Find Academic Supervisors

• Electric Power Systems
• Renewable Energy Integration
• Distributed Generation
• +5 more

Carlos David Zuluaga Ríos is a Postdoctoral Researcher at the Institute for Research in Technology (IIT) of the Comillas Pontifical University, affiliated with the Higher Technical School of Engineering (ICAI). He holds a B.Sc. and M.Sc. in Electrical Engineering from the Universidad Tecnológica de Pereira (2010, 2013), and a Ph.D. in Engineering (2018) from the same institution. His research focuses on electric power systems, renewable energy integration, distributed generation, and electric vehicle technologies. Key research areas include probabilistic modeling of energy systems, optimization algorithms for grid integration, and control systems for sustainable technologies. He has contributed to projects like improving tools for hybrid AC/DC power systems analysis under renewable energy penetration. Recent work includes studies on temperature effects on air quality solutions and neuro-probabilistic frameworks for EV energy demand forecasting. He has authored over 20 journal articles, conference papers, and a book chapter. His work has been published in journals like Applied Energy , IEEE Transactions on Power Systems , and Sustainability . He has presented at international conferences such as ICIAP and IREP. His current project (TED2021-130610B-C22) focuses on advancing hybrid power system tools with EU funding.

• Evolutionary Biology
• Human and Non-Human Primate Behavior
• Ecology
• +3 more

Riana Minocher is a Guest researcher at the Max Planck Institute for Evolutionary Anthropology, affiliated with the Department of Human Behavior, Ecology, and Culture. She is currently a PhD Candidate in this department, exploring evolutionary processes and cultural transmission dynamics in human populations. Her research integrates evolutionary biology with anthropological studies, focusing on both human and non-human primate behavior and ecology. Education: 2018–present: PhD Candidate, Department of Human Behavior, Ecology, and Culture, Max Planck Institute for Evolutionary Anthropology 2018–present: MSc in Erasmus Mundus Master Programme in Evolutionary Biology (MEME), Uppsala University and Ludwig-Maximilians-Universität Munich 2012–2015: BASc in Arts and Sciences (First Class Honours), University College London Research Interests: Riana’s work centers on evolutionary processes shaping behavioral diversity within and between groups, particularly through the lens of cultural transmission in human populations. She investigates how ecological and social factors influence primate behavior and microbiome dynamics, as well as demographic patterns in human societies. Her current focus includes understanding the evolutionary drivers behind cultural variation and its implications for human well-being. Her methodologies span fieldwork, phylogenetic analysis, and computational modeling. Awards and Achievements: Erasmus Mundus Joint Master Degree Scholarship (EU-funded), 2015–2017 Dean’s List for Academic Excellence, University College London, 2015 Best Academic Performance Per Pathway (Years 1–2), UCL, 2013–2014 CIE Outstanding Learner Award – Top in India (Across 5 AS Levels), 2011 CIE Outstanding Learner Award – Top in India (Literature in English), 2011 Advising and Grants: While her current role does not explicitly list grant funding, her research has been supported by scholarships and institutional grants. She has not listed formal advisees at this stage. Labs and Teams: Riana contributes to interdisciplinary research within the Department of Human Behavior, Ecology, and Culture at the Max Planck Institute, collaborating with experts in evolutionary anthropology, microbiology, and computational social science.

Umit TokacView profile

Assistant Professor

• Bayesian Data Analysis
• Artificial Intelligence Models
• Hierarchical Linear Modeling
• +4 more

Umit Tokac is an Assistant Professor in the Department of Nursing at the University of Missouri–St. Louis (UMSL) College of Nursing. He holds a Ph.D. in Measurement and Statistics from Florida State University (FSU), along with multiple master's degrees in mathematics education and educational statistics. His research focuses on Bayesian data analysis, artificial intelligence applications in healthcare, and statistical methodologies for measuring human conditions. His work bridges quantitative methods with clinical practice, particularly in nursing and patient safety contexts. Key research areas include resilience prediction in healthcare workers, patient safety culture analysis, and machine learning approaches to healthcare challenges. Education: Ph.D. in Measurement and Statistics, Florida State University (2016) Certificate in Online Instructional Development, Florida State University (2014) MS in Educational Statistics and Research Methods, Florida State University (2010) MS in Math Education, Gazi University (2006) BS in Math Education, Gazi University (2005) Research Interests: Bayesian Modeling Artificial Intelligence in Healthcare Patient Safety Culture Meta-analysis Decision Theory Nursing Informatics Recent work trends emphasize predictive modeling for healthcare outcomes, pandemic-related psychological impacts, and application of machine learning in clinical and public health settings. His publications often address nurse well-being, patient safety, and methodological advancements in healthcare data analysis. Scientific Awards: Marie Geneau Memorial Endowed Scholarship (2024) Robert M. Gagne Research Award (2024) Statewide Graduate Student Research Symposium Second Place (2024) Advising and Grants: While specific grants are not detailed, his research portfolio reflects collaborations across disciplines including psychology, nursing, and computer science. He has mentored students in applied statistical projects with real-world healthcare implications.

• Computer Vision
• Machine Learning
• Signal Processing
• +6 more

Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.

Hongyang GaoView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +3 more

Hongyang Gao is an Assistant Professor at Iowa State University, focusing on AI, Machine Learning, and Data Science. His work bridges theoretical foundations and practical applications in neural networks, graph representation learning, and cybersecurity. Education: PhD in Computer Science, Texas A&M University MS in Computer Science, Tsinghua University B.S. in Biomedical English, Peking University Research Interests: Explores neural ODEs, graph neural networks (GNNs), and their applications in molecular modeling, vulnerability detection, and trustworthy AI systems. His recent work emphasizes model interpretability (e.g., MotifExplainer), optimization theory for implicit networks, and graph-based explainability techniques. Publications Trends: Recent papers (2022-2025) highlight advancements in graph learning frameworks, such as G2T-LLM for molecule generation and MAGE for GNN explainability. His work on Neural ODEs explores activation function impacts on convergence, while cybersecurity papers apply dataflow analysis for vulnerability detection. Advising: Collaborates with students on projects involving motif-based methods (e.g., Motifpiece), meta-learning (Meta-AdaM), and trustworthy AI (data preconditions research). No grants explicitly listed in provided texts.

Elizabeth StojanovskiView profile

Senior Lecturer

• Biostatistics
• Longitudinal Modelling
• Multivariate Statistics
• +6 more

Dr. Elizabeth Stojanovski is a Senior Lecturer at the University of Newcastle , affiliated with the School of Information and Physical Sciences and the Priority Research Centre for Computer-Assisted Research Mathematics and its Applications (CARMA) . She leads the Mathematics Education Research Group and manages the statistical consulting unit, serving industry, academics, and postgraduate students. With a PhD in Statistics from the University of Newcastle, she specializes in Biostatistics , Longitudinal Modelling , and Multivariate Statistics . Education : PhD (University of Newcastle), Bachelor of Mathematics (Honours), Bachelor of Mathematics Professional Roles : Senior Lecturer (2007–present), Research Academic (2004–2006) Memberships : Member of the Australian Statistics Society Her research spans health and education , focusing on longitudinal relationships between life events and physical/mental health. She has developed Bayesian frameworks , longitudinal models , and meta-analytic techniques for applications in tobacco control, cardiovascular disease, cancer treatment, and mathematics education. Her work includes collaborations with Eric Beh on Nobel Prize data analysis, Manohar Garg on lipid-lowering studies, and Suzanne Snodgrass on physiotherapy biomechanics. Recent publications highlight her expertise in correspondence analysis for educational and scientific data, multivariate assessment of diabetes risk , and reinforcement learning for optimization. She has supervised 15 PhD/Masters students and peer-reviewed for journals and grants. Her grants total $496,207 , including projects on statistical literacy and phytosterol efficacy. Scientific Awards : 2001 "Temporal association between life events and health" Elizabeth's teaching spans theoretical and applied statistics, with a focus on psychology, health, and business applications. She has also contributed to forensic science through cut mark analysis on bone and policy research on tobacco investments in pension funds. Her leadership in CARMA and statistical consulting underscores her interdisciplinary impact.

Chong WangView profile

Professor

• Biostatistics
• Meta-Analysis
• Disease Surveillance
• +4 more

Dr. Chong Wang is a Professor at Iowa State University, holding dual appointments in the Department of Veterinary Diagnostic and Production Animal Medicine and the Department of Statistics. He specializes in Biostatistics, Meta-Analysis, and Disease Surveillance, with a focus on veterinary applications such as livestock disease control and diagnostic test evaluation. Education: PhD in Statistics (Cornell University, 2006), MS in Statistics (Cornell University, 2004), BS in Statistics and Probability (Peking University, 2001). Research interests include systematic reviews, machine learning applications in veterinary epidemiology, and statistical methods for animal disease surveillance. His work emphasizes optimizing trial designs and analyzing antimicrobial resistance patterns. He has advised numerous PhD and Master’s students in biostatistics and veterinary science. Publications span over 150 articles, focusing on network meta-analysis, PRRS virus dynamics, and diagnostic assay validation. Collaborations include studies on porcine epidemic diarrhea virus and swine health management. Affiliations include leadership roles in statistical methodologies for veterinary research and contributions to grants on disease surveillance and antimicrobial stewardship.

Lisa HülsmannView profile

Professor

• Ecosystem Analysis
• Forest Ecology
• Biodiversity Mechanisms
• +3 more

Prof. Lisa Hülsmann is a Professor of Ecosystem Analysis and Simulation at the University of Bayreuth, leading the Ecosystem Analysis and Simulation (EASI) Lab within the Bayreuth Center of Ecology and Environmental Research (BayCEER). She serves as Deputy Director of BayCEER and member of its Executive Board. Her research focuses on forest ecosystem dynamics, biodiversity mechanisms, and the integration of ecological data into process-based models. Key topics include forest resilience to climate change, species coexistence, and large-scale ecological transitions. Her work combines statistical analysis of ecological data (e.g., national forest inventories) with dynamic vegetation modeling to address global challenges like biodiversity loss and climate change. Recent studies explore tree demographic responses to environmental gradients, mortality patterns, and the future viability of species such as sweet chestnut under global change. Publications highlight methodological advances in mortality modeling, climate-plant interactions, and biodiversity drivers. She emphasizes societally relevant research, such as developing forest management strategies for resilience and assessing greenhouse gas fluxes in wetland systems. Prof. Hülsmann’s lab (EASI) collaborates internationally, focusing on cross-scale analyses from local to global levels. Her contributions bridge empirical data and theoretical frameworks, advancing understanding of ecological processes under environmental change.

Lan DuView profile

Associate Professor

• Machine Learning
• Natural Language Processing (NLP)
• Active Learning
• +4 more

Dr. Lan Du is an Associate Professor in the Department of Data Science & AI at Monash University's Faculty of IT. His research focuses on cross-disciplinary applications of machine learning and AI, particularly in text analytics, uncertainty estimation, knowledge distillation, and multi-modal learning. He leads projects addressing real-world challenges in public health, marketing, and clinical decision-making. Key collaborations include work with Victoria Police, Monash Health, and the National Health and Medical Research Council (NHMRC). Education: PhD in Computer Science (ANU, 2012), Bachelor of Information Technology (ANU, 2007), and B.Communication & IT (Flinders University, 2006). Research Interests: Machine/deep learning for NLP, active learning strategies, uncertainty quantification, and AI-driven solutions for healthcare and business analytics. His work bridges theoretical advancements with practical implementation, emphasizing translational research. Recent Projects (2021–2026): Includes AI models for predicting fracture outcomes (NHMRC-funded PRAISE study), risk prediction tools for pregnancy complications, and medical surveillance systems. He also collaborates on business insights derived from unstructured customer data. Teaching Commitment: Served as Chief Examiner and Lecturer for courses like FIT5149 (Applied Data Analysis) and FIT5196 (Data Wrangling). Lab/Team Involvement: Leads initiatives in AI for healthcare analytics and cross-modal learning, with active participation in Monash's research networks.

Jan-Willem RomeijnView profile

Professor

• Philosophy of Science
• Probability Theory
• Statistical Inference
• +5 more

Jan-Willem Romeijn is a Professor of Philosophy of Science at the University of Groningen, affiliated with the Faculty of Philosophy's Theoretical Philosophy department. He earned dual degrees in Physics and Philosophy from Utrecht University (cum laude) and a PhD in Philosophy from the University of Groningen (cum laude, 2005). His research focuses on probability theory, statistical methodology, and their applications in science and psychiatry. Notably, he received a Vidi grant from NWO (2010) to study probability and statistical methodologies in science and business, and was named Lecturer of the Year 2009 at the University of Groningen. Romeijn's work spans interdisciplinary collaborations, including projects on psychiatric classification, Bayesian model selection, and formal epistemology. He has advised on judicial and policy contexts and led research initiatives like the Vici project 'Gold Rush in the Data Mine' (2023). His recent articles address topics such as group deliberation dynamics, statistical weighting mechanisms, and psychiatric disorder subtyping. Romeijn also served as Dean of the Faculty of Philosophy (2020–2022) and contributes to ancillary activities like lecturing for legal professionals.

Jacob SpertusView profile

Researcher

• Causal Inference
• Nonparametric Statistics
• Statistical Design
• +5 more

Jacob Spertus is a postdoctoral researcher in the Department of Statistics at the University of California, Berkeley. He completed his PhD in Statistics at Berkeley in 2024, advised by Philip B. Stark. His work spans interdisciplinary applications including risk-limiting election audits, soil organic carbon measurement, cardiovascular outcomes research, and motor carrier safety. Prior to his PhD, he was a research assistant at Harvard Medical School’s Department of Health Care Policy, working with Sharon-Lise Normand. He holds a Bachelor’s degree in Mathematics from Bowdoin College. Education: PhD in Statistics (UC Berkeley, 2024), BA in Mathematics (Bowdoin College, 2016) His research focuses on causal inference, nonparametric methods, design-based statistics, and policy optimization. He develops statistical tools for environmental science (soil carbon monitoring), election integrity (risk-limiting audits), and healthcare policy (cardiovascular outcomes analysis). His technical work emphasizes rigorous validation and practical implementation of statistical methodologies. Key contributions include methodologies for stratified sequential inference, optimal sampling designs for soil carbon measurement, and audit techniques to ensure election accuracy. His applied work bridges theoretical statistics with real-world challenges in public policy and scientific measurement. Labs/Teams: Collaborates with Philip B. Stark’s research group at Berkeley and previously with Harvard’s Health Care Policy department

• Robotics
• Human-Computer Interaction
• Machine Learning
• +3 more

Dr. Yi-Chi Liao is a Lecturer in the Department of Computer Science at ETH Zürich, specializing in intelligent interactive systems. Their research focuses on human-robot interaction, wearable technology, and optimization techniques for user interface design. Key areas include developing datasets for naturalistic handover behaviors with robotic limbs, human-in-the-loop optimization methods, and computational workflows for designing input devices. Research Interests: Explores the intersection of robotics, machine learning, and human-centered design. Recent work emphasizes Bayesian optimization techniques, affordance theory, and tactile feedback systems. Projects like the 3HANDS dataset and ThirdHand wearable robotic arm demonstrate innovation in human augmentation and interaction design. Advising: Supervises doctoral student Peizhuo Li in the D-INFK program. Research outputs span 2015–2025, with notable contributions to haptic interfaces, multi-objective optimization, and wearable computing. Active in conferences and journals addressing HCI, robotics, and design automation.

Lindsay CollinView profile

Assistant Professor

• Cancer Epidemiology
• Health Disparities
• Bayesian Analysis
• +5 more

Dr. Lindsay Collin is an Assistant Professor of Epidemiology at Emory University's Rollins School of Public Health. Her research focuses on cancer epidemiology, particularly addressing disparities in cancer outcomes across socioeconomic and racial groups. She specializes in methodological advancements such as Bayesian statistics, bias analysis, and validation study design. Education: BSc, University of British Columbia MPH, Emory University PhD, Emory University Research Interests: Dr. Collin's work examines multilevel predictors of cancer treatment efficacy, recurrence, and mortality. She investigates how social determinants (e.g., neighborhood deprivation, redlining) and molecular factors (e.g., tumor biology) intersect to influence health disparities. Her methodological contributions include improving bias quantification and validation study frameworks. Recent Work Trends: Her recent studies emphasize racial disparities in breast and ovarian cancer outcomes, leveraging geospatial and genomic data. She also explores how systemic factors like historical redlining affect tumor biology and clinical outcomes. Methodologically, her team develops Bayesian approaches to handle measurement error and unmeasured confounding in observational studies. Awards: No specific awards listed, though her work reflects sustained contributions to health equity and epidemiologic methods. Advising & Grants: Advises on studies addressing cancer disparities and methodological rigor. Her grants likely support collaborative projects analyzing large-scale cancer registries and population health datasets. Labs/Teams: Active in Emory's Cancer Research Group and collaborates with Danish cohorts (e.g., ProBe CaRE study). Engages with the Kaiser Permanente Research on Ovarian Cancer Survival (KP-ROCS) initiative.

Ashley NaimiView profile

Associate Professor

• Maternal and Child Health
• Nutrition
• Biostatistics
• +9 more

Ashley Naimi is an Associate Professor in the Department of Epidemiology at Emory University. Her expertise lies in causal inference, machine learning, and artificial intelligence methods applied to reproductive and perinatal epidemiology, nutritional epidemiology, and social determinants of health. She currently co-leads the AJE Classroom section at the American Journal of Epidemiology, focusing on methods education. Dr. Naimi holds a PhD from the University of North Carolina at Chapel Hill. Her research emphasizes innovative methodologies such as machine learning for dietary synergy analysis and mediation analysis to explore health disparities. Recent contributions include studies on low-dose aspirin in pregnancy, causal effect estimation challenges with machine learning, and the role of neighborhood poverty in cardiovascular disease. Her work spans 15+ peer-reviewed articles in top journals like Annals of Internal Medicine and American Journal of Epidemiology . Key themes include methodological advancements in causal inference, health disparities, and real-world data applications. She leads a research team focused on translating data science innovations into public health practice.

• Cardiac Magnetic Resonance Imaging (CMR)
• Myocardial Perfusion
• Cardiovascular Imaging
• +5 more

Dr. Adriana Villa is a researcher specializing in Cardiac Magnetic Resonance Imaging (CMR) and myocardial perfusion analysis , with 819 citations and 28 research outputs. Her work intersects medical imaging, cardiovascular diagnostics, and advanced computational methods. Key Research Areas : Cardiac MRI, Hypertrophic/Takotsubo Cardiomyopathy, Myocardial Perfusion, Sudden Cardiac Death risk stratification Notable Projects : Calibrated multimodality quantitative myocardial perfusion analysis (2018-2022, British Heart Foundation funded) Collaborations : Active in international networks with 26+ co-authors, including Prof. S. Plein and Prof. R. Razavi Innovation : Developed CardiSort (2022) - a neural network for automated CMR image sorting Her publications span high-impact journals like JACC: Cardiovascular Imaging and European Radiology , covering topics from multi-slice perfusion techniques to machine learning applications in cardiac imaging. While no formal awards are listed, her work has been referenced in patents and widely shared across academic networks. Current research focuses on quantitative CMR for ischemia detection, myocardial strain analysis in cardiomyopathies, and AI-driven image processing to improve diagnostic accuracy.