Find Academic Supervisors

Karen StendalView profile

Professor

• Information Systems
• Health Technology
• Extended Reality (XR)
• +3 more

Karen Stendal is a Professor at the Department of Economics, Marketing and Law within the USN Business School at the University of South-Eastern Norway. She holds a PhD in Information Systems from the University of Agder (2010–2014), complemented by studies at the University of Nebraska at Omaha and the University of Georgia. Her research focuses on Extended Reality (XR), virtual team collaboration, health technology, and accessibility for individuals with disabilities. Key projects include the I-Merse research group and the Science Center for Health and Technology. Her academic career spans roles from Assistant Professor at the University of Agder (2013) to her current position since 2022. She has contributed to over 50 publications, with recent work emphasizing XR's impact on mental health, environmental engagement, and workplace innovation. Notable projects explore virtual nature interventions, ethical considerations in XR for disabilities, and agile development in virtual settings. Dr. Stendal’s research bridges technology and human-centered applications, addressing challenges in healthcare, education, and disability inclusion. She actively supervises PhD candidates and collaborates with institutions like NHH Norwegian School of Economics and the Science Center for Health and Technology.

Daniel A. AdamoView profile

Assistant Professor

• Abdominal Imaging
• Interventional Radiology
• CT Guided Procedures
• +5 more

Daniel A. Adamo, M.D. is an abdominal radiologist at Mayo Clinic in Rochester, Minnesota, specializing in abdominal imaging and intervention including CT and US guided procedures. He is affiliated with Mayo Clinic College of Medicine and Science as part of the Department of Radiology within the College of Medicine. Dr. Adamo completed his medical education at University of Illinois College of Medicine (MD, 2013), with undergraduate studies in Biology at Vanderbilt University (BS, 2007) and Medical Science at Boston University (MA, 2009). His clinical training includes a fellowship in Cross-Sectional Imaging (2019) and residency in Diagnostic Radiology (2018), both at Mayo Clinic. His research focuses on abdominal imaging, interventional radiology techniques, tumor ablation therapies, and MRI-guided procedures. His work spans applications in prostate cancer, liver tumors, kidney cancer, and other abdominal malignancies, with emphasis on minimally invasive image-guided interventions. Dr. Adamo's publication record shows consistent contributions to radiology literature, with recent work focusing on percutaneous ablation techniques, MRI-guided therapies, and diagnostic imaging methodologies. His research demonstrates expertise in both clinical applications and technical aspects of abdominal imaging. Excellence in Education Society of Abdominal Radiology (2023) Summa Cum Laude Interventional MRI Symposium (2022) Certificate of Merit Radiological Society of North America (2019) Chief Resident Mayo Clinic Department of Radiology (2016) Dr. Adamo is actively involved in professional organizations including the Society of Abdominal Radiology, American Roentgen Ray Society, and Radiological Society of North America. His educational contributions are recognized through his Excellence in Education award and ongoing participation in residency training programs at Mayo Clinic.

Xuanjun GongView profile

Assistant Professor

• Media Selection
• Computational Modeling
• Communication Networks
• +4 more

Xuanjun (Jason) Gong is an Assistant Professor in the Department of Communication and Journalism at Texas A&M University. His research program bridges computational modeling, media psychology, and neuroscience to investigate sequential media selection, information diffusion, and neural correlates of media engagement. He employs diverse methodologies including fMRI, behavioral experiments, and large-scale social media analysis. Gong's research focuses on the computational mechanisms of media choice, exploring how curiosity, mood, and cognitive processes shape decisions in dynamic media environments. His work formalizes theories like mood management and flow states using drift-diffusion models and network neuroscience approaches. Key themes include prediction of media behaviors, cross-platform information spread, and neurocognitive foundations of audience engagement. His publications demonstrate consistent emphasis on developing integrative frameworks that combine communication theory with computational rigor. Recent work advances understanding of time-dependent media selection, neural dynamics during flow experiences, and social media discourse patterns during exogenous events. Gong's research has been recognized with a Graduate Student Award (CNS21). Awards & Honors: Graduate Student Award, CNS21 (2021)

• Genetics
• Nephrology
• Endocrinology
• +5 more

Rany Salem is an Associate Professor at the Herbert Wertheim School of Public Health & Human Longevity Science School Vc-health Sciences-schools, University of California San Diego. His research focuses on genetic and epigenetic mechanisms underlying complex diseases, particularly diabetic kidney disease, endocrine disorders, and PTSD. He leads NIH-funded projects investigating longitudinal genetic analysis methodologies. Education details are not explicitly stated, though his work spans genomic epidemiology and clinical genetics. Research interests include gene-environment interactions, metabolic syndromes, and public health applications of genetic insights. Publications highlight contributions to kidney disease genetics, autoimmune conditions, and pediatric endocrinology. Grants include NIH R00HL122515 (2017-2020) and K99HL122515 (2014-2016) as Principal Investigator. Collaborations involve international consortia like SUMMIT and GENIE. No specific labs or teams are named, but his work integrates multi-omics data with clinical studies. Mentoring and training activities are noted under 'Faculty Mentoring' in his profile.

Daniel DohView profile

Senior Lecturer

• Social Policy
• Ageing and Health
• Long-Term Aged Care
• +5 more

Dr. Daniel Doh is a Senior Lecturer in Social Work and Social Policy at the University of Western Australia (UWA), based in the School of Allied Health. Previously, he held roles at the University of Western Sydney, Edith Cowan University, and the University of Ghana, where he led the Master of Social Policy program. He specializes in policy-engaged research, mixed-methods evaluation, and quantitative modeling/qualitative analysis. His work focuses on aging populations, disability care, child protection, migration, and policy design. Education: PhD in Social Policy (Edith Cowan University, 2017), MPhil in Social Work (University of Ghana, 2010). He has collaborated with UNICEF, HelpAge Ghana, and governments in West Africa to shape social protection policies for vulnerable groups. His current research includes evaluations of Australia's Hospital-in-the-Home program and migrant care systems. Teaching: Coordinates core units in social work research, policy analysis, and human development. Advocates collaborative, student-centered learning strategies. Key Research Themes: Long-term care systems, informal caregiving, migration's impact on health, and randomized control trials. His work aligns with UN Sustainable Development Goals on health (SDG3), education (SDG4), and reduced inequalities (SDG10). Publications: 19 peer-reviewed articles focusing on elder abuse, nutritional outcomes in aging populations, and migrant health challenges. Recent work emphasizes global health equity and policy innovation.

Torsten BraunView profile

Professor

• Machine Learning
• Networking and Telecommunications
• Edge Computing
• +3 more

Prof. Torsten Braun is a Professor and Head of the Communication and Distributed Systems (CDS) research group at the Institute of Computer Science, University of Bern. His research focuses on advanced networking technologies, edge computing, and machine learning applications in telecommunications. He leads projects addressing challenges in 5G/6G networks, federated learning optimization, and intelligent systems for smart cities. His work integrates theoretical frameworks with practical implementations, emphasizing distributed systems, service-oriented architectures, and IoT security. Key research areas include edge caching strategies for VR/AR applications, trajectory prediction using reinforcement learning, and resilient network design against jamming attacks. Braun has contributed to innovations in vehicular networks (V2X), RAN intelligence, and decentralized machine learning frameworks. He holds leadership roles in developing adaptive resource management systems for edge-cloud environments and has pioneered solutions for energy-efficient federated learning in heterogeneous IoT ecosystems. Publications span topics like spatial-temporal point cloud sensing, mobility-aware service orchestration, and secure positioning systems. His team explores cross-disciplinary applications such as LoRaWAN-based urban heat monitoring and blockchain-inspired public key infrastructures for IoT (Veritaa-IoT). Braun actively engages in standardization and industry collaborations to advance next-generation network architectures.

Kay BusseyView profile

Professor

• Developmental Psychology
• Clinical Psychology
• Bullying and Cyberbullying
• +7 more

Professor Kay Bussey is affiliated with Macquarie University’s School of Psychological Sciences and the Lifespan Health and Wellbeing Research Centre. She holds roles as Unit Convenor in courses like Applied Child and Adolescent Psychology and Advanced Issues in Child Abuse and Neglect. Her research focuses on bullying, cyberbullying, mental health outcomes in adolescents, gender identity development, and the impact of social media on youth. She has collaborated internationally, including a postdoctoral fellowship at Stanford University under Albert Bandura and ongoing involvement as a Visiting Scholar. Education: Kay earned her PhD in Developmental and Clinical Psychology from the University of Queensland, preceded by studies at the University of Queensland and University of Oregon (USA). She also completed a Postdoctoral Fellowship at Stanford University. Research Interests: Kay’s work centers on Bandura’s social cognitive theory, examining sociocognitive factors like self-efficacy and moral disengagement in bullying contexts. She investigates mental health outcomes such as anxiety, depression, and eating disorders linked to victimization, as well as strategies to empower bystanders as defenders. Her studies also address gender identity development, children’s lie-telling behavior, parenting practices (including physical punishment), legal system participation of children, and social media’s dual role in development and body image issues. Scientific Awards: Kay has received notable recognitions including the Commonwealth Postgraduate Research Award (1975), Commonwealth Undergraduate Scholarship (1971), Early Career Award (1982), Excellence in Research for Supervision (2015), and a Fulbright Fellowship (1977). Grants and Advising: She leads projects such as ‘Reducing Cyberbullying: Turning Bystanders into Constructive Defenders’ (2022–2025) and collaborates on studies like ‘Prevalence of Eating Disorders in Adolescent Australians’ (2017–present). Her supervision spans PhD and master’s students researching topics from moral responsibility to internet pornography’s impact. She actively contributes to academic committees and editorial boards, including the British Journal of Developmental Psychology and the Psychology of Men and Masculinities Journal. Labs and Teams: Kay is part of Macquarie University’s Centre for Emotional Health and Centre for Children’s Learning in a Social World. She advises the Royal Commission into Child Sexual Abuse and participates in BRNET (Bully Research Network), leveraging interdisciplinary networks to address bullying and child welfare challenges.

Jake KendrickView profile

Lecturer

• Medical Imaging
• Artificial Intelligence
• Machine Learning
• +6 more

Jake Kendrick is a Lecturer in the Department of Computer Science and Software Engineering at The University of Western Australia (UWA), within the School of Physics, Mathematics and Computing. He holds a PhD in medical physics and has expertise in artificial intelligence applied to medical imaging. Education: BSc (Engineering Science & Pharmacology), Diploma of Physics, MSc (Medical Physics), and PhD (Medical Physics). Research focuses on AI-driven medical imaging techniques for precision medicine, including automated cancer detection, radiomics for survival prognosis, and imaging biomarkers. He leads UWA’s Machine Learning in Cancer Research group (awarded 2022 for enhancing learning) and participates in the ACPSEM AI Working Group. Recent articles emphasize AI in oncology imaging, meta-analyses of radiomics quality, and clinical radiotherapy applications. Awards include the ACPSEM Best PhD Award (2024) and contributions to student learning initiatives. Active grants focus on prostate cancer prognosis, generative AI for cancer imaging, and clinical AI integration. He collaborates internationally and has been featured in media for AI’s real-world impact.

Marije VerhageView profile

Associate Professor

• Developmental Psychology
• Parent-Child Attachment
• Meta-Analysis
• +4 more

Marije Verhage is an Associate Professor at the Vrije Universiteit Amsterdam, holding dual affiliations in the Clinical Child and Family Studies department and the APH - Mental Health research group. She also contributes to the LEARN! - Child Rearing initiative. Her research focuses on developmental psychology, parent-child attachment dynamics, and meta-analytic methodologies. She leads the Zooming in on the 'transmission gap' project investigating intergenerational transmission of attachment through individual participant data. Her work addresses topics such as parenting self-efficacy, child temperament, and maternal/father sensitivity using advanced statistical techniques. Key contributions include studies on fear of childbirth’s impact on parenting behavior and the role of attachment quality in child language competence. Received the APA Early Career Outstanding Paper award (2016) Active in interdisciplinary collaborations, including international meta-analyses Teaches Wetenschappelijk Redeneren met de Gehechtheidstheorie (Scientific Reasoning with Attachment Theory) Publications span peer-reviewed journals like Developmental Psychology and Child Development , with open-access contributions emphasizing methodological rigor. Media engagement includes discussions on parenting uncertainties and child-rearing challenges.

Marcus RohrbachView profile

Professor

• Computer Vision
• Computational Linguistics
• Machine Learning
• +1 more

Marcus Rohrbach is a Full Professor at Technische Universität Darmstadt, leading the 'Multimodal Reliable AI' lab. Previously, he served as a Research Scientist at Meta AI (2017-2023), PostDoc at UC Berkeley (2014-2017), and completed his PhD at Max Planck Institute for Informatics. His research focuses on computer vision, computational linguistics, and developing trustworthy AI systems. His work explores multimodal learning, video understanding, and improving AI reliability. Recent publications demonstrate strong emphasis on video-language integration, fact verification systems, and developing methods for AI abstention when uncertain. Research consistently addresses challenges in cross-modal alignment and trustworthy reasoning. Dr. Rohrbach maintains an active publication record in top-tier venues, with recent work focusing on video instruction understanding, multimodal fact-checking, and efficient pretraining techniques. His contributions advance both theoretical frameworks and practical applications in trustworthy vision-language systems.

Zhibin ChenView profile

Research Fellow

• Neurology
• Epidemiology
• Health Economics
• +3 more

Dr Zhibin (Ben) Chen is a Senior Research Fellow in the Department of Neuroscience at Monash University's School of Translational Medicine. He holds dual expertise as a neuroscientist and Chartered Statistician (Royal Statistical Society, UK). His research focuses on statistical modeling, epidemiology, and health economics applied to neurological diseases, particularly epilepsy, traumatic brain injury, and dementia. He secured $6.3 million in grants as Chief Investigator, including NHMRC Early Career Fellowship (2018–2022). Dr Chen leads over 50 cohort studies and clinical trials, authoring 75 articles in journals like JAMA Neurology and BMJ . Education: PhD in Clinical Pharmacology, University of Melbourne (2016) Master of Biostatistics, University of Sydney (2013) Bachelor of Science (Honours) in Neuroscience, University of Melbourne (2012) Bachelor of Commerce (Accounting), Macquarie University (2007) Research Interests: Chen’s work integrates machine learning, clinical epidemiology, and health economics to improve epilepsy management. Key areas include: Modeling treatment responses to antiseizure medications Predicting seizure outcomes using deep learning Evaluating the economic burden of neurological disorders Optimizing personalized therapy strategies Grants & Awards: NHMRC Early Career Fellowship (GNT1156444) IDEAS Grant (GNT2010382-CIB) Clinical Trials Grant (GNT2014800-CIB) Collaborations: Active partnerships with institutions like the University of Melbourne, Epilepsy Foundation Australia, and international epilepsy networks. Leads projects such as the PERSONAL trial for personalized epilepsy medication selection. Labs/Teams: Oversees interdisciplinary teams combining neurology, biostatistics, and health economics to advance translational research in epilepsy management.

Mengchu LiView profile

Assistant Professor

• Change Point Analysis
• High-Dimensional Statistics
• Robust Statistics
• +3 more

Roles & Affiliations: Dr. Mengchu Li is an Assistant Professor in the School of Mathematics at the University of Birmingham. Previously, he served as a Postdoctoral Research Fellow in the Department of Statistics at the University of Warwick (Feb 2023–Sept 2024), and completed his PhD in Statistics at Warwick under Prof. Yi Yu. Education: PhD in Statistics, University of Warwick (2023) MASt in Mathematical Statistics, University of Cambridge (2019) BSc in Mathematics and Economics, Durham University (2018) Research Interests: Focuses on statistical theory and methodology for heterogeneous data analysis, particularly in high-dimensional settings and under privacy constraints. Key areas include change point analysis, robust statistics, differential privacy, and transfer learning. His work emphasizes minimax optimality and explores fundamental trade-offs between data contamination, privacy, and statistical performance. Research Trends in Articles: Recent work spans federated learning with privacy guarantees, robust change point testing in high-dimensional settings, and privacy-preserving network analysis. His papers often bridge statistical theory with applications in machine learning and distributed systems. Awards & Honors: Harrison Award (2023) for highly commended PhD thesis NeurIPS 2022 Scholar Award Teaching & Service: Taught graduate courses on statistical inference and computation at Birmingham. Previously led tutorials in mathematical statistics and linear modeling at Warwick. Active reviewer for Annals of Statistics , Biometrika , NeurIPS , and other top venues. Contributed to the R package changepoints for change-point detection methods. Labs & Collaborations: Engaged in interdisciplinary research through workshops on heterogeneous data (2024) and change point analysis (2023). Collaborates with institutions like the Alan Turing Institute and Warwick’s Statistics department.

• Medical Image Segmentation
• Motion Segmentation
• Visual Object Tracking
• +9 more

Dr. Sandeep Singh Sengar is a Senior Lecturer in Computer Science at Cardiff Metropolitan University, United Kingdom. Prior to his current role, he served as a Postdoctoral Research Fellow at the Machine Learning Section of the Computer Science Department at the University of Copenhagen, Denmark. His academic journey includes a Ph.D. in Computer Science and Engineering from Indian Institute of Technology (ISM), Dhanbad, India, and an M. Tech. in Information Security from Motilal Nehru National Institute of Technology, Allahabad, India. Sengar actively engages in research collaboration and has organized special sessions and delivered keynote presentations at international conferences. Education: Dr. Sengar holds the following academic qualifications: Ph.D. in Computer Science and Engineering from Indian Institute of Technology (ISM), Dhanbad, India M. Tech. in Information Security from Motilal Nehru National Institute of Technology, Allahabad, India Research Interests: Dr. Sengar's research focuses on advanced applications of machine/deep learning and computer vision, particularly in healthcare diagnostics and cybersecurity. His work combines theoretical methodologies with practical implementations, including: Medical Image Segmentation (e.g., brain tumors, functional connectivity) Motion Segmentation and Tracking for surveillance and activity recognition Visual Object Tracking and Recognition in dynamic environments Video Compression techniques leveraging adaptive optimization Cybersecurity frameworks targeting DDoS attack mitigation Exploring Metaverse applications in healthcare and AI ethics Publications Trends: His recent articles emphasize interdisciplinary innovation, with a strong focus on healthcare AI and cybersecurity. Key themes include hybrid neural networks for medical imaging, entropy-driven frameworks for neurological disorders, and proactive network threat detection using deep learning. He also investigates emerging technologies like the Metaverse's role in healthcare systems and generative AI's broader applications. Scientific Awards: No scientific awards were explicitly mentioned in the provided texts. Advising & Grants: While Dr. Sengar has not listed formal advisees or grant details, his research mentorship is evident through collaborative projects and conference contributions. He actively participates in organizing special sessions and peer-review processes, fostering academic-industry partnerships. Labs & Teams: Though no specific labs are named, his work aligns with Cardiff Met's research centers and innovation initiatives, such as the PDR International Centre for Design and Research and ZERO2FIVE Food Industry Centre.

Ania MajewskaView profile

Assistant Professor

• Biology Education Research
• Infectious Disease Ecology
• Undergraduate Research Experience
• +5 more

Ania Majewska is an Assistant Professor in the Department of Physiology and Pharmacology at the University of Georgia's College of Veterinary Medicine. She holds a PhD in Ecology from the University of Georgia, followed by a postdoctoral fellowship in Research and Science Teaching at Emory University. Her research spans two major areas: biology education innovation and infectious disease ecology. In education, she focuses on course-based undergraduate research experiences (CUREs), AR/VR applications for anatomy/physiology learning, and culturally responsive teaching strategies. In disease ecology, her work examines how non-native species and human activities influence pathogen spread, with a focus on monarch butterflies, helminths like Guinea worm, and zoonotic risks in wildlife and shelter animals. Her educational background includes a BS in Biology (Boston University), MS in Wildlife Biology (University of Montana), and a PhD in Ecology (UGA). She has pioneered studies on monarch butterfly conservation, including parasite dynamics, larval diet impacts on flight performance, and the effects of non-native milkweed on population health. Her lab also investigates copepod consumption patterns in relation to Guinea worm transmission and has developed innovative tools like the Gutsy game to teach microbiome concepts. Recent research highlights include spatial analyses of vector-borne pathogens in shelter dogs, clinical trials for Guinea worm treatments, and reviews of experimental approaches in monarch conservation. Her work bridges basic science with applied solutions, emphasizing One Health principles and community engagement. The Majewska lab collaborates across disciplines to address ecological and educational challenges in veterinary medicine and biology education.

Gabriela FerraroView profile

Research Fellow

• Natural Language Processing (NLP)
• Computational Linguistics
• Machine Learning for Text Processing
• +6 more

Gabriela Ferraro is a Research Fellow at the School of Cybernetics, Australian National University , specializing in Natural Language Processing (NLP) and Computational Linguistics . Her work focuses on multidisciplinary applications for social good, including: Text simplification and language learning technology Mental health detection through language analysis Preservation of endangered languages E-health systems development In 2021, she was awarded the Woman in Science Career Award by the Commonwealth Scientific and Industrial Research Organization (CSIRO). She actively participates in academic leadership as a member of the Australasian Language Technology Association Committee . Her recent research spans: Transfer learning for hate speech detection Continual learning with noisy data Transformer-based semantic parsing Context-aware neural optimization Lexical taxonomy construction Scientific Awards : Woman in Science Career Award (CSIRO) Gabriela combines technical expertise with ethical priorities, leading projects that intersect NLP, healthcare, and environmental monitoring. She also emphasizes teaching and student supervision as integral parts of her academic practice.