Domenec Puig is a Professor at the Department of Computer Science and Mathematics, Rovira i Virgili University, Spain. His research focuses on machine learning, medical imaging, and computer vision, with applications in healthcare and biomedical informatics. He collaborates extensively with researchers in AI-driven medical diagnostics, including fundus image analysis, MRI segmentation, and neonatal birth weight prediction using multimodal data. Key research areas include deep learning architectures for semantic segmentation, generative adversarial networks (GANs), and interpretable AI models. His work bridges theoretical advancements in neural networks with practical solutions for challenges in medical image analysis, such as crack detection in materials, tumor classification, and survival prediction in oncology. Recent publications highlight contributions to transformer-based models, hybrid architectures (e.g., CoAtUNet), and domain adaptation techniques for cross-site generalization in MRI segmentation. His methodologies often emphasize explainability and efficiency in resource-constrained settings.
Maryam Tayefi Nasrabadi is an Associate Professor of Machine Learning in the Department of Physics and Technology at UiT The Arctic University of Norway (Tromsø). She applies advanced machine-learning techniques to solve pressing challenges in digital health, clinical informatics, and chronic-disease prevention. Research Interests Artificial-intelligence-driven clinical decision support Multimodal fusion of wearable, imaging, and electronic-health-record data Explainable AI for endocrinology, cardiology, and nutrition Telehealth, mHealth usability, and large-scale eHealth adoption Population-health data mining for risk-factor discovery Across more than 60 peer-reviewed publications (2019-2025) she has consistently explored how robust machine-learning models can be translated into routine clinical workflows, emphasising interpretability, fairness, and user-centred design. Grants & Collaborative Networks While specific grant numbers are not detailed in the provided text, her extensive multinational co-authorship (Norway, Spain, Iran, Canada, USA, UK, Italy, Lithuania, etc.) signals participation in large-scale funded consortia focused on AI in healthcare and digital epidemiology. Selected Professional Contributions Member of editorial boards and peer-review panels for leading journals in medical informatics and AI Active contributor to Norwegian national reports on AI implementation in healthcare (2022-2023) Frequent speaker at international conferences on machine learning in medicine
Carolyn P. Rosé is the Kavčić-Moura Professor of Language Technologies and Human-Computer Interaction at Carnegie Mellon University, affiliated with the Language Technologies Institute and the Human-Computer Interaction Institute. Her research focuses on Sociotechnical Artificial Intelligence, blending computational linguistics, sociolinguistics, and learning sciences to develop AI systems that enhance human communication and learning. She earned her Ph.D. in Language and Information Technologies from CMU and has authored over 300 publications across five fields. Rosé oversees the Master of Computational Data Science program and teaches courses like Applied Machine Learning. She is a Fellow of the International Society of the Learning Sciences and an AAAS Leshner Leadership Fellow. Her research interests include neural representation learning, multimodal multi-agent systems, and computational discourse analysis. Recent work explores AI applications in collaborative learning, code review, and healthcare. She leads the Teledia Lab, fostering interdisciplinary projects in AI ethics, explainability, and societal impact. Rosé actively participates in academic leadership roles, including co-chairing EMNLP 2025 and editing special journal issues on AI in collaborative learning. Awards include recognition in four research domains, with contributions to benchmarks like coreference for dialogue and event ordering datasets. She advises numerous students, many of whom have successfully defended dissertations. Rosé’s professional affiliations include IEEE and the Association for Computational Linguistics, reflecting her influence in both computational and educational AI domains.
Devika Subramanian is a Professor of Computer Science and Electrical and Computer Engineering at Rice University. Her research focuses on artificial intelligence, machine learning, and their applications in systems biology, neuroscience, hurricane risk assessment, and conflict prediction. She holds a PhD from Stanford University and has received numerous awards, including the Julia Miles Chance Prize for Excellence in Teaching (2000). Education: PhD in Computer Science, Stanford University (1989) MS in Computer Science, Stanford University (1984) BTech in Computer Science and Electrical Engineering, Indian Institute of Technology Kharagpur (1982) Research Interests: Devika’s work bridges AI with interdisciplinary challenges. Key areas include: Predictive modeling for healthcare (e.g., cardiology, oncology) Machine learning for disaster risk analysis (hurricanes, power grids) Natural language processing and text data analysis Cognitive modeling of learning processes Pharmacovigilance and drug interaction prediction Recent Research Trends: Her articles emphasize AI-driven solutions for public health (e.g., long-COVID prediction, thyroid cancer classification) and environmental systems (e.g., hurricane risk stratification, climate modeling). She also explores cybersecurity and bot detection in social media platforms. Awards and Recognition: 2000: Julia Miles Chance Prize for Excellence in Teaching 2007: Invited Plenary Speaker, International Joint Conference on Artificial Intelligence 2009: Invited Speaker, Heart Failure Society of America Advising and Grants: While specific student advisees are not listed, her labs likely engage students in interdisciplinary AI projects. Her work on hurricane models and drug safety has attracted funding from agencies like Microsoft Research and the National Institutes of Health.
Murali Krishna Emani is an Assistant Computer Scientist in the Data Science group at Argonne Leadership Computing Facility (ALCF) within Argonne National Laboratory. Previously, he served as a Postdoctoral Research Staff Member at Lawrence Livermore National Laboratory. His research spans High Performance Computing , Scalable Machine Learning , and Emerging HPC architectures . Key interests include parallel programming models, runtime systems, and online adaptation for scientific applications. At ALCF, he co-leads the AI Testbed initiative exploring AI accelerator performance for scientific machine learning, and chaired the MLPerf HPC group at MLCommons for benchmarking large-scale ML on HPC systems. His recent publications (2023-2025) reveal strong focus on LLM optimization (MoE inference, KV cache management), AI accelerator benchmarking , and scientific applications (climate modeling, protein design). The work demonstrates cross-cutting themes in hardware-software co-design and performance modeling for emerging architectures. ACM Gordon Bell Prize finalist for climate modeling (2025) Winner of ACM Gordon Bell Special Prize for HPC-based Covid-19 research (2022) Emani actively mentors PhD students and postdocs, with advisees now faculty at Binghamton University, California State University, and researchers at NVIDIA, Microsoft, and national labs. His service includes program committees for SC, IPDPS, and AAAI conferences. Current projects focus on performance modeling for ML/DL frameworks on supercomputers, co-design of hardware architectures for ML algorithms, and benchmarking ML/DL frameworks on HPC systems.
Christopher Gerling, M.Sc., is a Ph.D. candidate at the Chair of Information Systems within the School of Business and Economics at Humboldt University of Berlin. He combines his academic pursuits with a professional role as a data scientist in the banking sector. Education: Master's degree in Information Systems (2021) from Humboldt University of Berlin Research Focus : Specialization in natural language processing (NLP) and multimodal models for banking applications Development of machine learning techniques within big data frameworks Exploration of unstructured data analytics and representation learning (e.g., Company2Vec project) Integration of technical expertise with business analytics in financial domains
Boya Xu is an Assistant Professor of Marketing at the Pamplin College of Business, Virginia Tech. She holds a Ph.D. and MA in Marketing/Economics from Duke University and a BS in Statistics from Zhejiang University, China. Education: BS in Statistics, Zhejiang University (China) MA in Economics, Duke University PhD in Marketing, Duke University Research focuses on: Platform design and digital content strategy Economies of emerging technologies (e.g., green food tech adoption) Algorithmic bias in online recommendations Influencer marketing dynamics including controversial content framing Methods include econometrics, online experiments, and unstructured data analytics. Awards: 2024 ASA Marketing Dissertation Award 2023 NET Institute Grant Advising/grants: Active in doctoral supervision and maintains research grants focused on digital marketing innovation.
Dr. Lan Du is an Associate Professor in the Department of Data Science & AI at Monash University's Faculty of IT. His research focuses on cross-disciplinary applications of machine learning and AI, particularly in text analytics, uncertainty estimation, knowledge distillation, and multi-modal learning. He leads projects addressing real-world challenges in public health, marketing, and clinical decision-making. Key collaborations include work with Victoria Police, Monash Health, and the National Health and Medical Research Council (NHMRC). Education: PhD in Computer Science (ANU, 2012), Bachelor of Information Technology (ANU, 2007), and B.Communication & IT (Flinders University, 2006). Research Interests: Machine/deep learning for NLP, active learning strategies, uncertainty quantification, and AI-driven solutions for healthcare and business analytics. His work bridges theoretical advancements with practical implementation, emphasizing translational research. Recent Projects (2021–2026): Includes AI models for predicting fracture outcomes (NHMRC-funded PRAISE study), risk prediction tools for pregnancy complications, and medical surveillance systems. He also collaborates on business insights derived from unstructured customer data. Teaching Commitment: Served as Chief Examiner and Lecturer for courses like FIT5149 (Applied Data Analysis) and FIT5196 (Data Wrangling). Lab/Team Involvement: Leads initiatives in AI for healthcare analytics and cross-modal learning, with active participation in Monash's research networks.
Dr. Tao Wang is a Research Fellow at King’s College London (KCL), affiliated with the Institute of Psychiatry, Psychology & Neuroscience (IoPPN) and the Department of Biostatistics & Health Informatics. He holds honorary research positions at leading UK hospitals and serves on Meta/Facebook’s Global Safety Policy Advisory Board on Mental Health and King’s AI Institute. His research focuses on advancing Natural Language Processing (NLP), Network Science, Multimodal Data Modeling, and AI applications in healthcare, public health, sociology, and law. He completed his PhD at the University of Southampton (2015–2018) and has contributed to over 45 publications, securing prestigious awards like the Alan Turing Fellowship and the HSJ Partnership Award for Best Mental Health Partnership. Education: PhD in Health Informatics, University of Southampton (2015–2018), supported by the UK ESRC Research Exchange at the University of Western Australia Research Interests: Natural Language Processing (NLP) for healthcare data Knowledge Graphs and Causal Inference in health analytics Clinical Decision Support Systems Multimodal Learning and AI ethics Key Projects: VIEWER: a visual analytics framework for mental healthcare Clinical Decision Support Systems using CogStack Pain analysis via knowledge graph embeddings Awards: Chair’s Prize at NIHR Maudsley BRC conference (2019) Early Career Research Award (2022) HSJ Best Mental Health Partnership Award (2022) HETT Best Data Innovation Award (2022) Labs & Teams: Precision Health Informatics Data Lab (IoPPN), King’s AI Institute.
Melissa Dell is the Andrew E. Furer Professor of Economics at Harvard University's Department of Economics, part of the Harvard Faculty of Arts and Sciences. She holds affiliations with the National Bureau of Economic Research (NBER) as a faculty research fellow and the Canadian Institute for Advanced Research (CIFAR) as a Global Scholar in the Institutions, Organizations, and Growth program. Her research focuses on the interplay between state structures, non-state actors, and economic development, particularly in Latin America and Southeast Asia. Education includes a Ph.D. in Economics from MIT (2012), a Bachelor's degree summa cum laude from Harvard University (2005), and an M.Phil. with Distinction from the University of Oxford (as a Rhodes Scholar). Her research interests span Development Economics, Political Economy, and Economic History, with notable studies on drug violence in Mexico, colonial economic legacies in Peru and Vietnam, and the impact of historical state policies on current economic trajectories. Her work often integrates large-scale historical datasets and computational methods, such as digitizing archives and applying machine learning to analyze economic patterns over time. Recent research trends include developing frameworks for robust inference with unstructured data, historical news analysis, and computational tools for economic history (e.g., EfficientOCR, LinkTransformer). These projects emphasize bridging historical and modern data to understand long-term economic dynamics. Awards and honors include the Rhodes Scholarship and Harvard Society of Fellows Junior Fellowship. Her contributions span academic leadership, interdisciplinary collaborations, and policy-relevant economic analysis.
Sridharan Sridha is a Professor at the University of Queensland, specializing in advanced AI and computer vision research. His work spans neural networks, robotics, medical informatics, and surveillance systems. He collaborates extensively with institutions like the University of Queensland’s School of Information Technology and Electrical Engineering. Key research focuses include adversarial machine learning, multimodal fusion, and domain adaptation. His contributions to aerial-ground person re-identification (AG-ReID), LiDAR-based place recognition, and medical signal analysis have been widely recognized. Recent projects emphasize self-supervised learning, transformer-based architectures for hyperspectral imaging, and autism severity detection using physics-augmented models. His work bridges theory and practical applications in autonomous systems, healthcare, and robotics.
Jason Hartford is a Research Professor and Dame Kathleen Ollerenshaw Fellow specializing in Machine Learning and Robotics. He leads the Research Unit at Valence Labs since April 2023 and holds a Doctor of Science from the University of British Columbia (2021). His research focuses on causal representation learning, causal inference from unstructured data, and deep learning methodologies. Education: Doctor of Science (2021), University of British Columbia, specializing in Architectures and learning algorithms for data-driven decision making. Research Interests: Hartford's work bridges causal inference and machine learning, addressing challenges in representation learning, active learning, and multimodal data integration. He explores how AI can enhance scientific discovery, particularly in biological and medical domains through projects like Virtual Cells and Mendelian randomization applications. Awards: Recipient of the prestigious Dame Kathleen Ollerenshaw Fellowship, highlighting his contributions to causal machine learning and interdisciplinary research. Grants & Projects: Leads the MCAIF: Centre for AI Fundamentals (2021–2026), a collaborative initiative involving over 20 researchers and students, focusing on foundational AI advancements in reinforcement learning, neural networks, and approximation algorithms. Students: Supervises multiple PhD candidates including Hosseinzadeh, Mousa, and Das, fostering innovation in AI-driven scientific exploration.
Dr. Xingjie Wei is an Associate Professor in Business Analytics and Machine Learning at the Centre for Decision Research (CDR), Leeds University Business School, University of Leeds. Her research bridges data science and business management, focusing on understanding human behavior through unstructured data such as images, text, and digital footprints using machine learning and big data analytics. She leads and advises on multiple funded research initiatives and supervises PhD students in related areas. PhD in Computer Science, University of Warwick Research Associate, Psychometrics Centre, Cambridge Judge Business School, University of Cambridge Lecturer, University of Bath Visiting Researcher, National Lab of Pattern Recognition (NLPR), Chinese Academy of Sciences Her research interests include business analytics, unstructured data mining, social computing, human trait analysis, multimodal data, financial risk analytics, and initial coin offerings. She develops algorithms to extract psychological and behavioral insights from digital footprints, with applications in policy, finance, and public services. Her work emphasizes human-centered decision-making and service optimization. The recent publications reflect a strong trend in using machine learning to analyze human traits from visual and textual data, with applications in finance, marketing, and behavioral science. Topics include CEO image analysis, emotion from EEG, facial similarity in films, and tabular data visualization. Her work spans computer vision, affective computing, and behavioral finance, demonstrating interdisciplinary depth. Winner of the 2014 IGI Global's Excellence in Research Journal Award Published in Journal of Corporate Finance , IEEE Transactions on Affective Computing , Annals of Operations Research , and International Marketing Review Active editorial roles in Sensors and Discover Analytics Xingjie Wei has secured and led multiple research grants, including ESRC, Innovate UK, and LUBS Challenge Fund projects. She supervises PhD students and hosts postdoctoral researchers. Her collaborative work includes partnerships with SR Mailing, Katchr, KAIST, and interdisciplinary teams across Leeds. She actively mentors early-career researchers and contributes to academic service through reviewing, conference organization, and public engagement. She is involved in the Centre for Decision Research and leads projects on digital footprints for policy, climate change impacts, and AI-driven decision support. Her team includes current PhD students and research associates working on topics like credit risk, problem gambling, and life quality measurement. She promotes interdisciplinary collaboration and knowledge transfer between academia and industry.
Dr. Ramsey M. Wehbe, MD, MSAI is a dual-appointed Assistant Professor at the Medical University of South Carolina (MUSC) , holding positions in both the Division of Cardiology and the Biomedical Informatics Center . As a physician scientist , he merges clinical cardiology with artificial intelligence to advance cardiovascular care. Education: Duke University (undergraduate), University of North Carolina at Chapel Hill (MD), Northwestern University (internal medicine residency, cardiovascular disease fellowship, and advanced heart failure fellowship) Specialized Training: Master of Science in Artificial Intelligence from Northwestern McCormick School of Engineering Dr. Wehbe's research interests focus on applying deep learning techniques to multi-modal unstructured clinical data including cardiac imaging, electronic health records, and sensor data. His work aims to uncover pathophysiologic insights , enhance diagnostic accuracy , and improve patient outcomes in heart failure management. The Heart AI Lab (HEAL) under his leadership develops innovative approaches to bridge AI and clinical cardiology . His recent publications demonstrate a strong emphasis on large language models for clinical applications, AI-driven cardiac imaging analysis , and predictive modeling for heart failure patients. The research spans multimodal data fusion , electronic health record mining , and clinical decision support systems . While the text doesn't explicitly list scientific awards , it does mention he has received multiple research grants in the field of clinical artificial intelligence applications. As part of MUSC's advanced heart failure program , he works closely with LVAD patients and explores AI-enhanced phenotyping of heart failure syndromes.
Hoifung Poon is General Manager at Microsoft Health Futures and affiliated faculty at University of Washington Medical School. He leads Real-World Evidence (RWE) research focusing on AI applications for precision health. Poon earned a B.S. with Distinction in Computer Science from Sun Yat-Sen University and a Ph.D. in Computer Science and Engineering from University of Washington. Specializes in biomedical AI research Focuses on structuring unstructured medical data Co-PI for DARPA Big Mechanisms projects Research strength lies in biomedical multimodal learning (text, radiology, pathology, genomics) and causal learning for real-world evidence generation. His team develops methods for LLM self-verification , multi-modal fusion , and biases correction in observational data. Publications show expertise in Nature , Nature Methods , and NEJM AI , covering topics from digital pathology to clinical text analysis. Scientific recognition includes: Best Paper Awards at NAACL, EMNLP, and UAI Winner of ACM Health Best Paper Award Named Technology Champion 2022 by Puget Sound Business Journal
