Find Academic Supervisors

Lisa DelhommeView profile

Academic

• Health Information Management
• Healthcare Privacy and Security
• Healthcare Data Management
• +6 more

Lisa Delhomme is a Master Instructor and Program Director for the Health Information Management Program in the College of Nursing and Health Sciences at the University of Louisiana at Lafayette. She plays a key academic and administrative role in shaping the curriculum and student experience in health information sciences. Master of Science in Health Services Administration, University of St. Francis, 2003 Bachelor of Science in Health Information Management, University of Southwestern Louisiana, 1999 Her research and teaching focus on critical areas in modern healthcare systems, including healthcare privacy and security, health data management, CPT coding, and ethical-legal frameworks in healthcare. She emphasizes professional standards and regulatory compliance in health information practices. Lisa's publications predominantly center on certification exam preparation for health information professionals, covering RHIA/RHIT, CCS, CCS-P, and CCA credentials. These works reflect her dedication to medical education and workforce readiness in health information management, with recurring themes in coding standards, data integrity, and interprofessional training. Ray P. Authement Excellence in Teaching Award, 2020 Career Achievement Award, Louisiana Health Information Management Association, 2020 Colonel Jean Migliorino and Lt. Colonel Philip J. Piccione 1951 Commemorative Endowed Nursing Faculty Award for Teaching Excellence, 2018 ULearn Certified Course: HSA 404, 2015 Certified Online Course Designer and Teacher, 2012 Action Community for Excellence Member, AHIMA, 2012 Multiple Awards for Excellence in Academic Advising (2009–2011) Louisiana Medical Group Management Association’s Traveling Scholarship, 1999 Lisa has been recognized extensively for academic advising and teaching excellence. While no formal grants are mentioned, her leadership in developing certified online courses and interprofessional education initiatives demonstrates a strong commitment to innovative pedagogy and student mentorship in health sciences. Though no specific lab or research team is described, her collaborations with colleagues such as Hazelwood, Venable, and Schnering highlight active engagement in curriculum development and professional resource creation within the health information management community.

Sharon C. KiangView profile

Associate Professor

• Vascular Surgery
• Endovascular Interventions
• Peripheral Vascular Disease
• +4 more

Sharon C. Kiang, MD, is an Associate Professor and Vice Chair of Research in the Department of Surgery, Vascular Division at Loma Linda University's School of Medicine. She specializes in vascular surgery with a focus on endovascular interventions, peripheral vascular disease, and the application of artificial intelligence (AI) in clinical decision-making. Her research spans traumatic vascular injuries, aortic aneurysms, and outcomes analysis in chronic limb-threatening ischemia (CLI). Dr. Kiang holds an MD from Vanderbilt University and a BS from the University of Maryland. Her clinical and research activities are supported by grants from LLU departments, including studies on AI's role in vascular care, radiation safety in fluoroscopy-guided procedures, and long-term outcomes of vascular injuries. Her research outputs emphasize translational science, with publications in Annals of Vascular Surgery , Circulation , and Journal of Vascular and Interventional Radiology . Key areas include validating ICD-10 codes for CLI, analyzing AI biases in vascular imaging, and investigating technical failures in endovascular procedures. She has led or collaborated on over 75 peer-reviewed articles since 2008. Dr. Kiang’s academic contributions extend to mentoring students through her roles in residency and fellowship training programs. Her work integrates clinical practice with advancing technologies to improve patient care outcomes.

Saskia E. DröslerView profile

Professor

• Health Services Research
• Medical Informatics
• Quality Assurance
• +5 more

Professor Saskia E. Drösler holds a faculty position at Niederrhein University of Applied Sciences in the Department of Health Care Management within the School of Health Sciences . Her academic expertise spans health services research, medical informatics, patient safety, and quality assurance with a focus on administrative data analysis for healthcare policy. Her research, visible through publications from 2019 to 2011, explores: ICD-11/10 coding frameworks for safety events Small-area health disparities in diabetes care International healthcare quality comparisons Administrative data limitations and improvements Standardization of diagnosis-timing in hospital records Key collaborations include institutions such as University of Calgary, UC Davis , and participation in WHO ICD-11 advisory groups . She has served in multiple university governance roles at Hochschule Niederrhein from 2004 to 2023.

Erica KoeglerView profile

Assistant Professor

• Human Trafficking
• Global Health
• Public Health
• +5 more

Erica Koegler serves as an Assistant Professor in the School of Social Work at the University of Missouri-St. Louis, where she teaches Master's level research methods courses and advises MSW students. Her academic journey includes a PhD in Public Health from Johns Hopkins Bloomberg School of Public Health (2016), a Master of Social Work from the University of Chicago (2010), and a BA in Psychology from Eastern Illinois University (2004). Her educational foundation is reflected in her research profile: PhD, Public Health, Johns Hopkins Bloomberg School of Public Health (2011-2016) MA, Social Work, University of Chicago (2008-2010) BA, Psychology, Eastern Illinois University (2000-2004) Dr. Koegler's research centers on human trafficking in the American Midwest and global health issues affecting conflict-affected populations, with expertise in mixed methods and community-based participatory research. Fluent in Swahili, she has conducted fieldwork in the Democratic Republic of Congo and South Africa, examining intersections of mental health, sexual risk, and substance use among vulnerable populations including refugees and transgender individuals. Her 24+ publications reveal consistent methodological rigor and thematic focus on trafficking victimization patterns, mental health correlates, and service gaps. Recent work employs latent class analysis for victimization typologies and examines ICD coding practices in emergency departments, demonstrating both epidemiological sophistication and practical clinical applications. Her research consistently bridges global health perspectives with domestic Midwest contexts. Dr. Koegler currently leads two human trafficking research initiatives in the Midwest while mentoring graduate students. Her collaborative work with service providers informs trauma-informed treatment approaches for survivors, particularly regarding substance use interventions. She actively engages with community stakeholders through participatory research frameworks to address systemic gaps in trafficking identification and response systems.

Asai AsaithambiView profile

Professor

• Artificial Intelligence
• Computational Science
• High-Performance Computing
• +3 more

Asai Asaithambi is a Professor and Graduate Program Director in the School of Computing at the University of North Florida (UNF), where he also served as School Director from 2011-2015. With 37+ years of academic experience, he previously held leadership roles including Professor/Chair at the University of South Dakota (2004-2011), Associate Professor/Chair at Saint Louis University (1997-2004), and Assistant/Associate Professor positions at Lincoln University and Mississippi State University. His academic credentials include: PhD in Computer Science, University of Wisconsin-Madison (1985) MS in Computer Science, Indian Institute of Technology-Madras (1980) Bachelor of Technology in Electrical Engineering, Indian Institute of Technology-Madras (1977) Professor Asaithambi's research spans artificial intelligence, computational science, and high-performance computing, with significant contributions to quantum computing algorithms, biomedical image analysis, and numerical methods for differential equations. His work demonstrates consistent focus on applying computational techniques to real-world problems in healthcare, cybersecurity, and engineering, while maintaining strong commitment to broadening participation of underrepresented groups in computing. Analysis of his 15 most recent publications (2019-2025) reveals dominant themes in medical imaging AI (lung X-ray and brain MRI analysis), bioinformatics algorithms for genome rearrangement, quantum computing security implications, and multi-robot coordination systems. His methodology consistently combines theoretical algorithm development with practical implementation, often leveraging advanced optimization techniques and parallel computing approaches. Key research projects include: Getting American Indians to Information Technology (GAIN-IT) as Co-PI (2008-2013) A Computational Intelligence to Gene Structure Prediction as PI (2007-2008) South Dakota Biomedical Research Infrastructure Network as Co-PI (2001-2012) Throughout his career, Professor Asaithambi has directed numerous doctoral dissertations and Master's theses while teaching diverse courses from introductory programming to quantum computing. His teaching philosophy emphasizes critical thinking development for both traditional and non-traditional student populations, with particular attention to mathematical preparation challenges in computational courses.

Hyacinth I. HyacinthView profile

Professor

• Stroke Disparities
• Neuroscience
• Genetic Epidemiology
• +4 more

Hyacinth I. Hyacinth is a Professor at the University of Cincinnati, affiliated with the Department of Neurology. His research focuses on the biological basis of disparities in stroke, cognitive impairment, and dementia, particularly examining the role of sickle cell mutations and genetic variants in cerebral macro/microvascular pathologies among individuals of African ancestry. Education: PhD (Walden University), Postdoctoral training (Medical University of South Carolina, Emory University, Morehouse School of Medicine), MPH (University of Liverpool), MD (University of Jos) His work integrates genetic epidemiology, neurovascular biology, and hematology to define mechanisms linking genetic variation to differential disease risk. Recent studies include neuroinflammation in sickle cell mice, perivascular space quantification in SCD patients, and genomic risk scores for stroke prediction. Scientific contributions include peer-reviewed publications in JAMA Neurology , Nature , Stroke , and Experimental Biology and Medicine . He holds a FAHA fellowship from the American Heart Association.

• Natural Language Processing
• Clinical Text Mining
• Artificial Intelligence
• +7 more

Hercules Dalianis is a Professor at the Department of Computer and Systems Sciences, Stockholm University. His research focuses on Natural Language Processing, particularly in clinical text mining for Swedish language data. He leads the Natural Language Processing Research Group and serves as director of the Health Bank - Swedish Health Record Research Bank infrastructure. MSc in Electrical Engineering (1984), KTH PhD in Technology (1996), KTH Professor of Computer and Systems Science (2011), Stockholm University His research addresses privacy-preserving NLP for clinical text analysis, including automated de-identification , domain adaptation of BERT models , and clinical entity recognition . Current projects like DataLEASH and Privacy-Preserving Techniques explore machine learning solutions that balance data utility with patient confidentiality. Key publication trends show emphasis on Swedish clinical text processing , ICD-10 coding automation , and privacy-aware language modeling . Collaborations span Karolinska University Hospital, Nordic healthcare institutions, and international AI research communities. He teaches courses in Internet Search Techniques and Business Intelligence (ISBI) , Natural Language Processing (NLP) , and Principles and Foundations of Artificial Intelligence (PFAI) . His work has produced the open-access textbook Clinical Text Mining: Secondary Use of Electronic Patient Records , establishing foundational frameworks for clinical NLP in low-resource languages.

Danielle MoweryView profile

Assistant Professor

• Natural Language Processing
• Knowledge Representation
• Patient Phenotyping
• +3 more

Danielle Mowery is an Assistant Professor at the Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania and serves as the Chief Research Information Officer (CRIO) at Penn Medicine. She also directs the IBI Clinical Research Informatics Core, focusing on leveraging technology to enhance clinical research infrastructure. Her research interests span natural language processing , knowledge representation , patient phenotyping , clinical research services , clinical/translational informatics , and learning health systems . These areas center on improving disease understanding, treatment efficacy analysis, and patient outcomes through computational methods. Recent publications highlight her work in large language models for clinical text analysis , multinational cohort studies for COVID-19 and long-term outcomes , and machine learning applications in dermatology and mental health . Her research emphasizes health equity , data harmonization , and automated health systems . FAMIA (Fellow of the American Medical Informatics Association)

Brian L. StromView profile

Professor

• Pharmacoepidemiology
• Drug Safety
• Public Health
• +3 more

Brian L. Strom is a Professor of Epidemiology at the University of Pennsylvania’s Perelman School of Medicine, affiliated with the Department of Biostatistics, Epidemiology, and Informatics (DBEI). His academic career spans over four decades, with a focus on pharmacoepidemiology, drug safety, and public health outcomes. Strom earned his M.D. from Johns Hopkins University (1975), M.P.H. from UC Berkeley (1980), and B.S. from Yale University (1971). His research emphasizes drug utilization, safety, and efficacy, particularly in chronic disease populations such as diabetes, rheumatoid arthritis, and pediatric conditions. Key areas include vaccine effectiveness, antibiotic exposure impacts, and pandemic health effects. Collaborators include prominent figures like James D. Lewis (Professor of Medicine and Epidemiology) and Meenakshi Bewtra (Associate Professor of Medicine and Epidemiology). Strom’s work bridges clinical and population health, addressing real-world drug outcomes and healthcare policy. Recent studies explore opioid-benzodiazepine mortality risks in veterans and antibiotic effects on pediatric chronic diseases. He has contributed to methodological advancements in algorithm validation for disease identification and public health surveillance. Educational Background: B.S., Yale University, 1971 M.D., Johns Hopkins University, 1975 M.P.H., University of California, Berkeley, 1980 Research Collaborations: James D. Lewis (Medicine & Epidemiology) Meenakshi Bewtra (Medicine & Epidemiology) Gregory Tasian (Urology & Epidemiology) Methodological Expertise: Pharmacoepidemiological study designs Health outcomes research Algorithm development for disease detection His publications span high-impact journals like Chest , Contraception , and Diabetes Care , reflecting interdisciplinary contributions to drug safety and public health. Strom’s work has informed FDA guidelines and healthcare policy, emphasizing evidence-based practices in clinical and population settings.

• Clinical Decision Support Systems
• Knowledge Management
• Personalized Medicine
• +3 more

Dr. Kensaku Kawamoto is a Professor and Vice Chair of Clinical Informatics at the University of Utah Department of Biomedical Informatics, and Associate Chief Medical Information Officer at University of Utah Health. He leads the ReImagine EHR initiative, developing award-winning EHR add-ons like neonatal bilirubin management and lung cancer screening tools. He chairs the Clinical Decision Support committee and co-leads the HL7 Clinical Decision Support Work Group. His research focuses on clinical decision support systems, personalized medicine, and health IT standards. Education: B.A. in Biochemical Sciences, Harvard University M.D., Ph.D. in Biomedical Engineering (focusing on informatics), Duke University M.H.S. in Clinical Research, Duke University School of Medicine Key Roles: HL7 Interoperability Standards Priorities Task Force Co-Chair Past Board Member of HL7 Fellow of American College of Medical Informatics & AMIA His research interests emphasize clinical decision support , standards development , and healthcare equity . Recent work addresses disparities in genetic testing access via chatbot platforms (BRIDGE trial) and lung cancer screening optimization. He has secured $55M+ in grants from NCI, NIDDK, AHRQ, and industry. Notable achievements include 100+ peer-reviewed articles, OpenCDS initiative leadership, and recognition as a Top 25 Healthcare Innovator (2019). Current projects include the MAINTAIN PRIME weight management trial and lung cancer screening tools integrated into EHR workflows. Dr. Kawamoto's labs focus on interoperable clinical decision support, with collaborations spanning Huntsman Cancer Institute and national standards bodies. He advocates for precision medicine enabled by scalable informatics solutions.

Matthew IvesonView profile

Research Fellow

• Mental Health Data Science
• Cognitive Ageing
• Antidepressant Treatment Response
• +3 more

Matthew Iveson is a Senior Research Fellow at the Centre for Clinical Brain Sciences (University of Edinburgh) with a focus on mental health data science, cognitive ageing, and administrative data linkage. He holds a PhD in Psychology (2015) from the University of Edinburgh and Suor Orsola Benincasa University, an MSc by Research (2010), and an MA (Hons.) in Psychology (2009). Research Interests : Mental health prediction using EHRs, antidepressant response phenotyping, treatment resistance, dementia risk modeling, and socioeconomic determinants of mental health. Grants : 2024–2025: RDS Accelerator Award (PI) on 'Antidepressant exposure and resistance' 2023–2028: Wellcome Mental Health Award (Work Package 2 lead) for 'Understanding antidepressant mechanisms' 2018–2020: MRC Mental Health Pathfinder Award (Work Package 1) on intergenerational mental health patterns Scientific Awards : JSPS Post-doctoral Fellowship (2015–2016) in Kyoto, Japan. Knowledge Exchange : Advocates for NHS data utilization, develops best practices for mental health data science, and collaborates with policymakers to enhance administrative data resources.

• Information Retrieval
• Text Mining
• Geographical Information Sciences
• +3 more

Bruno Martins is an Associate Professor at Instituto Superior Técnico (IST) of the University of Lisbon, affiliated with the Human Language Technologies Lab at INESC-ID and the Lisbon ELLIS Unit (LUMLIS). His research focuses on information retrieval, text mining, geographical information sciences, and machine learning. He leads projects on geospatial aspects of information access and integrates NLP, ML, and GIS methodologies. Research Interests: Geographic Information Retrieval (GIR) Text Mining & Natural Language Processing (NLP) Machine Learning Applications in Geospatial Analysis Information Extraction from Historical Documents Recent Research Trends: Recent work emphasizes deep learning for geospatial tasks (e.g., population distribution modeling), multimodal systems (medical VQA, remote sensing captioning), and computational social science (political discourse analysis). His publications address challenges in geocoding, spatial disaggregation, and multilingual NLP. Advising & Projects: Supervised over 50+ students across PhD, MSc, and postdoc levels. Key projects include the EU-funded DETECT (epidemiological surveillance), MATISSE (shellfish safety forecasting), and DigCH (historical Mexican documents analysis). Active in initiatives like GeoAI and Geospatial Humanities workshops. Labs & Teams: Directs research at INESC-ID’s Human Language Technologies Lab and participates in the Lisbon ELLIS Unit. Collaborates with international teams on vision-language models, geospatial AI, and computational humanities.

• Infectious Diseases
• Sepsis Pathophysiology
• Immunology
• +7 more

Professional Overview Jan Kristian Damås is a Professor at the Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU). His research focuses on sepsis pathophysiology, infectious diseases, and translational epidemiology, with particular emphasis on leveraging genomics and machine learning for clinical prediction. Research Interests His work spans multiple domains including: Infectious Disease Mechanisms : Investigating immune responses to pathogens like scrub typhus and pneumocystis pneumonia Sepsis Outcomes : Developing predictive models for mortality and long-term morbidity using electronic health records Genetic Epidemiology : Identifying risk loci for infections through Mendelian randomization and GWAS Clinical Informatics : Optimizing diagnostic coding practices and healthcare data systems Key Contributions Recent work includes: Systematic review of machine learning approaches for bloodstream infection prediction Nationwide registry studies on sepsis trends and outcomes Discovery of canonical notch pathway dysregulation in scrub typhus patients Awards & Collaborations Collaborates extensively with clinical teams at St. Olavs Hospital and international groups. Active in translational research bridging basic immunology and clinical practice.

• Health Services Management
• Strategic People Management
• Organizational Change
• +3 more

Generosa of Birth is a Professor in the Department of Human Resources and Organizational Behavior at ISCTE - University Institute of Lisbon. She leads the Executive Master's in Strategic People Management and Leadership , Executive Master's in Health Services Management , and Postgraduate Program in Management for Health Professionals through ISCTE Executive Education. Research Focus: Her work bridges Health Services Management and Strategic People Management , emphasizing leadership impacts, organizational transformation, and non-monetary workforce incentives. She has consulted extensively on organizational change across public, private, and social sectors. Academic Contributions: Recent publications explore pandemic-driven organizational shifts (2021-2024), AI applications in clinical coding (2024), and contextual ambidexterity in public institutions (2021). Her studies span healthcare sustainability ( Bloco Operatório Verde , 2021), lean healthcare (2018), and performance management systems (2018). Expertise: She combines theoretical rigor in organizational behavior with practical healthcare management applications, balancing broad disciplines like Public Health and Leadership with niche subfields such as ICD-10 coding AI and integrated ENT service centers.

Mohammad Nazmul HaqueView profile

Lecturer

• Data Analytics
• Evolutionary Computing
• Machine Learning
• +5 more

Dr. Mohammad Nazmul Haque is an Associate Lecturer at the School of Information and Physical Sciences, University of Newcastle, Australia, where he also holds a Casual Academic position. His academic career spans both Australian and Bangladeshi institutions, with significant contributions to data analytics, evolutionary computing, and machine learning research. Dr. Haque earned his Doctor of Philosophy in Computer Science from the University of Newcastle in February 2017. Prior to this, he completed his B.Sc and M.Sc in Computer Science & Engineering from Daffodil International University (DIU), Dhaka, Bangladesh in 2006 and 2011, respectively. His academic journey includes lecturing positions at Daffodil International University (2009-2012) and Daffodil Institute of IT (2007-2009) before commencing his PhD studies. His research focuses on innovative applications of continued fractions for regression methods using memetic algorithms, with applications spanning astronomy, scientific functions, and predictions. He has extensive interdisciplinary experience in data analytics from diverse data sources including gene expression, business and consumer behavior, and images. His work bridges theoretical computer science with practical applications in health informatics, network analysis, and complex systems. Dr. Haque's publication record demonstrates a consistent focus on continued fractions, memetic algorithms, and ensemble methods across diverse domains. His recent work shows increasing application of these techniques to physical sciences, materials science, and even digital humanities, reflecting his interdisciplinary approach. The publications reveal strong collaboration with Professor Pablo Moscato and others across multiple disciplines. ACM-Solver Coding Championship (2005) University of Newcastle International Postgraduate Research Scholarship (2012) University of Newcastle Research Scholarship Central (2012) Chartered Professional Engineer (CPEng) from Engineers Australia (2025) Senior Member of IEEE (2021) ACS Certified Professional (2024) Dr. Haque has successfully supervised seven Honours/Masters students with projects ranging from computer vision applications to data mining and software engineering. He has secured research funding totaling $16,445, including a $14,945 grant from Hunter Water Corporation for data science methods to cluster consumer water consumption. His professional memberships include IEEE Senior Member, ACM Member, and Australian Computer Society certifications, reflecting his standing in the computing community. As part of the University of Newcastle's research ecosystem, Dr. Haque contributes to collaborative projects with international reach, evidenced by his publications with co-authors from Australia, United States, Canada, Malaysia, Bangladesh, UK, and South Korea. His work with the Data Science and Statistics group continues to explore innovative mathematical representations for complex data analysis problems.