Find Academic Supervisors

• Biostatistics
• Clinical Trials
• Pediatric Oncology
• +4 more

Willem H Collier is a Clinical Assistant Professor in the Department of Population and Public Health Sciences at the University of Southern California. His work focuses on the design, conduct, and analysis of pediatric cancer clinical trials through the Children’s Oncology Group, with an emphasis on supportive care, survivorship, and cellular therapy. Additionally, he specializes in statistical methods for surrogate endpoint evaluation, particularly Bayesian hierarchical modeling for meta-regression, in collaboration with the Chronic Kidney Disease Epidemiology Collaboration. Department: Population and Public Health Sciences Specialties: Biostatistics, Pediatric Oncology, Surrogate Endpoint Analysis Affiliations: University of Southern California, Children’s Oncology Group, Chronic Kidney Disease Epidemiology Collaboration Research Interests include developing advanced biostatistical techniques for surrogate endpoints in heterogeneous clinical trials. His work bridges methodological innovation with real-world applications in pediatric oncology and chronic kidney disease, focusing on improving trial efficiency and patient outcomes. He also investigates healthcare utilization patterns in nonelderly adults and postsurgical complications in plastic surgery contexts. Scientific Awards and honors are not explicitly mentioned in the provided text. Advising details are unavailable, but his collaborations with major clinical research groups indicate significant mentorship and team-based contributions to trial design and analysis.

• Neuroscience
• Biomedical Engineering
• Gerontology
• +6 more

Andrei Irimia, PhD, is an Associate Professor at the University of Southern California, holding cross-departmental appointments in Gerontology, Quantitative & Computational Biology, Biomedical Engineering, and Neuroscience. His research focuses on neuroimaging analysis, traumatic brain injury (TBI), aging, and machine learning applications in healthcare. He leads the ENIGMA Consortium's work on TBI and neurotrauma imaging, coordinating global multi-site studies to advance understanding of brain injury mechanisms and outcomes. His work integrates advanced neuroimaging techniques with computational methods to study TBI sequelae, brain aging, and dementia. Key contributions include developing interpretable AI models for neuroimaging diagnostics and identifying biomarkers for post-traumatic epilepsy and cognitive decline. He has authored over 200 peer-reviewed articles, with recent work emphasizing connectomic profiling, neuroanatomical correlates of aging, and cross-cultural brain morphology studies. Dr. Irimia’s research also explores the intersection of oral health, systemic inflammation, and brain volume in indigenous populations. He collaborates internationally through ENIGMA working groups on military TBI, sports injury, and intimate partner violence-related trauma. His lab develops neuroimaging tools like the LONI QC system for data quality control and 3D-printed medical devices for localized cavitation studies.

• Health Inequalities
• Public Health
• Global Health
• +3 more

Eliud Kibuchi is a Research Fellow at the MRC/CSO Social and Public Health Sciences Unit at the University of Glasgow. He holds a PhD in Social Statistics from the University of Southampton, an MSc in Environmental Statistics from the University of Glasgow, and a BSc in Applied Statistics with Information Technology from Maseno University, Kenya. His research focuses on statistical and survey methods applied to understanding health inequalities in public and global health. Key research interests include causal inference methods on linked datasets, drivers of health inequalities in slums of lower/middle-income countries, and survey data quality aspects (mixed-mode, interviewer effects, incentives). He has contributed to over 27 publications since 2014, with recent work examining ethnic health disparities in Scotland during the pandemic and health inequities in urban slums across Kenya, Bangladesh, and Sierra Leone. Teaching includes contributing to the Master of Public Health program at the University of Glasgow, focusing on measurement and bias in survey data. Current grants include studying climate change impacts on mental health in Kenya (2024–2027) and debt's mental health effects in the UK (2024–2025). His work bridges methodological innovation (e.g., Bayesian frameworks, propensity score matching) with applied health equity research, emphasizing transformative approaches to address systemic inequities in urban informal spaces.

• Clinical Epidemiology
• Multimorbidity
• Chronic Diseases
• +2 more

David McAllister is Professor of Clinical Epidemiology and Medical Informatics at the University of Glasgow's Institute of Health and Wellbeing, with honorary consultant role in NHS Scotland. His Wellcome-funded research examines treatment effectiveness in multimorbidity using Bayesian methods combining trial data with healthcare databases. Current projects investigate diabetes complications, frailty trajectories, and cardiovascular outcomes in chronic disease populations. Research leverages routine healthcare data for epidemiological insights into chronic disease management. Publications demonstrate methodological innovation in clinical trial design and population health analysis. He mentors researchers in epidemiology, statistics, and health data science.

• Health Technology Assessment
• Health Economics
• Clinical Evaluations
• +4 more

Olivia Wu is the William R Lindsay Chair of Health Economics and co-Director of the Health Economics and Health Technology Assessment (HEHTA) at the University of Glasgow. She also holds an Adjunct Professorship at Mahidol University's Faculty of Pharmacy in Thailand. Her expertise spans health technology assessment (HTA), health economics, clinical evaluations, and evidence synthesis. She leads the Complex Reviews Synthesis Unit (CRSU), funded by the National Institute for Health and Care Research (NIHR), and has contributed to numerous economic evaluations that inform clinical guidelines and health policy globally. Olivia collaborates internationally, leading research projects worth over £50 million. She advises HTA agencies and has served on the NICE Technology Appraisal Committee and chairs the NIHR HTA Clinical Evaluations and Trials Funding Committee. Committed to mentoring and diversity, she co-leads the Women in Health Economics and Outcomes Research Initiative and supports inclusive environments for early-career researchers. Olivia's work bridges methodological rigor with practical healthcare decision-making, emphasizing equity and innovative evidence use. Her research integrates applied and methodological studies in HTA, economic evaluations, and evidence synthesis, addressing clinical areas like oncology, cardiovascular health, and public health interventions. Collaborations include studies on venous thromboembolism, stroke resource utilization in Thailand, and disinvestment strategies in Malaysia. She has pioneered frameworks for assessing healthcare value, such as the Complex Reviews Synthesis Unit, enhancing evidence synthesis methodologies. Grants and projects focus on advancing HTA methodologies and their real-world application, including cost-effectiveness analyses of treatments like aflibercept for macular degeneration and biosensor-integrated medical devices. Olivia's contributions extend to mentoring academics and policymakers, particularly in regions with limited resources, and advocating for women and underrepresented groups in health economics. Her lab, HEHTA, fosters interdisciplinary research, combining clinical, economic, and policy perspectives to address global health challenges.

• Emotion Regulation
• Psycho-physiology (e.g., Heart Rate Variability)
• Neurodevelopmental Disorders (e.g., ADHD, Autism)
• +3 more

Dr. Alessio Bellato is a Lecturer in Child and Adolescent Mental Health at the University of Southampton and Honorary Assistant Professor at the University of Nottingham Malaysia. His research focuses on emotion regulation, neurodevelopmental conditions (e.g., ADHD, autism), and psycho-physiological mechanisms like heart rate variability. He leads the South East Asia Mental Health Consortium (SEAMHC) and co-edits the JCPP Advances journal. His teaching spans developmental psychopathology, evidence synthesis, and clinical research methods at both undergraduate and postgraduate levels. He supervises PhD students in clinical psychology and mental health research. Recent projects include investigations into digital psychoeducation tools for ADHD and autonomic arousal biomarkers in depression. Dr. Bellato has contributed to over 50 peer-reviewed publications, with a focus on systematic reviews and meta-analyses. His work emphasizes global mental health equity, particularly in South-East Asia, and integrates clinical practice with rigorous evidence synthesis. He has secured funding from the NIHR, Academy of Medical Sciences, and international organizations for collaborative research initiatives.

• Psychometrics
• Neuroscience
• Education Technology
• +4 more

Mariagrazia Benassi is a Full Professor at the Department of Psychology, University of Bologna, leading the Psychometrics and Neuropsychology Lab. She coordinates the PhD Programme in Psychology and focuses on developing psychometric tools for clinical assessment, particularly in learning disorders and neurodevelopmental conditions. Her research integrates advanced statistical models (e.g., Bayesian networks, machine learning) with eye movement analysis and tele-rehabilitation technologies. Collaborations include the BoostPerception and DeduTech-2life groups, addressing topics like gamification in education and ethical AI applications for children. Her work emphasizes digital tools such as PsycAssist (adaptive neuropsychological assessment) and Eye-Riders (cognitive training games). She investigates visual perception deficits in clinical populations and advocates for rigorous methodological frameworks in psychological research. Office hours are held in Cesena and online, with a focus on student training and professional internships in psychometric evaluation. Research highlights include cluster analysis of cognitive profiles, validation of assessment instruments (e.g., MatriKS for fluid intelligence), and cross-disciplinary studies on decision-making influenced by Pavlovian cues. Her contributions bridge psychological theory with technological innovation in education and healthcare.

• Work and Organizational Psychology
• Career Development
• Employability Studies
• +4 more

Marco Giovanni Mariani is an Associate Professor in the Department of Psychology "Renzo Canestrari" at the University of Bologna, specializing in Work and Organizational Psychology (PSIC-03/B). He serves as a Member of the Guarantee Committee for Equal Opportunities, Employee wellbeing and non-discrimination at work. His academic work spans multiple campuses, with offices in both Bologna (Via Ranzani 14) and Cesena (Viale Europa, 109). Dr. Mariani's research focuses on several key areas within organizational psychology. His primary interests include: Career development and employability, particularly among university students and graduates Workplace safety and safety training effectiveness Human resource management practices and their impact on employee wellbeing Psychological aspects of organizational identification and job satisfaction Public policy evaluation, particularly regarding employment and health interventions His recent scholarly output demonstrates a strong focus on contemporary workplace issues. Analysis of his 15 most recent publications (2024-2025) reveals several prominent trends: a significant emphasis on employability and career sustainability research, particularly examining how students and graduates navigate the modern labor market; growing interest in digital transformations of HR practices including virtual assessment centers and e-recruitment; cross-cultural studies of human resource management practices across dozens of countries; and methodological innovations in statistical analysis for organizational research. His work consistently bridges theoretical organizational psychology with practical workplace applications. Dr. Mariani actively contributes to the academic community through committee service and mentorship. While specific details about his supervisees aren't provided in the available information, his research on student employability suggests engagement with graduate students. His work has received support from various research grants focused on employment policy, workplace safety, and career development initiatives. His research methodology spans quantitative approaches including longitudinal studies, meta-analyses, field experiments, and cross-cultural comparative research. Recent projects demonstrate collaboration with international research teams across Europe and beyond, particularly in studies examining organizational practices across multiple countries.

• Quantitative Psychology
• Statistical Methodology
• Research Design
• +5 more

Dr. Jolynn Pek is an Associate Professor in Quantitative Psychology at The Ohio State University. Her expertise lies in statistical methodology, particularly quantifying uncertainty in research design and analysis. She holds a PhD from the University of North Carolina at Chapel Hill and has held academic positions at York University and other institutions. Education: PhD, Quantitative Psychology, University of North Carolina (2012) MS, University of North Carolina (2010) M.A., Applied Psychology, National University of Singapore (2006) B.S., National University of Singapore (2002) Research Interests: Dr. Pek focuses on uncertainty quantification in statistical methods, including power analysis limitations, confidence interval estimation, and fungible parameter estimation. Her work emphasizes bridging methodological research and practical application, with contributions to psychometrics and statistical inference. Key Awards: APA Division 5 Anne Anastasi Award (2022) APS Rising Star Award (2017) Multiple Fred Brown Research Awards (2019–2024) Advising & Grants: Advised students including J. Park and O. Wong. Her research has been supported by grants from the Ontario Ministry of Research and Innovation and others. She collaborates on methodological advancements in statistical software (e.g., hybridpower package). Labs/Teams: Engages in collaborative work through The Ohio State University’s Psychology Department, focusing on statistical methodology and reproducible research practices.

Qinglong TianView profile

Assistant Professor

• Statistical Methodology
• Reliability Engineering
• Machine Learning
• +3 more

Qinglong Tian is an Assistant Professor in the Department of Statistics at the University of Waterloo. His research focuses on statistical methodology development with applications in reliability engineering, machine learning, and predictive modeling. He specializes in areas such as nonparametric estimation, domain adaptation, privacy-preserving data analysis, and Bayesian reliability analysis. His work bridges theoretical statistics with practical challenges in engineering and healthcare sectors. Key research interests include semi-supervised learning, ROC curve analysis, label shift adaptation, and prediction interval methods. He has contributed novel techniques for handling imperfect reference standards, continuous target shift, and heterogeneous reliability data. His recent publications address challenges in post-randomization data privacy and model-agnostic parameter estimation. Qinglong's methodological innovations are applied to real-world problems like pipeline life assessment, diagnostic testing accuracy, and failure prediction in complex systems. His interdisciplinary approach combines rigorous statistical theory with computational methods to solve modern data science challenges.

Matthias SchonlauView profile

Professor

• Survey Methodology
• Natural Language Processing
• Data Visualization
• +3 more

Matthias Schonlau is a Professor in the Department of Statistics at the University of Waterloo. He previously worked as a statistician at the RAND Corporation (1999-2011), where he led the RAND Statistical Consulting Service. He holds a PhD from the University of Waterloo (1997) and a Master's from Queen's University (1993). His research focuses on survey methodology, natural language processing for open-ended questions, data visualization, and statistical software development. Key contributions include the Hammock Plot for mixed data visualization and automated classification algorithms for open-ended survey responses. His work spans algorithmic innovation (e.g., occupation coding, multi-label classification) and statistical software tools (e.g., HAMMOCK and RFOREST modules for Stata). Recent projects address semi-automated classification, one-shot learning, and text dataset distillation. He has held sabbaticals at the University of Auckland (2015-2016) and DIW Berlin (2009-2010), collaborating with the Max Planck Institute. Major awards include the Humboldt Research Prize (2022) and ASA Fellowship. His publications emphasize bridging statistical methods with practical applications, including books like Applied Statistical Learning (2023) and peer-reviewed articles in computational statistics and machine learning.

Lisa FosterView profile

Associate Professor

• Educational Leadership
• Fidelity of Implementation
• Gifted Education
• +6 more

Lisa Foster is an Associate Professor of Education and Chair of Quantitative Research at Liberty University. She previously served as a Post-Doctoral Fellow at Harvard Graduate School of Education (2011–2014) and as Co-PI/Project Director at Development Services Group (2014). University of Virginia (Ph.D. in Educational Leadership, 2011) Her research focuses on fidelity of implementation in educational interventions, mixed-methods design, and contextual factors influencing academic outcomes. She has explored these themes in gifted education and literacy/math programs. Recent publications address synthesis challenges in evidence-based policy, summer reading programs in high-poverty schools, and fidelity metrics in curriculum evaluation. National Board Certification in World Languages (2006) Elsie Hughes Cabell Scholar (2008) AERA Division L Travel Stipend (2010) Harvard Post-Doctoral Fellowship (2011–2014) AERA Division B Graduate Student Seminar (2011) She actively participates in AERA as Chair of Mixed Methods Research SIG and Executive Awards Committee for the National Association of the Gifted and Talented.

Rebecca GrazianiView profile

Researcher

• Bayesian Statistics
• Computational Methods
• Social Sciences Applications
• +2 more

Rebecca Graziani is a Researcher with tenure at Bocconi University's Department of Decision Sciences. She earned her PhD in Methodological Statistics from Università di Trento and conducted postdoctoral research at Bocconi University. Graziani specializes in Bayesian methods and computational statistics with applications in social sciences, particularly demographic forecasting and healthcare analytics. Her research focuses on developing Bayesian approaches for complex statistical problems including stochastic population projections, network meta-analysis of clinical outcomes, and nonparametric biomarker modeling. She has collaborated with institutions like ISTAT to develop Italy's first probabilistic population forecasts. Graziani teaches courses in Quantitative Methods, Statistics, and Computational Statistics using SPSS, STATA, R, and Python at both undergraduate and graduate levels across Bocconi's social science programs.

Clemens HeitzingerView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +6 more

Associate Professor Clemens Heitzinger holds a position at the Technical University of Vienna (TU Wien) within the Department of Informatics (Computer Science), specifically in the Research Unit Machine Learning under the Institute of Information Systems Engineering. His research focuses on machine learning, reinforcement learning, Bayesian methods, and their applications in healthcare, energy systems, and nanotechnology. He leads the TUW Center for Artificial Intelligence and Machine Learning (CAIML) and has received prestigious awards including the START Prize 2013 and an Erwin Schrödinger Fellowship. Education and Academic Background: Clemens earned his doctoral degree (Dr.techn.) in technical mathematics and holds a Diplom-Ingenieur (Master’s equivalent). His academic career includes roles at the University of Cambridge, Purdue University, and Arizona State University. Research Interests: Combines mathematical modeling with machine learning to address challenges in nanoscale sensor design, medical decision support systems, and energy optimization. Key areas include stochastic differential equations, PDE-based modeling, and reinforcement learning for critical applications like sepsis treatment and renewable energy control. Publications: His work spans high-impact journals such as Nonlinear Dynamics , EClinicalMedicine , and Sensors , with a focus on interdisciplinary applications of AI. Recent trends show strong contributions to reinforcement learning in healthcare and energy systems, as well as novel methods in fingerprint recognition and cryogenic sensor control. Awards: Recognized for his START Prize project on PDE models for nanotechnology and his contributions to AI ethics and medical AI. The 2024 nomination for the Austrian National Technology Prize highlights his work on energy system optimization via reinforcement learning. Advising and Grants: Supervises over 20 graduate and undergraduate students, with notable projects in reinforcement learning for sepsis therapy, cryogenic sensor control, and fingerprint analysis. Leads funded initiatives like the FFG-backed RELY project on sustainable energy systems and the WWTF-funded Mathematics and Nanosensors initiative. Labs and Teams: Co-directs CAIML and collaborates with the Medical University of Vienna on AI-driven healthcare solutions. Active in international collaborations through the AIDA doctoral academy and the Vienna Center for Partial Differential Equations.

Yichao ZengView profile

Researcher

• Ecology
• Evolutionary Biology
• Conservation Biology
• +3 more

Yichao Zeng is a Researcher affiliated with the Department of Ecology and Evolutionary Biology at the University of Arizona. He holds a Ph.D. in Ecology and Evolutionary Biology (2019–2023), an M.Sc. in Wildlife Ecology and Conservation (2017–2019), and a B.S. in Biological Science from Peking University (2013–2017). His research focuses on ecology, evolutionary biology, and conservation biology, particularly the intersection of species interactions and macroevolution. Key projects include studying anthropogenic threats to giant pandas and developing phylogenomic methods for species like the saguaro cactus. Education: Ph.D., Ecology and Evolutionary Biology, University of Arizona (2019–2023) M.Sc., Wildlife Ecology and Conservation, University of Florida (2017–2019) B.S., Biological Science, Peking University (2013–2017) His work spans theoretical and applied domains, including deep learning for wildlife identification and participatory conservation approaches. Publications highlight contributions to understanding species interaction dynamics, cophylogenetic patterns, and conservation strategies for endangered species. Zeng collaborates widely, with projects involving global teams across academia and conservation organizations. Research outputs emphasize coevolutionary mechanisms, diversification drivers, and conservation applications. Notable studies include analyses of mutualistic vs. antagonistic interactions and the use of genotype-based phylogenomics in saguaro cactus evolution. His interdisciplinary approach bridges ecology, genetics, and computational methods to address complex evolutionary questions.