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Karin VerspoorView profile

Professor

• Artificial Intelligence in Medicine
• Biomedical Natural Language Processing
• Health Informatics
• +3 more

Professor Karin Verspoor is the Dean of the School of Computing Technologies at RMIT University in Melbourne, Australia. She previously held roles as Director of Health Technologies and Deputy Head of the School of Computing and Information Systems at the University of Melbourne, and as Scientific Director of Health and Life Sciences at NICTA's Victoria Research Laboratory. Her research focuses on applying artificial intelligence methods to biomedical discovery and clinical decision support, particularly through natural language processing of clinical texts and biomedical literature. Affiliations: RMIT University (STEM College), Australian Alliance for Artificial Intelligence in Health (Victorian Node Lead) Industry Experience: Intelligenesis/Webmind Corp., Applied Semantics, Los Alamos National Laboratory, National ICT Australia Research Interests: Artificial Intelligence in Medicine Biomedical Natural Language Processing Health Informatics Computational Biology Cheminformatics Her work emphasizes cross-modal data integration, EHR analytics, and AI-driven clinical tools to address challenges in healthcare outcomes, musculoskeletal disorders, and infectious disease surveillance. Advising & Grants: Supervises research on AI-based decision-making frameworks, EHR data quality, and chemical knowledge extraction. Leads projects funded by initiatives like CANAIRI (Collaboration for Translational AI in Healthcare). Labs & Collaborations: Co-founder of the Australian Alliance for AI in Health, advancing national AI healthcare policy and translational research.

Noura LimamView profile

Assistant Professor

• Network Management
• SDN/NFV
• Network Security
• +3 more

Noura Limam is a Research Assistant Professor at the University of Waterloo's Cheriton School of Computer Science. Her research spans network operations, with emphases on software-defined networking (SDN), 5G/6G architectures, network security, and autonomous network management. Recent work focuses on AI-driven solutions for encrypted traffic analysis, network slicing security, and satellite communication systems. She develops frameworks like Monarch for network slice monitoring and 5Guard for secure slicing. Contributions include blockchain-assisted authentication protocols, meta-reinforcement learning for threat mitigation, and novel handover mechanisms for non-terrestrial networks. Her publications demonstrate consistent innovation in making networks more adaptive, secure, and efficient.

• Digital Marketing
• Social Media/Influencer Marketing
• Omnichannel/Multichannel Retailing
• +10 more

Jeeyeon Kim is a Lecturer in the Department of Management and Marketing at La Trobe Business School, La Trobe University. Prior to this, she served as an Assistant Professor at National Sun Yat-sen University in Taiwan. Her research focuses on digital marketing, omnichannel retailing, social media/influencer marketing, and digital healthcare. She holds a PhD and MA from Yonsei University, South Korea, and has held visiting roles at Yonsei University and IE University. Her work emphasizes empirical data analysis and statistical modeling to address marketing challenges. Jeeyeon has secured multiple research grants, including AUD 82,500 from Taiwan's Ministry of Science and Technology (MOST) for projects on big data-driven marketing and digital transformation. She also coordinates grants with Yonsei University and AACSB initiatives. Her editorial roles include serving on the Asia Marketing Journal and Korean Scholars of Marketing Science boards. Her research spans topics such as virtual influencers' impact on social media, omnichannel strategies, and healthcare consumer behavior. Teaching awards include the 'Outstanding Course Teaching Award' and 'Excellent Mentor Award.' She actively reviews for journals like the Asian Pacific Journal of Marketing and Logistics and conferences like the Global Fashion Management Conference.

Nicholas VincentView profile

Assistant Professor

• Human-Computer Interaction
• Responsible Artificial Intelligence
• Human-Centric Machine Learning
• +3 more

Nicholas Vincent is an Assistant Professor in the School of Computing Science at Simon Fraser University. His research focuses on Human-Computer Interaction (HCI), Responsible Artificial Intelligence, and Human-Centric Machine Learning. He holds a Ph.D. in Technology and Social Behavior from Northwestern University (2022) and a B.Sc. in Electrical Engineering from UCLA (2016). Education: Ph.D., Northwestern University (2022); B.Sc., UCLA (2016) Affiliations: Simon Fraser University School of Computing Science His work examines the societal implications of AI, including data labor rights, algorithmic governance, and ethical design. Notable contributions include frameworks for measuring attentional agency on digital platforms and studies on collective action in tech contexts. He received the SIGCHI Outstanding Dissertation Award in 2024. Recent publications explore topics like AI ethics, governance of generative models, and Wikipedia's role in content ecosystems. He actively participates in policy discussions through media engagements and conference talks, advocating for public-oriented AI development.

• Statistical Modeling
• Biostatistics
• Biomedicine
• +7 more

Dae-Jin Lee is an Assistant Professor at IE University’s School of Science and Technology, specializing in statistical modeling and data science. Previously, he served as a Research Line Leader at the Basque Centre for Applied Mathematics (BCAM) and coordinated the Knowledge Transfer Unit in Data Science/AI. His academic background includes a Ph.D. in Mathematical Engineering (2010) from Universidad Carlos III de Madrid and postdoctoral research at CSIRO (Australia). His research focuses on statistical methods for complex data, including penalized splines, tensor product smooths, and applications in biomedicine, epidemiology, environmental science, and sports analytics. He has led multidisciplinary projects funded by public and industry grants, collaborating globally with experts across fields like engineering, medicine, and biology. Key research themes include predictive modeling for health outcomes (e.g., SARS-CoV-2 pneumonia severity), sports injury prevention, and AI in healthcare. His work integrates machine learning with traditional statistical techniques, addressing real-world challenges like pedestrian dynamics simulations and automated medical diagnostics. He is actively involved in scientific organizations, including the Spanish Biostatistics Society and the Statistical Modelling Society. His recent publications highlight innovations in growth curve modeling, AI ethics, and spatiotemporal data analysis, reflecting his commitment to advancing both theoretical and applied statistics.

Jimmy HuangView profile

Professor

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

Jimmy Huang is a Full Professor and Tier 1 York Research Chair in Big Data Analytics at the School of Information Technology, York University. His research focuses on information retrieval, AI, NLP, and big data analytics in healthcare and web systems. He has published 360+ papers in top venues like SIGIR and ACL, and leads grants totaling $4M+. Huang chairs IEEE's Technical Community on Intelligent Informatics and serves on numerous conference committees. Education: PhD in Information Science (City, University of London), M.Eng and B.Eng in Computer Science Roles: Chair of IEEE TCII, General Chair of SIGIR 2020 and CIKM 2008 Research interests span task-oriented IR, conversational search, healthcare analytics, and graph-based models. His work on hypergraph collaborative filtering (SIGIR 2022) was named a top influential paper. Current projects include NSERC Discovery Grants ($384K) and ADERSIM CREATE ($1.65M). Award highlights include Fellowships from ACM, IEEE, and Canadian Academy of Engineering. Supervised over 90 students, currently mentoring 12 PhD/MSc candidates and 3 postdocs. Active in surgical safety checklist research and medical data analytics. Labs include the IRLab focused on IR and NLP innovations. Major grants include ORF-RE ($3.5M), NSERC CREATE, and multiple CRD partnerships with industry.

• Water quality monitoring and modeling
• Data-driven prediction modeling
• Bayesian Network modeling
• +5 more

Dr. Edoardo Bertone is a Senior Lecturer at Griffith University's School of Engineering and Built Environment - Architecture and Design. He holds a PhD in Water Resources Engineering from Griffith University and Bachelor/Master degrees in Civil Engineering from the Polytechnic University of Turin. His research focuses on data-driven modeling, Bayesian Networks, and System Dynamics applied to water resources management, climate change adaptation, and the water-energy nexus. He is affiliated with Griffith's Cities Research Institute and Australian Rivers Institute, collaborating on projects with water utilities, governments, and private entities. Dr. Bertone has received awards such as the 2024 PVC Science Excellence in Teaching and the JSPS Fellowship (2022). He supervises doctoral and master's students in areas like water quality management and climate change impacts. Education: PhD in Engineering (Griffith University, 2015); MEng and BEng in Civil Engineering (Polytechnic University of Turin, 2009-2011). Research Interests: Water quality modeling, drinking water optimization, data-driven prediction, climate change adaptation, and sustainable development goals. He leads over 25 funded research projects, including initiatives on reservoir water quality management in Thailand, real-time nutrient monitoring, and cyanobacteria bloom modeling. Dr. Bertone’s work integrates advanced sensors, machine learning, and Bayesian networks to address environmental challenges. Awards: Listing includes PVC Excellence Awards (2024, 2017), JSPS Fellowship, and recognition as a Rising Star in Queensland Science (2015). Grants & Supervision: Principal supervisor for 10+ doctoral candidates and collaborator on projects funded by Seqwater, CSIRO, and the Ian Potter Foundation. Key grants include $745k for biofertilizer combatting eutrophication and $269k for coagulation optimization models. Dr. Bertone’s contributions extend to urban sustainability, co-editing the book *SeaCities: Urban Tactics for Sea-Level Rise* and developing frameworks for integrating SDGs into architectural education.

• Artificial Intelligence in Healthcare
• Biomedical Engineering
• Traditional Chinese Medicine Informatics
• +3 more

Dr. Jing Wang is a Professor in the Department of Bioinformatics at Southern Medical University's School of Medicine, with extensive research at the intersection of artificial intelligence and biomedical applications. Her work demonstrates strong cross-disciplinary collaboration across medical institutions, engineering departments, and computer science research groups. Her primary research interests include Artificial Intelligence in Healthcare , Biomedical Engineering , and Traditional Chinese Medicine Informatics , with recent publications showing particular expertise in medical imaging analysis, diagnostic assistance systems, and clinical decision support. Her work spans both theoretical algorithm development and practical clinical implementations. Analysis of her 15 most recent publications (2025-2026) reveals a strong trend toward clinically applicable AI systems, with approximately 60% of publications focused on medical diagnostics and treatment support systems. The remaining publications demonstrate expertise in industrial applications of computer vision and fundamental AI research. Her work shows consistent collaboration with both domestic Chinese institutions and international research groups. Notable scientific contributions include: Development of 'Tianyi', a traditional Chinese medicine language model for clinical practice Innovations in bionic soft robotics for rehabilitation assistance Novel approaches to medical image analysis for cancer diagnostics Her research program appears well-funded with consistent publication output across high-impact journals in biomedical engineering, AI, and medical informatics. Current work suggests strong emphasis on translating AI research into clinical practice, particularly in diagnostic support systems and rehabilitation technology.

• Data Science
• Computational Social Science
• Misinformation
• +3 more

Meeyoung Cha is a Professor at KAIST and Scientific Director of the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. Her research focuses on Data Science for Humanity, encompassing computational social science, misinformation dynamics, and human-machine interaction. She holds a PhD in Computer Science from KAIST (2008) and previously served as Chief Investigator at the Institute for Basic Science and Visiting Professor at Facebook. Her work addresses societal challenges such as poverty mapping, fraud detection, and AI ethics. Key achievements include best paper awards and recognition like the Hong Jin-Ki Creator Award (2024) and Test-of-Time Awards (ACM IMC 2022, AAAI ICWSM 2020). Research interests span AI ethics, social media analysis, and interdisciplinary applications of machine learning. Notable projects include modeling climate risks via satellite imagery and analyzing chatbot interactions' societal impacts. She leads the MPI-SP's Data Science for Humanity Group, mentoring over 20 students across PhD and postdoc programs. Education: PhD in Computer Science (KAIST, 2008) Affiliations: MPI-SP (Germany), KAIST Key Awards: Hong Jin-Ki Creator Award, Korean Young Information Scientist Award, Test-of-Time Awards Her publications bridge computational methods with societal issues, including climate modeling, protein engineering, and algorithmic fairness. Current projects explore geospatial AI for economic development and ethical AI design frameworks.

Theodora VarvarigouView profile

Professor

• Cloud Computing
• Multimedia Content Processing
• Social Networking Technologies
• +3 more

Theodora Varvarigou is a Professor in the Department of Electrical and Computer Engineering at the National Technical University of Athens (NTUA). She holds a B.Eng. from NTUA and M.Eng. and Ph.D. degrees from Stanford University. Her career includes research at AT&T Bell Labs and roles at the Technical University of Crete. From 2008-2012, she served as director of NTUA's 'Technoeconomic Systems' postgraduate program. Her research focuses on Cloud Computing, Multimedia Content Processing, Social Networking Technologies, and emerging areas like blockchain, edge computing, and cybersecurity. She has published over 200 papers and led numerous European research projects, emphasizing scalable systems, data management, and smart infrastructure applications. Her work spans technical innovations such as intrusion detection systems, edge resource optimization, and blockchain-based solutions for IoT, healthcare, and smart cities. Recent publications highlight advancements in AI-driven resource allocation, privacy-preserving blockchain designs, and predictive analytics for edge computing environments. Professor Varvarigou has contributed to interdisciplinary initiatives, including cohort data harmonization in biomedical research and social media analytics for urban planning. Her teaching includes courses on digital systems, network programming, and fault-tolerant systems.

• Machine Learning
• Health Informatics
• Public Health
• +3 more

Ke Wu is a Professor in the Department of Computer Science and Engineering at the University of Michigan. Their research focuses on the intersection of machine learning, biostatistics, and healthcare technology, with an emphasis on mobile health interventions, causal inference, and Bayesian methods. They lead a small, hands-on research group mentoring PhD students and postdocs. Key interests include developing predictive models for health outcomes, improving treatment effect estimation, and leveraging mobile technology for caregiver support. Their work has addressed critical challenges in clinical decision-making, public health surveillance, and healthcare innovation. Research projects span synthetic data generation for electronic health records, mHealth app development for care partners of traumatic brain injury patients, and algorithmic fairness in reinforcement learning. Ke Wu emphasizes interdisciplinary collaboration and has contributed to global health studies, including analyses of pneumonia etiology in low-resource settings and the PERCH study. Their group's methodologies often integrate wearable sensor data and machine learning to address real-world health challenges. Advising priorities include fostering student independence while maintaining close mentorship, with expectations for consistent research productivity and professional development. Students are encouraged to pursue teaching roles (e.g., GSI positions) and internships aligned with career goals. Funding support for conference participation is available through institutional and external grants. Ke Wu's contributions extend to statistical methodology, including Bayesian latent class models and dynamic risk prediction frameworks. They actively engage in translational research, bridging computational methods with clinical and public health applications, and prioritize open-source software development to advance reproducible research practices.

Claude DELPHAView profile

Professor

• Signal Processing
• Fault Diagnosis and Prognosis
• Electrical Engineering Systems
• +4 more

Claude DELPHA is a Full Professor at Université Paris Saclay, affiliated with CentraleSupélec’s Laboratoire des Signaux et Systèmes (L2S). He holds an IEEE Senior Member status and has been with L2S since 2001. His expertise spans signal processing, fault diagnosis, electrical engineering systems, and machine learning. He leads the Modelling and Estimation team (GME) at L2S and oversees engineering admissions at Polytech Paris Saclay. Education: PhD in Instrumentation & Measurements and Signal Processing from Université de Metz, with a focus on intelligent sensor systems. Graduate degree in Electrical and Signal Processing Engineering. Research Interests: Multidimensional/statistical signal processing, fault diagnosis/prognosis (modeling, detection, estimation), electrical systems (drives, converters, PV), data hiding (watermarking), and pattern recognition (machine/deep learning). Active in energy systems, industry 4.0, and health/biology applications. Professional Roles: Director of GME research team, Polytech admissions lead, member of Polytech’s executive and academic boards, and IUT department council member. Engaged in labs like SYCOMORE and ILOCOS. Publications: Over 200 works since 2015, focusing on fault diagnosis in electrical systems, photovoltaic modules, bearings, and tidal turbines. Key methods include Kullback-Leibler divergence, Jensen-Shannon divergence, Mahalanobis distance, and PCA-based approaches. Awards: Not explicitly listed in provided texts.

Hemant K. BhargavaView profile

Professor

• Technology Management
• Management Information Systems
• Economics of Information Technology
• +12 more

Hemant K. Bhargava is a Distinguished Professor at the Graduate School of Management, University of California Davis, where he holds the Jerome and Elsie Suran Chair in Technology Management. He serves as Associate Dean for Academic Affairs and Director of the Center for Analytics and Technology in Society (CATS), and previously served as Associate Dean for Instructional Programs (2010–2013) and Faculty Chair. He co-founded and was the first Academic Director of the UC Davis Master of Science in Business Analytics (MSBA) program. His educational background includes: Ph.D. in Decision Sciences, The Wharton School, University of Pennsylvania (1989) MBA, Indian Institute of Management Bangalore (1986) B.S. in Mathematics, University of Delhi (1984) Bhargava is a leading scholar in technology management, information systems, and decision analytics. His research focuses on the economics of digital goods and platforms, pricing strategies, AI in business, healthcare IT, and platform ecosystems. He builds economic models to study operations, marketing, and competitive strategy in tech-driven markets, with applications in healthcare, media, electric vehicles, and generative AI. His recent publications reveal a strong trend toward platform business models, data sharing, AI’s impact on research and education, and healthcare cost transparency. He explores how digital platforms shape competition, how data can be leveraged across markets, and how real-time decision tools can reduce prescription drug costs. His work bridges theory and practice, often involving collaboration with industry and policy makers. His scientific awards include: INFORMS Journal on Computing Test of Time Award (2023) INFORMS CIST Best Paper Award (2021) Google Research Excellence Gift (2017–18) Distinguished Alumni Award, IIM Bangalore (2024) INFORMS ISS President's Service Award (2023) Global 100 Top Academic Data Leaders (2020) Bhargava has played a pivotal role in academic leadership and program development. He co-founded the Theory in Economics of Information Systems (TEIS) workshop and has served on editorial boards of top journals including Management Science (where he is Department Editor for Information Systems), Operations Research, and Marketing Science. He has advised numerous campus initiatives, including the UC Davis Data Lab and Religions of India Initiative. His grants and collaborations include work with Google and healthcare organizations on real-time price transparency tools. He leads the Center for Analytics and Technology in Society (CATS), which focuses on the societal implications of data and AI. He also co-founded the MSBA program and continues to shape the future of business education in the age of AI through strategic planning committees.

Brad CampbellView profile

Associate Professor

• Ubiquitous Computing
• Internet of Things (IoT)
• Embedded Systems
• +12 more

Brad Campbell is an Associate Professor in the Department of Computer Science and Electrical and Computer Engineering at the University of Virginia, where he is a member of the Link Lab, a cross-disciplinary research group focused on cyber-physical systems. His research centers on designing and building scalable, effective, and unobtrusive embedded systems for the Internet of Things, with applications in smart buildings, smart cities, and personal health. His work spans hardware design, networking, and cloud infrastructure, with a strong emphasis on energy-harvesting systems, low-power wireless communication, and resilient embedded operating systems. He has led projects such as the Living Link Lab, a heavily instrumented smart building testbed, and has developed open-source platforms for self-powered sensing and IoT ecosystems. His recent publications reflect a strong trend toward privacy-preserving federated learning, contactless occupancy sensing using WiFi and light, decentralized edge computing, and sustainable IoT systems. These works are published in top venues including SenSys, BuildSys, MobiCom, and IPSN, indicating a high impact in the systems and networking community. NSF CAREER Award (2022) Best Paper Award at DFHS’19 Multiple graduate fellowships and teaching awards for his students UVA Engineering Endowed Graduate Fellowships Link Lab Seminar Award CPS Rising Star recognition Brad Campbell has advised numerous PhD and master’s students, many of whom have gone on to academic and industry roles. He has secured significant research funding, including from the NSF, and has contributed to curriculum development in cyber-physical systems. He is actively involved in teaching courses on computer networking, IoT, and operating systems, and has co-taught wireless IoT courses across multiple institutions. His lab focuses on real-world deployment of IoT systems, emphasizing scalability, fault tolerance, and long-term sustainability. He continues to push the boundaries of what embedded systems can achieve in everyday environments, from homes to cities.

• Business Process Management
• Robotic Process Automation (RPA)
• Process Mining
• +4 more

Rehan Syed is a Professor at Queensland University of Technology's School of Information Systems within the Faculty of Science and Engineering. His research focuses on Business Process Management (BPM), Robotic Process Automation (RPA), and Process Mining, with significant contributions to understanding digital transformation challenges in public and healthcare sectors. He has authored/co-authored over 35 peer-reviewed publications in top journals and conferences like BPM, HICSS, and ECIS. Key areas of expertise include leadership in IT initiatives, healthcare data quality, and low-code adoption strategies. Recent work emphasizes RPA's impact on organizational processes and knowledge retention. His research bridges academic theory with practical implementation, often collaborating with institutions like UN agencies and healthcare organizations. Publications span case studies in developing countries, systematic reviews, and curriculum development frameworks for BPM education. His work is cited across disciplines, reflecting its relevance to both academia and industry.