at the University of Edinburgh's School of Informatics. He has been recognized as a Turing Fellow and serves as an Academic Advisor to the Commonwealth Scholarship Council and an Associate Editor of Computer Graphics Forum . His research spans cutting-edge topics including 3D reconstruction, adversarial attacks on motion recognition, and AI-driven groundwater modeling. He has supervised six PhD students to completion and actively engages in collaborative projects, consultancy, and grant evaluations. His lab welcomes students through dedicated recruitment channels.
CHI Chunyan is an Associate Professor and Assistant Head (Graduate Programme) in the Department of Chemistry at the National University of Singapore (NUS), within the Faculty of Science. She holds a Ph.D. in Chemistry from the Max-Planck Institute for Polymer Research (2004) and completed a postdoctoral fellowship at the University of California, Santa Barbara (2007). Her research focuses on developing novel π-structured materials, particularly conjugated systems for organic electronics and sensors. She has pioneered studies on carbon nanobelts, aromaticity modulation, and diradicaloid molecules, with breakthroughs in synthesizing fully π-conjugated carbon nanobelts and exploring their electronic properties. Education: Ph.D., Max-Planck Institute for Polymer Research (2004) Postdoctoral Research, University of California, Santa Barbara (2007) Research Interests: Design and synthesis of π-conjugated molecules Organic electronics and sensor materials Aromaticity and diradical character in conjugated systems Novel carbon nanostructures (e.g., carbon nanobelts) Recent Research Highlights: Synthesized the first fully π-conjugated, pentagon-embedded non-alternant carbon nanobelts (2024) Explored global aromaticity in aza-superbenzene derivatives (2024) Developed covalent organic frameworks with radical sites for oxygen reduction reactions (2025) Awards & Recognition: SNIC-AsCA2019 Singapore Award for Distinguished Woman Chemist (2024) NUS Faculty Teaching Excellence Award (2023) Chemical Society of Japan Distinguished Lectureship Award (2017) Asian Core Program Lectureship Awards across multiple countries (2013–2023) Editorial Roles: Associate Editor, Organic Letters (2024–present) Editorial Board Member, Chemistry - A European Journal (2021–present) International Advisory Board Member, Journal of Materials Chemistry C (2017–present) Lab & Group: Laboratory of π-Conjugated Molecules and Materials Recruits postdocs, PhD/Master students, and visiting scholars in organic chemistry, macromolecular chemistry, and materials science Focus on translating molecular design into functional materials for electronics and energy applications
Eliese-Sophia Lincke is a Junior Professor at the Department of History and Cultural Studies, Freie Universität Berlin, since May 2022. Her work bridges computational methods with Egyptology, focusing on digital tools for studying ancient texts. Bachelor's and Master's in Egyptology, Humboldt-Universität zu Berlin (2007) PhD in "The Conception of Spaces in Language" (TOPOI Cluster, 2012) Research interests include: Digital Humanities : Developing machine learning models for Hieroglyphic, Demotic, and Coptic text processing Linguistic Typology : Analyzing classifier systems in Ancient Egyptian and Sign Languages Spatial Linguistics : Investigating prepositions and spatial adverbs in Egyptian-Coptic Recent publications focus on Neural Lemmatization , OCR for Coptic , and Classifier Semantics , demonstrating her commitment to computational Egyptology. Scientific awards include the Humboldt-Preis 2008 for best Master's thesis and the Prize for Good Teaching 2014 . She has co-organized workshops like "Wege zum Ägyptischen" and served as Co-Editor for Lingua Aegyptia . Her teaching contributes to the Digital Studies of Ancient Texts Master's program.
Shahram Rahimi is a Professor and Department Head in the Department of Computer Science at the University of Alabama, College of Engineering. He concurrently holds an Adjunct Professor position at Mississippi State University. His research spans computational intelligence, machine learning, healthcare AI, cybersecurity, and quantum computing. He leads the PATENT Lab, focusing on predictive analytics, decision support systems, and AI-driven healthcare solutions. His educational background includes a Ph.D. in Computer Science. Key research areas include multi-agent systems, generative models, and predictive maintenance. He has served as an editor for journals like Scalable Computing: Practice and Experience and Informatica . Rahimi’s recent work emphasizes secure MLOps, quantum algorithms, and patient-centric medical systems. His publications address challenges in explainable AI, anomaly detection, and healthcare informatics. He actively contributes to conferences and journals in AI, cybersecurity, and computational intelligence. Editorial Roles: Scalable Computing, Engineering Letters, Informatica Labs: Predictive Analytics & Technology Integration (PATENT) Lab Key Focus Areas: Healthcare AI, Quantum Computing, Cybersecurity, Explainable Machine Learning
Bei Wu is Dean’s Professor in Global Health and Vice Dean for Research at the NYU Rory Meyers College of Nursing, where she also serves as Co-Director of the NYU Aging Incubator. She holds an additional appointment as Affiliated Professor in the Ashman Department of Periodontology & Implant Dentistry at NYU, reflecting her interdisciplinary expertise. She previously held the Pauline Gratz Professorship at Duke University School of Nursing, underscoring her national prominence in gerontology and nursing science. Her educational background includes a PhD and MS from the Gerontology Center at the University of Massachusetts, Boston, and a BS from Shanghai University. These foundational qualifications have supported her extensive research career focused on aging, global health, and oral-systemic health linkages. Dr. Wu’s research is centered on gerontology and global health, with a strong emphasis on oral health, dementia, cognitive decline, caregiving, and health disparities among minority populations, particularly older Asian Americans. She is a pioneer in studying the connections between oral health and cognitive outcomes in older adults. Her work integrates interdisciplinary approaches across nursing, dentistry, public health, and social sciences, often with a focus on vulnerable and underserved communities. Her recent publications reveal a consistent focus on the biological and social determinants of cognitive aging, oral frailty, caregiving burden, and mental health in older populations. Themes include the role of toothbrushing in preventing dementia, the impact of social capital on mental health, and the development of culturally tailored interventions for dementia caregivers. Her work often employs mixed methods, including systematic reviews, cohort studies, and qualitative inquiry. Distinguished Scientist Award for Geriatric Oral Research, IADR (2017) Pauline Gratz Professorship, Duke University (2014) J. Morita Junior Investigator Award (2007) Fellow, Gerontological Society of America Fellow, New York Academy of Medicine 2022 Wei Hu Inspiration Award Honorary Member, Sigma Theta Tau International Dr. Wu has successfully mentored hundreds of early-career scientists and has secured substantial funding from the NIH, CDC, and private foundations. She currently leads multiple NIH-funded projects, including a clinical trial on oral health in dementia and a large data analysis on diabetes and cognitive decline. She co-leads the Rutgers-NYU Center for Asian Health Promotion and Equity and directs the Research and Education Core for the NIA-funded Asian Resource Center for Minority Aging Research (RCMAR), which focuses on building research capacity and addressing health inequities. Her work is conducted through key labs and initiatives including the NYU Aging Incubator, the Rutgers-NYU Center for Asian Health Promotion and Equity, and the Asian Resource Center for Minority Aging Research (RCMAR). These centers support interdisciplinary collaboration, innovation in aging research, and the development of culturally appropriate health interventions.
Federico Becattini is a Tenure-Track Assistant Professor at the Department of Information Engineering and Mathematics (DIISM), University of Siena, Italy. He is an active member of the Siena Artificial Intelligence Lab (SAILab), where he contributes to cutting-edge research in computer vision, deep learning, and artificial intelligence. His work spans multiple interdisciplinary domains, including autonomous driving, human behavior understanding, cultural heritage, neuromorphic vision, and fashion recommendation. His research interests center on memory-based neural networks , which he has applied in numerous publications at top-tier venues such as CVPR, ECCV, IEEE TPAMI, and ACM TOMM. He has also delivered tutorials on this topic at international conferences including ICIAP 2022 and ACM MM 2022, and taught a Ph.D. course at the University of Florence. His recent work is aligned with the Collectionless AI paradigm, which emphasizes continual learning and interaction with dynamic environments. The recent publications highlight a strong trend in human-centric AI , focusing on understanding people through multimodal analysis of face, body, and clothing, as well as generating 3D virtual avatars. There is also a clear emphasis on memory-augmented architectures for temporal reasoning, explainability, and adaptive learning. His editorial role as Associate Editor of the International Journal of Multimedia Information Retrieval further underscores his standing in the research community. Associate Editor, International Journal of Multimedia Information Retrieval (IJMIR) Organizer, Workshop on Facial and Body Expressions (ICPR2020) Co-organizer, T-CAP Workshop (ICIAP2021, ICPR2022) Co-organizer, MCFR Workshop (ACM MM 2022) Co-organizer, WCPA Workshop and Challenge (ECCV 2022) Federico Becattini actively advises students and researchers within SAILab, particularly in the context of Ph.D. theses and research projects related to Collectionless AI and memory-based models. While specific grants are not mentioned, his extensive publication record and leadership in workshops and editorial roles suggest involvement in funded research initiatives. He collaborates with both academic and international research communities, serving as a reviewer for top-tier conferences and journals. He is a core member of the SAILab research group, which is pioneering the Collectionless AI initiative—a framework for continual learning over time, interacting with humans and agents without relying on pre-built static datasets. This lab serves as a hub for innovation in adaptive and sustainable AI systems.
Prof. Dr. Harald Ritz serves as Professor of Practical Computer Science, especially Business Informatics, at the Technical University of Central Hesse (THM) within the Department of Mathematics, Natural Sciences and Computer Science since 2003. He holds leadership roles as Chair of Examination Committees for B.Sc. and M.Sc. Business Information Systems and Spokesperson for the MNI department in the Business Informatics Working Group (AKWI). His educational background includes a Diplom in Business Informatics (Dipl.-Wirtsch.-Inform.) and doctorate (Dr. rer. pol.) from the Technical University of Darmstadt, following professional experience at SAP SI AG and a professorship at Heilbronn University of Applied Sciences. Ritz's research centers on AI-driven digital transformation for data-driven enterprises, with focus on the “Data to Decision” value chain encompassing Framing, Allocation, Analytics, and Preparation phases. His work integrates business intelligence, data warehousing, machine learning, and SAP ecosystems to address challenges in SME digitalization, operational IT management, and educational technology. Current projects emphasize AI applications in higher education, including intelligent tutoring systems and automated feedback mechanisms. Analysis of his 15 most recent publications reveals a consistent trajectory toward applied AI solutions in business contexts, particularly in intelligent chatbots for educational support, financial trading algorithms, and cloud-based data infrastructure. The research demonstrates increasing integration of no-code platforms, real-time analytics, and domain-specific AI applications across logistics, banking, and procurement sectors. No scientific awards were documented in the source materials. Professor Ritz actively supervises academic development through bachelor’s and master’s theses, doctoral research, and collaborative projects. Current initiatives include the “Winfy” AI chatbot (v4.0, 2025), AI-based feedback systems for educational content (Freiraum 2025 grant), the frits intelligent tutoring project with Prof. Kammer, and doctoral research on AI adoption in SMEs. His work bridges theoretical research with practical implementation in SAP environments and cloud platforms. He operates within THM’s MNI department infrastructure, collaborating through the Business Informatics Working Group (AKWI) and contributing to the Digital Classroom communication platform for online education.
Paolo Prandoni is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He serves as a Scientist in the Audiovisual Communications Laboratory (LCAV) and teaches in the SSC-ENS and SIN-ENS units, focusing on signal processing theory and practical applications in audiovisual communications. He earned his PhD from EPFL after completing all prior education there, driven by childhood fascination with long-distance telephony. His doctoral work established foundations in communication systems that continue to inform his research. Prandoni's research spans audio/image processing, machine learning for media analysis, and DSP education. Key areas include computational photography (e.g., spectral imaging, stained glass rendering), speech quality assessment via transfer learning, music information retrieval (e.g., fingering prediction), and audience analytics through his company Quividi. His work consistently bridges theoretical signal processing with real-world implementation. Recent publications reveal a strategic shift toward machine learning integration in signal processing tasks, particularly non-intrusive speech assessment and lensless imaging reconstruction. Simultaneously, he advances DSP pedagogy through MOOC development and hands-on teaching tools using off-the-shelf hardware, emphasizing accessibility and practical skill development. No scientific awards are documented in the provided materials. He has advised PhD student Thanikachalam Niranjan (thesis: Image Based Relighting of Cultural Artifacts , 2016) and teaches Communication Systems and Computer Science courses. His educational impact extends through the open-access textbook Signal Processing for Communications (2008) and tools like MultiPub for maintainable online classes. Industry engagement includes Quividi co-founding (2006) and ongoing CSO role in attention analytics. As a core LCAV laboratory member, he collaborates on interdisciplinary projects including cultural heritage digitization, embedded signal processing systems, and real-time audience measurement, leveraging EPFL's infrastructure for both academic and commercial applications.
Stuart E. Middleton is a Professor in the Electronics and Computer Science (ECS) department at the University of Southampton, where he has been employed since 2003. His research bridges artificial intelligence with practical applications in social science, mental health, and security domains. He leads multiple research projects funded by DTP and CISDnS CDT, focusing on multimodal natural language processing and large language models for social good applications. Professor Middleton's research interests center on Natural Language Processing, Large Language Models, and Human-in-the-loop AI systems. His work spans mental health applications (particularly suicide risk detection and mood change analysis), social media analysis for crisis mapping, geoparsing for location extraction, and argument mining in political discourse. He has developed numerous open-source NLP projects and datasets including CPIQA for climate science, ConversationMoC for mental health monitoring, and M-Arg for multimodal argument mining. His research demonstrates how AI can effectively support human decision-making in critical domains like mental healthcare, defense applications, and crisis management. His recent publications reveal a strong trend toward applying LLMs to high-impact societal challenges, particularly in mental health monitoring and climate science verification. He has pioneered methods for detecting suicidal ideation in social media, identifying moments of mood change, and developing context-aware question answering for climate papers. His work consistently emphasizes the importance of human oversight in AI systems, with numerous publications on responsible AI, regulation, and human-in-the-loop approaches. Ranked 1st in ECAL-2024 shared task on suicidal ideation detection Ranked 1st in NAACL-2022 shared task on suicide risk and mood change classification Winner of 'best paper' award at WWW2002 Semantic Web Workshop Professor Middleton actively supervises PhD students through multiple funded projects including 'Multimodal Natural Language Processing for Computational Social Science', 'Large Language Models for Military Veteran Mental Health', and 'Large Language Models for Human/AI Information Foraging to Combat Digital Human Trafficking into Terrorism'. He has secured significant funding from UKRI, DSTL, and other sources to support his research in responsible AI applications. He organizes major workshops including the RAI UK Workshops on Responsible AI for Mental Health and AIUK workshops on AI for Data Rescue and Defense applications. His research group maintains numerous GitHub repositories with open-source NLP tools and datasets that have been widely adopted by the research community.
Prof. Dr. Thomas Stäcker is a Part-time Professor for Digital Humanities at the Department of Information Sciences, University of Applied Sciences Potsdam. He serves as Director of the University and State Library Darmstadt (since 2017) and Deputy Director of the Herzog August Library Wolfenbüttel (since 2009). His career spans roles as a librarian at Herzog August Bibliothek (since 1998) and Johannes a Lasco Library in Emden (1997-1998). Education: Master's degree in History of Philosophy and Latin (1991), doctorate (1994) from TU Braunschweig, University of Essex, and University of Osnabrück Research: Digital Humanities, digitization of early printed books, XML/TEI encoding, OCR technologies, and cultural heritage preservation Leadership: Key roles in major digitization projects (e.g., VD17, Dünnhaupt Digital) and digital library development His work bridges library science with scholarly research, emphasizing open access, digital editions, and metadata standards. He has authored/co-authored numerous monographs, including the 2015 'Grenzen und Möglichkeiten der Digital Humanities' and the 2016 Festschrift chapter on open access. Stäcker co-edited digital editions like Christoph Heidmann's Oratio de Bibliotheca Julia (2013) and maintained active engagement through blogs like 'dhd-blog.org' (2015 post on humanities research data).
