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Professor Marilyn Lennon is a leading academic in the Department of Computer and Information Sciences at the University of Strathclyde , holding the title of Professor in Digital Health and Care. She founded and directs the Digital Health and Wellness Group (DHaWG) , focusing on multidisciplinary research for designing, evaluating, and implementing digital technologies that enhance individual and population health and wellbeing. First class BSc in Psychology (University of Glasgow, 1998) PhD in Computing Science (University of Glasgow, 2002) Postgraduate diploma in academic practice (Fellow of Higher Education Academy) With over two decades of experience in Human-Computer Interaction (HCI) , usability, and user-centered design, her research spans wearable and mobile technologies for home healthcare, including remote monitoring (predictive falls monitoring), remote diagnosis (colon capsule technology evaluations), and smart home solutions for social care. She specializes in addressing technical and social barriers to real-world health technology implementation, emphasizing robust yet rapid evaluation methods. Recent publications highlight her work in 3D healthcare visualization, telehealth implementation, and medication management systems. Her research combines lab-based usability testing with real-world ('in the wild') evaluations of technologies like smart home systems and telehealth solutions. Key trends include cross-disciplinary collaboration, accessibility for sensory-impaired individuals, and AI-driven healthcare innovations. Scientific Awards MyCity: Glasgow - Gamechanger Award (Gold Medal) 2025 Images of Research 2025 Shortlist Emerald Outstanding Paper Award 2013 Best Paper Award (most replicable scientific paper) 2013 As course director for the Masters in Digital Health Systems , she has supervised 5 PhD students (2 completed) and over 100 honors projects. Current projects include the No Need to Fall initiative funded by the Health Foundation (£400k) and the PREMIO project co-creating clinical trials in advanced breast cancer. She maintains active collaborations with NHS, charities, and international institutions like the University of Waterloo.

• Digital Health
• Human-Computer Interaction (HCI)
• User-Centered Design
• +11 more

Arnab Nandi is a Professor in the Department of Computer Science & Engineering at The Ohio State University. His work bridges human interaction with data infrastructure, focusing on database systems, LLM-augmented analytics, and immersive query interfaces. Education: PhD in Computer Science & Engineering from the University of Michigan Leadership: Co-founder of OHI/O Hackathon Program and STEAM Factory interdisciplinary network Research spans human-in-the-loop data analytics , vibe querying (natural language + gestural interfaces), LLM integration into education, and climate response systems . Key projects include Omni (multimodal exploration), GestureDB , and Icarus (clinical pipelines). Recent publications analyze LLM-driven query stacks (HILDA 2025), video analytics (SIGMOD 2022), and data sunglasses for cognitive limits (HILDA 2025). Awards include NSF CAREER Google Faculty Research Award IEEE TCDE Early Career Award ACM Distinguished Member Advises students in database innovation , with alumni at Amazon, AWS, Roblox, and Meta. Teaches CSE 3241 (Database Systems), CSE 5889 (Software Startups), and CSE 5242 (Advanced Databases).

• Database Systems
• Human-in-the-Loop Data Analytics
• Next-Generation Query Interfaces
• +10 more

Anna Jon-And is a Researcher and Director of the Center for Cultural Evolution at Stockholm University , affiliated with the Department of Psychology . Her interdisciplinary work bridges linguistics , cognitive science , and cultural evolution , focusing on how sequence representation , language contact , and computational models explain the emergence of human language and its unique properties. Research Interests include: Language evolution through sequence learning and cognitive constraints Contact-induced language change in Portuguese varieties (Angola, Mozambique, Afro-Brazilian communities) Computational modeling of grammatical structure emergence Comparative analysis of pidgins, creoles, and non-contact languages Neurocognitive prerequisites for language and cultural complexity Publications highlight trends in language evolution models , compositional systems , and cross-linguistic complexity cycles . Her work demonstrates how demographic factors and learnability pressures drive linguistic innovation in multilingual settings. The Center for Cultural Evolution at Stockholm University serves as the primary platform for her interdisciplinary research initiatives.

• Linguistics
• Language Evolution
• Cultural Evolution
• +5 more

Ard Nieuwenhuizen is a Researcher at Wageningen University & Research in the Agro Field Technology Innovations department. His expertise spans Agricultural Engineering , Robotics , and Machine Learning , focusing on AI-driven solutions for crop management and harvesting automation. Agro Field Technology Innovations, Wageningen University & Research Research Interests Robotics and intelligent systems for agricultural operations Digital twin technology and precision agriculture Computer vision for weed and crop detection Human-robot collaboration in farming Machine learning for agricultural automation Publication Trends : His recent work emphasizes agricultural robotics , digital twins , and AI-driven crop monitoring , with applications in apple/pear harvesting, soybean weed detection, and spray optimization. Articles highlight collaborations with European institutions and contributions to journals like Smart Agricultural Technology . Projects : He leads initiatives such as Smart Autonomy for Precision Agriculture (SMP25029) and Redesigning future food production systems through AI and robotics (KB-53-000-010), focusing on integrating robotics and data science into sustainable farming practices.

• Agricultural Engineering
• Robotics
• Computer Science
• +12 more

Luciano Cavalcante Siebert is an Assistant Professor at the Interactive Intelligence Group within the Intelligent Systems department of the EEMCS faculty at Delft University of Technology. His research focuses on Responsible Artificial Intelligence, developing hybrid methods that combine machine learning and symbolic approaches to ensure AI systems remain under meaningful human control within sociotechnical contexts. His educational background includes: B.Sc. in Mechatronics from Pontifical Catholic University of Paraná (PUCPR) M.Sc. and Ph.D. in Electrical Engineering from the Federal University of Paraná (UFPR), Brazil During his Ph.D., he was a guest researcher at Eindhoven University of Technology (TU/e), and prior to his current position, he served as a postdoc at TU Delft's AiTech initiative. Professor Siebert's research program is structured around four pillars: (1) eliciting and aligning with human values and norms, (2) incorporating ethical principles into AI, (3) enabling interactive human control and responsibility mechanisms, and (4) adapting to emergent societal norms. His interdisciplinary work bridges computer science, ethics, and social sciences, with applications in robotics, optimization, and automation. With over eight years of research experience, he develops practical frameworks for human-centered AI that prioritize societal benefit and accountability. Based at TU Delft's campus in Delft, Netherlands, Siebert actively contributes to the Interactive Intelligence Group's mission of advancing intelligent systems that empower humans and enhance societal well-being through responsible innovation.

• Responsible Artificial Intelligence
• Ethical Artificial Intelligence
• Human-AI Interaction
• +4 more

Sergiu Nisioi is an Associate Professor at the Faculty of Mathematics and Computer Science, University of Bucharest, with expertise in computational linguistics, machine translation, and text simplification. He bridges cognitive science with NLP through eye-tracking and EEG research, while also exploring sound art and digital autonomy via initiatives like HYPHA.ro. Current projects include PN-IV-P2-2.1-TE-2023-2007 (text complexity/readability), Legal Document Processing , and Europarl Dialectal Corpora Research spans computational psycholinguistics , LSTM-based translation models , and algorithmic composition for sound art His work integrates interdisciplinary methodologies, combining EEG signal processing for architecture data with the University of Architecture, and DSP for ecological projects at chlorophylla.live.

• Computational Linguistics
• Machine Translation
• Text Simplification
• +8 more

Giorgia Ramponi is an Assistant Professor with Tenure Track at the Faculty of Business, Economics and Informatics at the University of Zurich. She is also an affiliated professor at the ETH AI Center and the Data Science and AI, Computer Science and Engineering department at Chalmers University of Technology. Her educational background includes a Ph.D. in Information Technology from Politecnico di Milano (completed June 2021 with honors), advised by Marcello Restelli, and a Master of Science in Computer Science with Honours Programme (110/110 cum laude) from la Sapienza (July 2017), advised by Flavio Chierichetti and Alessandro Panconesi. Dr. Ramponi's research focuses on machine learning and mathematical modeling, with particular emphasis on reinforcement learning and multiagent learning. Her work bridges theoretical foundations with practical applications, exploring how learning algorithms can make optimal decisions in complex environments. She has made significant contributions to areas including inverse reinforcement learning, multi-agent systems, constrained Markov decision processes, and human-AI interaction through preference learning. Her recent publications demonstrate a strong trend toward addressing fundamental challenges in reinforcement learning, particularly in multi-agent settings, constrained optimization, and learning from human feedback. Her work combines theoretical rigor with practical applications across robotics, economics, and decision-making systems. Hassler Research Grant for "Unified Feedback Integration Framework for Reinforcement Learning" Dr. Ramponi actively contributes to the academic community through conference participation, invited lectures (including at the Mediterranean Machine Learning Summer School), and teaching. She designed and taught the "Data Science and Machine Learning" course for the ETH-Ashesi Master program. She is also a member of the ELLIS community, which connects excellence in AI research across Europe. Her research group focuses on developing frameworks for reinforcement learning with various feedback types, including preferences, rewards, and demonstrations. The group aims to advance the theoretical understanding of learning algorithms while addressing practical challenges in real-world applications.

• Machine Learning
• Mathematical Modelling
• Reinforcement Learning
• +3 more

Allen Hsiao MD, FAAP, FAMIA is Professor of Pediatrics, Biomedical Informatics and Data Science, and Emergency Medicine at Yale School of Medicine. He serves as Chief Health Information Officer (CHIO) for Yale School of Medicine and Yale New Haven Health System, and as Vice Chair of Clinical Systems in Biomedical Informatics & Data Science. BA in Biomedical Ethics and MD from Brown University Pediatrics residency at Yale-New Haven Children's Hospital Fellowships in Pediatric Emergency Medicine and Medical Informatics at Yale His research focuses on leveraging health information technology to improve care delivery, with emphasis on electronic health records, clinical decision support, natural language processing, and AI applications. He actively explores how informatics can optimize systems, improve transitions of care, and advance health equity through diverse clinical trial participation. His work spans pediatric emergency medicine, gastroenterology, child abuse detection, and opioid safety. Dr. Hsiao's recent publications demonstrate leadership in AI-augmented clinical decision support, EHR-based machine learning models, and pandemic response infrastructure. His team has developed innovative tools for child abuse identification, gastrointestinal bleeding risk prediction, and collaborative research data platforms. 56 Hospital and Health System CMIOs and CNIOs to Know (Becker's Healthcare, 2023) Healthcare Diversity Leader Award (National Diversity Council, 2023) 48 CMIOs and CNIOs to Know (Becker's Hospital Review, 2022) Norman J. Siegel Faculty Award (Yale School of Medicine, 2022) As principal investigator and co-investigator on NIH and AHRQ-funded grants, he examines health information technology's impact on healthcare quality. He co-directs Yale's CTSA Informatics Core, working with Yale Center for Clinical Investigations to equip researchers with EHR (Epic) and clinical trials management (OnCore) tools. His leadership extends to national committees including American Academy of Pediatrics, HIMSS, and Children's Hospitals Association.

• Medical Informatics
• Electronic Health Records
• Pediatric Emergency Medicine
• +5 more

Arseny Moskvichev serves as a Research Fellow at the Santa Fe Institute, collaborating with Melanie Mitchell on measuring abstraction and analogy-making capabilities in AI systems. His work bridges Cognitive Science and Machine Learning to model how language and abstraction enable human knowledge sharing, with the goal of developing NLP systems capable of learning through natural dialogue beyond initial training phases. He holds a B.Sc. in Psychology and M.Sc. in Neuroscience from Saint Petersburg University, completed a two-year Machine Learning and Software Development program at the Computer Science Center, and earned an M.Sc. in Statistics and Ph.D. in Cognitive Science from UC Irvine under Mark Steyvers. Moskvichev employs behavioral studies, emergent communication simulations, and novel NLP architecture development to investigate language's role in knowledge transfer. His research specifically targets enabling AI systems to update long-term beliefs via conversation, reflecting his vision for "meaningful" human-AI interaction. He actively promotes mathematical skill development through self-study groups and created a Russian-language Neural Networks course on stepic.org. No scientific awards or current advising activities were documented in the source material. As a core member of the Santa Fe Institute's research community, Moskvichev contributes to interdisciplinary projects at the intersection of cognitive science, artificial intelligence, and complex systems theory, leveraging SFI's collaborative environment for foundational AI research.

• Cognitive Science
• Machine Learning
• Natural Language Processing
• +4 more

Xin Li is a Professor in the Department of Electrical and Computer Engineering at Duke University and serves as the Associate Vice Chancellor at Duke Kunshan University. He holds a Ph.D. from Carnegie Mellon University (2005) and has held leadership roles in research consortia like the FCRP Focus Research Center and the Center for Silicon System Implementation (CSSI). His research bridges integrated circuits , machine learning , and cyber-physical systems , with applications in autonomous driving, battery lifetime prediction, and smart buildings. Education : Ph.D., Carnegie Mellon University (2005); M.S., Fudan University (2001); B.S., Fudan University (1998) His work emphasizes robust design methodologies for analog/RF circuits, data-driven predictive modeling , and Bayesian inference for high-dimensional variation spaces. Recent publications focus on generative adversarial networks for circuit design, multi-view imputation for incomplete data, and knowledge-driven autonomous systems . He has received numerous accolades, including the NSF CAREER Award (2012) , IEEE Donald O. Pederson Best Paper Awards (2013, 2016) , and IEEE Fellow (2017) . He has served as Editor for journals like IEEE Transactions on Biomedical Engineering and as Chair for conferences including ISVLSI and CAD/Graphics.

• Integrated Circuits
• Signal Processing
• Data Analytics
• +4 more

Katy Ilonka Gero is a Lecturer at the School of Computer Science, University of Sydney , with prior postdoctoral roles at Harvard University and the Library Innovation Lab. She holds a PhD in Computer Science from Columbia University (2022) and a BS in Mechanical Engineering from MIT (2017). Education : PhD in Computer Science (Columbia University, 2022); BS in Mechanical Engineering (MIT, 2017) Her research focuses on Human-Computer Interaction in creative domains, particularly how language models impact creative practice, ownership, and learning . She develops community-driven AI models through co-design with creative communities and investigates ethical data governance practices. Recent work includes designing interactive writing tools for metaphor creation and exploring the social dynamics of AI support in writing . The 15 most recent articles reflect trends in AI-augmented creativity (Metaphoria, CHI 2019), AI ethics (Nature Machine Intelligence 2023), and human-AI collaboration (CHI 2020 Best Paper). These works span language model evaluation , creative ownership , and technical innovations in soft robotics (2012) and science communication (2021). Scientific Awards : NSF Graduate Research Fellowship Brown Institute for Media Innovation Grant Amazon Research Award Best Paper - CHI 2025 Honorable Mention - CHI 2024 Best Paper - CHI 2020 As a poet and essayist , she co-edits Ensemble Park , a human-computer co-writing magazine, and authored the dynamic poetry book The Anxiety of Conception (2025). Her grants include support from the National Science Foundation , Brown Institute , and Amazon . For technical details, visit her personal website or GitHub.

• Human-Computer Interaction
• Creativity
• Writing Technologies
• +6 more

S. Scott Graham is an Associate Professor of Rhetoric & Writing and Associate Director for A.I. Science & Culture at the Humanities Institute of the University of Texas at Austin . He is affiliated with the Center for Health Communication , the Addiction Research Institute , the University of Texas Opioid Response Consortium , and the Health Informatics Research Interest Group . PhD, Iowa State University Research focuses on AI and machine learning in bioscience/health policy communication, bioethics, and health AI Author of three books: The Doctor & The Algorithm , The Politics of Pain Medicine , and Where's the Rhetoric? Projects include Transparency to Visibility (T2V) and Spectacular Specimens: A Global Study of Cadaverous Rhetorics at Anatomical Museums His work combines science and technology studies , critical algorithm studies , and public interest informatics to analyze health AI's societal implications. He has developed tools for parsing conflicts of interest in biomedical publishing and studies the ethics of anatomical museum displays. Scientific Awards & Grants: NEH Digital Humanities Advancement Grant (T2V project) NSF's XSEDE computational resources NIH funding for health communication research National Endowment for the Humanities support for Spectacular Specimens He teaches courses like Arguing With Scientists , Rhetoric And Algorithms , and Research Methods In Rhet/Wrt . Current research explores ethical frameworks for health AI and global anatomical museum rhetorics.

• Computational Rhetorics
• Science, Technology & Medicine Studies
• New Materialisms
• +13 more

Philipp Koehn is a Professor in the Department of Computer Science at Johns Hopkins University, with additional affiliation at the University of Edinburgh. His primary research focuses on statistical and neural machine translation, specifically developing methods to leverage large-scale digital information for cross-lingual communication. He leads the Machine Translation Research Group and maintains key resources like the Moses toolkit and Europarl corpus. His research interests span: Core machine translation techniques (statistical/neural approaches) Low-resource and unsupervised translation methods Cross-lingual representation learning Speech-to-speech translation systems Large-scale parallel data mining and alignment Evaluation methodologies for generated text Koehn's recent publications demonstrate strong focus on improving translation efficiency (dynamic compression, streaming models), robustness (noise handling, error correction), and accessibility (low-resource languages, radio speech processing). Key trends include multilingual generalization, document-level coherence, and human-centered evaluation. Significant scientific recognition includes: ACL Fellow (2024) IAMT Award of Honor (2015) European Inventor Award Finalist (2013) He currently advises PhD students Rachel Wicks, Elina Baral, Bismarck Odoom, and Weiting Tan. His Machine Translation Group develops widely-used open-source tools and organizes major conferences including WMT and MT Marathon.

• Machine Translation
• Natural Language Processing
• Artificial Intelligence
• +2 more

Isaac Lage is an Assistant Professor of Computer Science at Colby College since July 2023, specializing in interactive optimization methods for sociotechnical machine learning applications. Their work bridges technical rigor with societal impact, emphasizing accessibility in educational settings and algorithmic accountability. Harvard University PhD in Computer Science (NSF GRFP Fellow) Microsoft Research Intern (Adaptive Systems and Interaction Group) Research Software Engineer at MIT/NYU with David Sontag Research focuses on interpretable machine learning , human-AI collaboration , and healthcare equity analysis . Current projects explore sociotechnical implications of computing systems through EHR data patterns , fairness in predictive models , and user-driven interpretability frameworks . Recent publications show trends in explainable AI (2020-2022), with subfields including clinical decision support , policy summarization , and uncertainty communication . Key themes: healthcare disparities , robust interpretability , and human-in-the-loop learning . Scientific Awards: NSF GRFP Fellowship NeurIPS Spotlight Presentation (2018) AAAI HCOMP Honorable Mention (2019) As a Pedagogy Fellow at Harvard SEAS (2022-23), they contributed to curriculum design for CS 152 and CS 231 at Colby. Also earned a Teaching Certificate from Harvard's Derek Bok Center (Spring 2023).

• Interactive Optimization
• Machine Learning
• Interpretability in Machine Learning
• +5 more

Dr. Christina Haag is a postdoctoral researcher at the Institute for Implementation Science in Health Care , affiliated with the Faculty of Medicine at the University of Zurich . She leads interdisciplinary projects at the intersection of mental health, digital health, and computational linguistics, focusing on chronic illnesses like multiple sclerosis (MS). Her work leverages free text, sensor data, and advanced analysis techniques such as hierarchical modeling and natural language processing (NLP). Doctorate from the Institute of Psychology, University of Zurich Research experience at the MRC Cognition & Brain Sciences Unit, University of Cambridge Her research explores: Daily-life mental and physical health indicators in MS Development of NLP methods for text classification and topic modeling Digital biomarker creation using wearable sensor data Mindfulness interventions for affective executive control Implementation of remote monitoring tools in healthcare Her recent publications highlight trends in applying NLP and machine learning to unstructured health data, analyzing MS activity patterns, and refining interdisciplinary research methodologies. She contributes to DSI communities including AI & Law , Health , and Ethics , and collaborates on projects like BarKA-MS and DSI-Approach . She is a core member of the UZH Digital & Mobile Health Group , working under Prof. Viktor von Wyl.

• Mental Health
• Natural Language Processing
• Activity Sensing Data
• +3 more