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Jacqueline CurtisView profile

Associate Professor

• Spatial Epidemiology
• GIS Applications in Public Health
• Environment-Health Relationships
• +4 more

Jacqueline Curtis, PhD, is an Associate Professor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine. She specializes in spatial epidemiology, focusing on environment-health relationships and using geospatial techniques to improve care for vulnerable populations, including neurodiverse individuals. Her work emphasizes integrating local knowledge into spatial data collection to inform clinical interventions. Education: PhD in Geography from Louisiana State University (2005). Professional affiliations include the American Public Health Association, Society of Gynecologic Oncology, and International Dyslexia Association. She teaches courses like MPH 426/PQHS 426: GIS for Health & Social Sciences. Research Interests: Spatial epidemiology, geonarratives, GIS applications in public health, and health disparities. Her contributions include developing methods to assess environmental perceptions and enhance geospatial approaches for marginalized populations. Affiliations: Member of the Case Comprehensive Cancer Center's Population and Cancer Prevention Program and faculty affiliate at the Mary Ann Swetland Center for Environmental Health. She also serves on editorial boards for Cartography and Geographic Information Science and International Journal of Environmental Research and Public Health . Grants & Labs: Focuses on projects addressing homelessness, air pollution exposure, and lung transplantation through place-based methods. Collaborates on initiatives like GeoMEDD for early disease detection.

• Medical Informatics
• Machine Learning in Healthcare
• Predictive Modeling in Medicine
• +3 more

Thomas Lasko is an Associate Professor holding dual appointments in the Department of Biomedical Informatics and the Department of Computer Science at Vanderbilt University. His work focuses on advancing machine learning and computational methods to address critical challenges in healthcare, particularly leveraging electronic health records (EHR) for disease prediction, risk stratification, and clinical decision-making. Lasko’s research spans areas such as predictive modeling for chronic diseases (e.g., COPD, systemic lupus erythematosus), multimodal data integration (e.g., imaging and EHR), and improving the generalizability of clinical models across healthcare institutions. His academic contributions emphasize scalable solutions for large-scale EHR analysis, including lightweight natural language processing (NLP) models for document classification and contrastive learning approaches for patient-level representation. Lasko has also pioneered methods for uncovering latent disease signatures using probabilistic independence and has developed tools like pyPheWAS for phenome-disease association studies. His work frequently intersects with clinical practice, aiming to translate algorithmic advancements into actionable clinical decision support systems. Lasko’s recent research trends highlight a focus on longitudinal data analysis, model calibration drift mitigation, and explainable AI (XAI) for enhancing trust in medical AI systems. While his articles emphasize technical innovation, they consistently ground methodologies in real-world healthcare challenges, such as optimizing lung cancer screening protocols and identifying comorbidity patterns in autoimmune diseases. Despite his prolific output, no formal scientific awards or student advising records are explicitly mentioned in the provided texts. He collaborates across disciplines, integrating expertise from computer science, biomedical informatics, and clinical medicine to tackle problems such as automated patient acuity determination and novel disease subtyping. Ongoing work includes refining multimodal fusion techniques for pulmonary nodule classification and developing synthetic data frameworks to improve model robustness in resource-limited settings.

Bradley MalinView profile

Professor

• Biomedical Informatics
• Data Science
• Machine Learning
• +3 more

Bradley Malin is a Professor of Biomedical Informatics, Biostatistics, and Computer Science at Vanderbilt University. His research focuses on biomedical informatics, data science, and machine learning, with an emphasis on data privacy and security in healthcare contexts. He holds academic appointments across multiple departments, reflecting his interdisciplinary work in informatics and computational health research. Malin earned all his degrees from Carnegie Mellon University, including a Ph.D. in Computation, Organization, and Society, and has contributed significantly to advancing methodologies for privacy-preserving data analysis, synthetic data generation, and equitable AI applications in medicine. His work bridges technical innovation with ethical considerations, addressing challenges in healthcare data interoperability, bias mitigation, and scalable solutions for genomic and clinical data. Recent research trends include optimizing AI fairness in clinical predictions, leveraging large language models for healthcare documentation, and enhancing cohort representativeness in participatory research programs. He has also explored the societal implications of AI in medicine, including the role of race in algorithmic decision-making and privacy risks in genomic data sharing. Malin has co-authored numerous influential papers on topics such as federated learning frameworks, privacy-preserving synthetic data, and ethical safeguards in genomic research. His contributions extend to policy development, including guidelines for trustworthy AI in healthcare and frameworks for balancing privacy with data utility in public health initiatives.

Jayanth RaghothamaView profile

Associate Professor

• Healthcare Informatics
• Complex Systems Modeling
• Simulation and Gaming in Policy Design
• +3 more

Jayanth Raghothama is an Associate Professor at KTH Royal Institute of Technology, affiliated with the School of Computer Science and Communication (CBH) and the Digital Futures Faculty. He is part of the Division of Health Informatics and Logistics, focusing on healthcare systems, complex systems modeling, and simulation-based decision-making. His academic role includes co-leading the Digitalization Platform and serving as a Vice-Director. Education: PhD in Complex Systems from KTH (2017). Prior experience includes software industry roles and work with a think tank in Bangalore. Research Interests: Healthcare logistics, complex systems analysis, participatory simulations, AI/ML ethics, and the philosophy of games and simulations. His work bridges computational models with real-world applications in healthcare, urban planning, and disaster management. Recent Projects: Co-Principal Investigator of the 'Data-driven Improvement of Work-Flows at the Karolinska University Hospital' project. Active in AI-based colorectal cancer detection in primary care (AIDCCIP project). Teaching: Courses on Applied Machine Learning, Simulation Methods, and Healthcare Logistics at Bachelor, Master, and PhD levels. Modules include AI/ML ethics and Agent-Based Modeling. Publications: Over 20 peer-reviewed articles focusing on healthcare simulation models, real-world data analysis in oncology, and hybrid modeling techniques. Notable works include patient flow optimization algorithms and frameworks for risk stratification in lung cancer studies.

• Community Detection
• Graph Clustering
• Graph Representation Learning
• +4 more

Susanna Pozzoli is a doctoral student at the Division of Software and Computer Systems (SCS) within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology. She also serves as the vice chairperson of the EECS PhD Council, representing doctoral students at the school level. Education: Bachelor of Science in Engineering of Computing Systems from Politecnico di Milano (2017) EIT Digital Master's Programme in Data Science (2017–2019), completed first year at Politecnico di Milano and second year at KTH in Stockholm, Sweden Research Interests: Susanna’s work focuses on advancing graph-based machine learning techniques, including community detection, graph clustering, role discovery, and unsupervised learning. She explores how graph representation learning can enhance performance evaluation in complex networks, with applications ranging from social network analysis to biomedical data science. Publications: Her recent work highlights innovations in graph embedding techniques and diffusion-driven role recognition, alongside interdisciplinary research in domain-agnostic feature selection for breast cancer analysis. These studies reflect her expertise in bridging theoretical computer science with practical data-driven solutions. Awards & Grants: No specific awards or grants mentioned in the provided information. Labs & Teams: While no specific lab affiliations are listed, her research is embedded within the collaborative environment of KTH’s SCS division.

• Machine Learning
• Medical Device Design
• Statistical Pattern Recognition
• +6 more

Adjunct Professor Andrew Bradley is affiliated with Queensland University of Technology (QUT), where he serves as the Associate Dean (Research) for the Science and Engineering Faculty. He holds positions in the Faculty of Engineering and School of Electrical Engineering & Robotics at QUT. Bradley has a PhD from the University of Queensland and extensive industry experience, including roles as Chief Technology Officer at Ausonex Pty Ltd and Principal Biomedical Engineer at Maxwell MRI. His expertise spans biomedical and electrical engineering, computing science, and communications technology. His research focuses on computer-aided diagnosis (CAD) systems in medical imaging, machine learning, medical device design, and statistical pattern recognition. He has contributed to projects involving deep brain stimulation, artificial hearts, neonatal hearing screening, and cancer diagnosis imaging. His work bridges academia and industry, emphasizing commercially oriented solutions while maintaining a strong publication record. Bradley has published over 140 peer-reviewed papers and holds 20 patents across multiple regions. His research has garnered over 8,400 citations with an h-index of 35. He has secured $6M+ in competitive grants and supported startups raising $4M in venture capital. His awards include the ARC Future Fellowship and Fellow of Engineers Australia. He has advised 21 PhD and 4 Research Masters graduates, with three current advisory roles. Bradley’s contributions span interdisciplinary collaborations and innovation in labs focusing on medical imaging, AI, and biomedical engineering applications.

• Biostatistics
• Spatial statistics and disease mapping
• Statistical methods for survival and longitudinal data
• +3 more

Cindy Feng, PhD, is an Associate Professor in the Department of Community Health and Epidemiology at Dalhousie University's Faculty of Medicine. Her research focuses on developing biostatistical models for analyzing correlated public health data, including spatial statistics, longitudinal studies, and environmental health applications. She holds a PhD from Simon Fraser University and has received funding from NSERC, Canadian Statistical Sciences Institute, and MITACS. Dr. Feng collaborates with interdisciplinary teams across medicine, psychology, and environmental sciences. Education: PhD (Simon Fraser University), MSc (Simon Fraser University), BSc (Beijing University of Technology). Her work emphasizes bridging statistical theory and practice in public health, with notable contributions to disease mapping, survival analysis, and infectious disease surveillance. Key grants include NSERC Discovery Grants ($80,000, 2019-2023) and a MITACS Accelerate Grant ($45,000, 2016-2019). Research interests include zero-inflated models, spatial epidemiology, and methodological advancements for correlated data. Recent work addresses pandemic-related mental health trends, occupational injury risk factors, and global health challenges in malaria-schistosomiasis co-endemic regions. She has published over 30 peer-reviewed articles, with a focus on statistical diagnostics, public health policy, and environmental health impacts.

Joseph C. AyoobView profile

Associate Professor

• Computational Biology
• Educational Program Development
• Mentoring Strategies
• +3 more

Joseph C. Ayoob, PhD, is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh School of Medicine. He holds roles as Associate Director of the UPMC Hillman Academy and Faculty Fellow of the University of Pittsburgh Center for Mentoring. His work focuses on innovative training programs for students at all academic levels, including the NSF-funded TECBio REU program (since 2010) and the CompBio Academy high school program (since 2011). He also directs the CPCB MetaSchool, a professional development series for graduate students. Dr. Ayoob earned his PhD in Neuroscience from Johns Hopkins University School of Medicine. His educational efforts span undergraduate, graduate, and postdoctoral trainees, emphasizing lab-based skills and professional development. He is a Principal Facilitator for the Entering Mentoring and Entering Research curricula, recognized as a National Research Mentoring Network (NRMN) Master Mentor and Mentor of the Month. Research interests include STEM education innovation, mentorship best practices, and computational biology training. He has pioneered programs like the CPCB Presentation Course and CoBB Professional Development course. Awards include recognition for mentorship excellence and contributions to mentored research training. Publications highlight his work in educational frameworks, undergraduate research effectiveness, and molecular mechanisms in neurodegenerative diseases. His lab supports interdisciplinary training through summer programs targeting underrepresented students in STEM fields.

David LovellView profile

Professor

• Data Science
• Bioinformatics
• Computational Biology
• +3 more

David Lovell is a Professor in the School of Computer Science at Queensland University of Technology (QUT). He previously served as Head of the School of Electrical Engineering and Computer Science (2014–2019) and Deputy Director of QUT’s Centre for Data Science (2020–2021). His research focuses on the intersection of humanity, science, and technology, particularly in data science and compositional data analysis (CoDA). Lovell holds a PhD in Electrical Engineering from the University of Queensland (1994) and a BEng (Honours 1) in Computer Systems (1989). Education Background: BEng(Hons 1)(Computer Systems), University of Queensland (1989) PhD, University of Queensland (1994) PGDipMgmt, Macquarie University Research Interests: David’s work emphasizes ethical and human-centric applications of data-driven decision-making systems. He advocates for CoDA in bioscience to ensure appropriate analysis of relative abundance data. His research spans bioinformatics, healthcare technology, and the societal implications of data science. Publications Overview: Lovell has contributed to over 50 peer-reviewed articles, including seminal work on proportional analysis in bioinformatics and applications of machine learning in healthcare. Recent trends include studies on Indigenous data literacy, AI in radiology training, and tumor microenvironment analysis. Awards & Recognition: While no specific awards are listed, his leadership roles and prolific publications highlight his significant contributions to the field. Advising & Grants: As Head of EECS and through interdisciplinary collaborations, Lovell fostered institutional growth. His supervision focuses on large-scale bioinformatics and information retrieval. Labs & Teams: Active in the Centre for Data Science and previously led the Statistical Bioinformatics-Agribusiness Group at CSIRO. Collaborates with organizations like EMBL Australia and Bioplatforms Australia.

Michelle GreiverView profile

Professor

• Public Health
• Cardiovascular Disease
• Diabetes Management
• +3 more

Prof Michelle Greiver is an Honorary Professor in the School of Medicine at the University of St Andrews. Her research focuses on public health outcomes in primary care settings, particularly addressing cardiovascular disease, diabetes management, and healthcare disparities. She collaborates on projects involving medication adherence patterns, mental health comorbidities, and advanced statistical modeling techniques. Her work contributes to UN Sustainable Development Goals related to good health and well-being (SDG 3). Recent studies include analyses of breast cancer screening equity among individuals with schizophrenia and the impact of adherence to medication regimens on cardiovascular outcomes in patients with diabetes and hypertension. Prof Greiver is affiliated with the University of St Andrews School of Medicine, contributing to both research and academic activities. Her methodologies often integrate epidemiological approaches with health informatics, including applications of marginal structural models and machine learning algorithms.

Jytte BrenderView profile

Adjunct Professor

• Medical Informatics
• Health Informatics
• Artificial Intelligence in Healthcare
• +8 more

Jytte Brender is an Adjunct Professor in the Department of Health Science and Technology at Aalborg University, Faculty of Medicine, Denmark. Her work bridges medical informatics, health information technology, and clinical decision-making, with a strong emphasis on evaluation methodologies. She has contributed to national and international research projects focused on patient safety, laboratory information systems, and AI in healthcare. University: Aalborg University School: Faculty of Medicine Department: Department of Health Science and Technology Academic Rank: Adjunct Professor Email: jmn@hst.aau.dk Her research interests include medical and health informatics, artificial intelligence in clinical settings, clinical decision support systems, evaluation of health IT, patient safety, and theoretical frameworks such as the Mereon Matrix. She has also explored applications in human molecular genetics through theoretical modeling. The recent publications (2023–2024) highlight a growing focus on the evaluation and ethical implementation of AI in clinical decision-making, emphasizing the need for robust frameworks and avoiding redundant evaluation cycles. Her work spans journal articles, book chapters, and conference proceedings, often in collaboration with leading figures in medical informatics. Scientific Contributions: Key contributor to the PSIP project on patient safety in medication. Active in LIS and laboratory system development and assessment. Prominent voice in AI evaluation in healthcare, cited in policy and news outlets. She has advised on health IT evaluation frameworks and participated in multi-institutional research. While no formal students are listed, her collaborative work suggests mentorship and advisory roles. She is not retired or deceased, and her adjunct status suggests a continued but possibly part-time or affiliated academic role.

A. BozzonView profile

Professor

• Artificial Intelligence
• Human-Computer Interaction
• AI Ethics
• +3 more

A. Bozzon is a Full Professor at Delft University of Technology, affiliated with the Department of Web Information Systems under the Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on Human-Centered Artificial Intelligence (HCAI), emphasizing ethical AI, human-AI interaction, and societal implications. He has authored over 240 publications and secured 8 notable awards, including Best Paper Awards at CHI 2023 and 2025. Affiliations : Delft University of Technology Research Interests : AI ethics, human-AI collaboration, algorithmic fairness, and sustainable design. His work bridges technical innovation with social impact, addressing topics like mental health diagnostic tools, automation adoption in organizations, and greenspace accessibility. Key contributions include datasets on adverse drug reactions and AI-driven urban analysis. Collaborations span global institutions, reflecting his interdisciplinary approach. Awards : Best Paper Award at CHI 2023 Best Paper Award at CHI 2025 CTwalk Map: Best Demo Award (2024) Supervised 14 academic works, fostering next-generation researchers in HCAI. Active in policy discussions around AI governance and has pioneered tools like Amalur for FAIR data integration.

Richard M. GoldenView profile

Professor

• Cognitive Science
• Statistical Machine Learning
• Computational Psychometrics
• +4 more

Richard M. Golden is a Professor of Cognitive Science and Participating Faculty in the Erik Jonsson School of Engineering and Computer Science at the University of Texas at Dallas (UTD). He directs the COgnitive INformatics and Statistics (COINS) Lab, focusing on computational psychometrics and statistical machine learning applied to educational technologies. His research integrates cognitive science, machine learning, and usability engineering to improve personalized instruction. He authored the textbook Statistical Machine Learning: A Unified Framework and hosts the Learning Machines 101 podcast. Golden leads UTD's Undergraduate Cognitive Science and Graduate Applied Cognition and Neuroscience programs. Education : Ph.D. in Experimental Psychology (Brown, 1987), M.S. in Electrical Engineering (Brown, 1986), B.S. in Electrical Engineering & Psychology (UC San Diego, 1982). Research : Prior work includes neural network models of text comprehension, model misspecification analysis, and Bayesian inference. Current projects include diagnostic assessment tools and AI-driven educational systems. Awards : Secretary-Treasurer Service Award (2017), IEEE Senior Member (1999), multiple invited speaker roles. Labs/Teams : COINS Lab develops tools like the ARCADE system for automated reading assessment. Collaborates with organizations like the Veteran's Administration and National Science Foundation.

Mousumi BanerjeeView profile

Research Professor

• Biostatistics
• Cancer Research
• Health Services Research
• +4 more

Mousumi Banerjee is the Anant M. Kshirsagar Collegiate Research Professor in the Department of Biostatistics at the University of Michigan School of Public Health. She also holds the position of Research Professor in Global Public Health and serves as Director of Biostatistics for the Pediatric Cardiac Critical Care Consortium (PC4) Analytic Center and Director of Global StatCore. Her affiliations include the Rogel Cancer Center, Institute for Healthcare Policy and Innovation, Center for Global Health Equity, and Michigan Institute for Data Science (MIDAS). Bachelor's & Master's in Statistics from Indian Statistical Institute, Kolkata PhD in Statistics from University of Wisconsin-Madison (1994) Dr. Banerjee develops advanced statistical methodologies for biomedical applications, particularly in cancer and pediatric heart disease. Her research includes predictive modeling, machine learning, causal inference, survival analysis, and correlated data techniques. She focuses on health disparities and equitable care delivery, collaborating across disciplines including neurology, surgery, and social determinants of health. Recent publications demonstrate her expertise in statistical algorithm development for clinical quality assessment, disease classification, and healthcare outcomes. Her work spans applications in bladder cancer detection, thyroid cancer follow-up imaging, HPV-related oropharyngeal carcinoma analysis, and cardiac arrest prevention in pediatric critical care settings. Scientific Awards Fellow of the American Statistical Association Elected member of the International Statistical Institute As Director of Global StatCore, Banerjee enhances biostatistical support for global public health initiatives. She leads major methodological projects for the Pediatric Cardiac Critical Care Consortium (PC4) and works with national datasets like SEER, NCDB, and Medicare claims to analyze healthcare disparities and quality improvement metrics.

Maruf AdewoleView profile

Research Fellow

• Artificial Intelligence
• Medical Imaging
• Medical Physics
• +7 more

Maruf Adewole, PhD, is a Postdoctoral Research Fellow and Associate Scholar at the University of Pennsylvania's Perelman School of Medicine, Department of Radiology. He is affiliated with the Center for Global Health and serves as Executive Director of the Medical Artificial Intelligence Laboratory (MAI Lab) in Lagos, Nigeria. His research focuses on leveraging artificial intelligence to improve medical imaging and cancer diagnosis in resource-constrained settings across Africa. Key areas include medical physics, radiomics, oncology, and open science. He leads impactful initiatives such as BraTS-Africa, ABreast, and SPARK Academy, which aim to build local AI capacity and promote health equity. The recent publications reflect a strong trend in applying machine learning to brain tumor segmentation and radiology in underserved populations, emphasizing inclusive dataset development and AI model validation in African patient cohorts. Scientific Awards: No scientific awards mentioned in the text. Advising and Grants: While formal students are not listed, Dr. Adewole has trained over 300 participants through the SPARK Academy, a structured AI and medical image computing program. He leads major research initiatives including BraTS-Africa and ABreast, which involve international collaboration, dataset curation, and model development for cancer care in Africa. Labs and Teams: Dr. Adewole leads the Medical Artificial Intelligence Laboratory (MAI Lab) in Lagos, Nigeria. He is also a key contributor to global health projects at the University of Pennsylvania’s Center for Global Health, working on initiatives that bridge AI, radiology, and equitable healthcare delivery.