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Matthew N. McCallView profile

Associate Professor

• Statistical Genomics
• Bioinformatics
• Systems Biology
• +7 more

Matthew N. McCall is a tenured Associate Professor in the Department of Biostatistics and Computational Biology and the School of Medicine and Dentistry at the University of Rochester. He also holds an associate professorship in Biomedical Genetics and is affiliated with the Goergen Institute for Data Science. He serves as Director of the Statistics PhD Program, co-director of the Wilmot Cancer Institute Biostatistics and Bioinformatics Shared Resource, and associate director of the Environmental Health Biostatistics Training Grant. His educational background includes a Ph.D. and M.H.S. in Biostatistics and Bioinformatics from Johns Hopkins Bloomberg School of Public Health (2010), and a B.S. in Statistics from the University of Michigan (2004). McCall's research lies at the intersection of statistical genomics, bioinformatics, and systems biology. His work emphasizes the development of computational and statistical methods for analyzing high-throughput biological data, particularly in cancer and immunogenomics. Key areas include preprocessing and analysis of miRNA-seq and Perturb-seq data, modeling gene regulatory networks, adjusting for cellular composition in tissue-level expression studies, and analyzing microglia imaging data. His methodological work supports biomedical discovery through rigorous data science. The recent publications reflect a strong focus on innovative statistical frameworks for single-cell and bulk sequencing data, with recurring themes in gene co-expression, non-coding RNA biology, and cancer systems biology. His work bridges computational innovation with biological insight, particularly in oncology and infectious disease. While no specific awards are listed in the provided text, his leadership roles and consistent publication record in high-impact journals suggest recognition within the field. McCall actively mentors graduate students and postdoctoral researchers, as evidenced by his supervision of PhD students and leadership of training grants. He is involved in significant research initiatives supported by institutional and federal funding, including the T32 training grant and shared resource leadership at the Wilmot Cancer Institute. He leads a research group focused on statistical genomics, as seen from his GitHub presence (mccall-group), where tools like miRglmm are developed and shared. His lab integrates biostatistical theory with practical applications in biomedical research, fostering collaboration across disciplines.

• Biostatistics
• Machine Learning
• Computational Biology
• +3 more

Zachary R. McCaw is a researcher active in biostatistics , machine learning , and computational biology . His recent work focuses on statistical frameworks for genetic analysis ( Nature Computational Science 2025 ), digital pathology artifact detection ( ISBI 2024 ), and fairness in healthcare ML ( CoRR 2024 ). Key themes include: Methodological innovation in genomic data analysis and multi-trait rare variant studies Algorithm development for digital pathology and tissue microarray optimization Advancing fairness and statistical rigor in healthcare ML applications His publications in BMC Bioinformatics demonstrate expertise in Gaussian mixture models and incomplete data handling . Collaborations across institutions suggest multidisciplinary engagement with biomedical informatics , health data science , and computational epidemiology .

• Information Visualization
• Visual Analytics
• Time-Oriented Data Analysis
• +5 more

Wolfgang Aigner is a Professor at TU Wien's Department of Visual Analytics, part of the Faculty of Computer Science. His main affiliation is with the Visual Analytics Research Department (E193-07). He focuses on advancing information visualization, time-oriented data representation, and knowledge-assisted visual analytics. His work emphasizes user onboarding methods for unfamiliar visualizations, integrating explicit domain knowledge into analytic workflows, and developing systems for malware analysis and healthcare informatics. Key research areas include: Design of intuitive visualization onboarding systems Temporal data visualization and pattern detection Integration of explicit knowledge in visual analytics Malware behavior visualization and forensic analysis Healthcare data exploration focusing on temporal patient trajectories Notable contributions include the KAMAS malware analysis system, task cube conceptual framework for visualization design evaluation, and methodologies for visually guided time series imputation. He has supervised 14+ graduate theses exploring topics like glyph editors, temporal data visualization, and bias detection in language models. Recent work emphasizes educational theories in visualization onboarding, with comparative studies on user guidance effectiveness and system scaffolding approaches. His research bridges cognitive science principles with practical visual analytics tools for domains like cybersecurity and healthcare.

• Trustworthy AI/ML
• Causal ML
• Out-of-distribution Robustness
• +6 more

David I. Inouye is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering (ECE) at Purdue University. His research focuses on trustworthy AI/ML, causal inference, distribution robustness, and explainable AI. He holds a PhD in Computer Science from The University of Texas at Austin and completed a postdoc at Carnegie Mellon University. Education: PostDoc in Machine Learning, 2019 – Carnegie Mellon University PhD in Computer Science, 2017 – The University of Texas at Austin MS in Computer Science, 2015 – The University of Texas at Austin BS in Electrical Engineering, 2012 – Georgia Institute of Technology BA in Natural Sciences, 2011 – Covenant College Research Interests: Developing robust machine learning methods resilient to distribution shifts and computational assumptions Exploring causal mechanisms to mitigate ML robustness issues Advancing explainable AI and fairness in automated decision systems Designing robust collaborative learning frameworks for edge device networks Publications Highlight Trends in: Counterfactual fairness and causal reasoning Federated learning and domain generalization Generative models and distribution matching Vertical data partitioning and dynamic network inference Grants: Funded by NSF, Army Research Laboratory (ARL), and Office of Naval Research (ONR). Active lab collaborations include projects on federated domain translation and causal ML. Labs/Teams: Leads the Inouye Lab, with contributions to open-source tools like FedINB and StarCraftImage datasets.

• Deep Learning
• Wastewater Treatment
• Transfer Learning
• +3 more

Maira Alvi serves as a Lecturer in Computer Science and Software Engineering at The University of Western Australia's School of Physics, Maths and Computing. Her research bridges artificial intelligence and environmental engineering, focusing on wastewater treatment optimization and urban infrastructure monitoring through advanced computational methods. Her technical expertise centers on deep learning architectures with specialization in: Variational Autoencoders for missing data imputation in industrial processes Transfer learning applications for data-scarce wastewater treatment scenarios Representation learning techniques for state estimation in complex urban systems Genetic algorithm-enhanced predictive modeling for process optimization Analysis of her 2023-2025 publications reveals a consistent research trajectory applying neural networks to environmental engineering challenges. Key innovations include hybrid AI models for wastewater quality prediction, critical assessments of deep learning limitations in treatment plants, and novel frameworks for automated system monitoring. Her work demonstrates increasing methodological sophistication in handling sparse industrial datasets. No scientific awards were mentioned in available documentation. Student supervision details and research grant information were not specified in the source materials. Collaborative research networks with chemical engineering specialists were evident, though specific laboratory facilities or dedicated research teams were not described.

Rendani MbuvhaView profile

Associate Professor

• Machine Learning
• Actuarial Science
• Climate Risk Management
• +3 more

Dr. Rendani Mbuvha is an Associate Professor in Actuarial Science at the Department of Mathematics, University of Manchester. He holds concurrent positions as Visiting Associate Professor at the University of the Witwatersrand and Research Fellow at the United Nations University. Previously, he served as a Google DeepMind Academic Fellow at Queen Mary University of London. His expertise spans machine learning applications in climate risk management and actuarial science, with a focus on physical climate risk mitigation in Africa through AI-driven solutions. Dr. Mbuvha earned his PhD from the University of Johannesburg under Prof. Tshilidzi Marwala, focusing on Shadow Hamiltonian Monte Carlo Methods in Bayesian Neural Networks. His research contributions include co-founding AfriClimate AI, a Pan-African initiative promoting AI for climate resilience, and authoring the book 'Hamiltonian Monte Carlo Methods in Machine Learning'. His work aligns with UN Sustainable Development Goals, particularly addressing climate action (SDG 13) and sustainable cities (SDG 11). Notable research areas include Bayesian econometrics, machine learning systems, decision-making models, and volatility analysis in climate systems. Dr. Mbuvha's academic recognition includes fellowships from the Institute and Faculty of Actuaries (UK) and the Actuarial Society of South Africa. His recent research explores AI-enhanced seasonal precipitation forecasts, multi-model climate prediction frameworks, and innovative streamflow prediction methodologies despite data gaps.

Nghia HoangView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Federated Learning
• +5 more

Nghia Hoang is a tenure-track Assistant Professor at the School of Electrical Engineering and Computer Science, Washington State University. Prior to his current appointment, he held positions as a Senior Research Scientist at AWS AI Labs (Amazon, 2020-2022), Research Staff Member at the MIT-IBM Watson AI Lab (2018-2020), Postdoctoral Research Associate at MIT's Laboratory for Information and Decision Systems (2017-2018), and Research Fellow at the National University of Singapore (2015-2017). Dr. Hoang earned his B.Sc. from the University of Science (Vietnam) in 2009 and completed his Ph.D. in Computer Science at the National University of Singapore in 2015 under the supervision of Associate Professor Kian Hsiang Low. His research focuses on Machine Learning , particularly in Federated Learning , Bayesian Methods , Optimization , and Deep Learning . He has made significant contributions to probabilistic federated learning, offline optimization techniques, and knowledge representation for collaborative learning across heterogeneous systems. His work often bridges theoretical foundations with practical applications in diverse domains including healthcare and multi-agent systems, with recent publications addressing challenges of data heterogeneity and scarcity in distributed learning environments. Dr. Hoang's publication record reveals strong trends in making federated learning more robust to non-IID data distributions, improving offline optimization through surrogate modeling, and developing techniques for knowledge transfer across heterogeneous systems without requiring orthologue mappings. His work spans both theoretical contributions and practical implementations, with increasing emphasis on real-world applicability of machine learning techniques. Dr. Hoang actively mentors students in research, with several publications led by his advisees including PhD students Pei-Yau Weng and Long Bui, as well as undergraduate mentee Cuong Dao. He has successfully guided multiple students to publish at top-tier conferences including NeurIPS, ICML, and UAI, demonstrating his commitment to student development and collaborative research. Professionally, Dr. Hoang serves on the boards of prestigious journals including Machine Learning Journal and Neural Networks as an Action Editor. He has been a program committee member for major AI conferences including AAAI, ICLR, ICML, NeurIPS, and IJCAI across multiple years (2018-2024), and organized workshops such as the Practical Bayesian Methods for Big Data workshop at IBM Research AI Week and the NeurIPS-21 Workshop on New Frontiers in Federated Learning.

• Mixed-effects models
• Longitudinal data analysis
• Missing data methods
• +3 more

Donald Hedeker is a Professor of Biostatistics in the Department of Public Health Sciences at the University of Chicago. His expertise focuses on advanced statistical methods for clustered and longitudinal data, particularly mixed-effects models. He developed four freeware programs (MIXREG, MIXOR, MIXNO, MIXPREG) for analyzing such data. He serves as an associate editor for Statistics in Medicine and Journal of Statistical Software , and has led numerous NIH/CDC-funded research grants. Research Interests: His work emphasizes ordinal outcomes in mixed-effects models, missing data strategies, intensive longitudinal studies (e.g., Ecological Momentary Assessment), computational statistics, and applications in tobacco/addictions research. Awards: Fellow of the American Statistical Association (highest honor, 2000) University Scholar, University of Illinois (2000) Grants & Roles: Principal/co-investigator on multiple NIH/CDC grants. Active in software development for statistical analysis.

Luke BudworthView profile

Research Fellow

• Health Informatics
• Patient Safety
• Medical Statistics
• +7 more

Dr Luke Budworth is a Senior Research Data Analyst at the Bradford Institute for Health Research and holds the position of Visiting Senior Research Fellow at the School of Medicine, University of Leeds, Faculty of Medicine and Health. His work bridges advanced data science with patient safety and health psychology research. His research interests lie at the intersection of health informatics, medical statistics, and patient safety. He specializes in using linked electronic health record data, particularly from the Connected Bradford data warehouse, to study intersectionality in health outcomes, develop risk prediction models for acute deterioration in primary care, and apply causal inference methods to co-produced safety questions. His methodological expertise spans Bayesian and frequentist multilevel modeling, survival analysis, missing data imputation, meta-analysis, and machine learning classification. Dr Budworth is actively involved in multiple research initiatives, including the MEaCC project developing maternal morbidity risk models across Yorkshire, and serving as trial statistician for the REBOOT RCT evaluating a psychological intervention for staff burnout. He also contributes to training and resource development for future researchers in patient safety informatics. His scientific recognition includes being a Fellow of the Royal Statistical Society, reflecting his contributions to statistical practice in healthcare research. He advises on research design and data analysis, supports multiple research teams, and welcomes PhD enquiries from prospective students interested in health data science. He has extensive experience in leading complex healthcare research projects, securing funding, publishing, and peer reviewing. Dr Budworth is supported by affiliations with the Bradford Institute for Health Research, an NIHR-funded patient safety center, and maintains an active academic presence through collaborations, presentations, and scholarly output.

Yuwen GuView profile

Assistant Professor

• High-dimensional statistics
• Variable selection
• Model combination
• +3 more

Yuwen Gu is an Assistant Professor in the Department of Statistics at the University of Connecticut. Their research focuses on high-dimensional statistics, variable selection, model combination, nonparametric methods, causal inference, and optimization techniques. Their recent publications highlight advancements in quantile regression, sparse modeling, and high-dimensional data analysis. Notable trends include applications of kernel methods, distributed computing for large-scale statistics, and regularization techniques for insurance and multi-source data. Yuwen Gu's work also explores interdisciplinary domains, such as bioacoustic signal processing in ecological studies, though their primary contributions remain in statistical methodology and computational efficiency.

Paul J. HuView profile

Professor

• Information Technology in Healthcare
• Artificial Intelligence
• Data-Driven Business Analytics
• +4 more

Paul J. Hu is the E.R. Dumke Jr. Presidential Endowed Chair in Business and Assistant Dean for Programs in Asia at the University of Utah's David Eccles School of Business, where he serves in the Department of Operations and Information Systems. He holds a PhD in Management Information Systems from the University of Arizona (1998), following a Master's degree in the same field (1992) and a Bachelor's in Chemical Engineering from National Taiwan University of Science and Technology (1987). Dr. Hu's research spans information technology empowerment in health care, artificial intelligence, data-driven business analytics, technology implementation and management in organizations, digital transformation, and information technology-enriched learning and innovations. His work demonstrates exceptional impact, with multiple papers ranking among the most cited in information systems literature, including three papers in the top 10 most cited health IT papers published between 1985-2011. His recent publications reveal a strong focus on applying deep learning and blockchain technologies to healthcare challenges, energy systems, and social media analysis. This research trajectory shows increasing sophistication in handling complex data environments while addressing critical societal challenges in healthcare and infrastructure management. #9 most cited paper in 50-year history of Decision Sciences (2020) INFORMS Information Systems Society Design Science Award (2016) Faculty Research Award, David Eccles School of Business (2016) Among top 50 most productive IS authors globally (1999-2003) Multiple recognition as among most productive/published IS researchers Dr. Hu has secured significant research funding including an NSF CIF21 DIBBs grant ($1.5M) and contributed to the National Center of Excellence for Infectious Disease Informatics. He serves in editorial capacities for prestigious journals including Journal of the AIS and ACM Transactions on Management Information Systems, demonstrating his leadership in the academic community.

• Maritime Safety
• Human Reliability Analysis
• Risk Assessment
• +3 more

Samet Bicen serves as a Researcher in the Department of Marine Engineering at Istanbul Technical University, where he conducts cutting-edge research at the intersection of maritime engineering and safety science. His work addresses critical challenges in operational risk management, human reliability, and inspection systems within the maritime industry, contributing to the development of safer and more efficient marine operations through rigorous empirical analysis and technological innovation. Dr. Bicen's primary research interests include Maritime Safety, Human Reliability Analysis, Risk Assessment, Ship Inspection, Reliability Engineering, and Artificial Intelligence Applications. He has pioneered methodologies for analyzing ship inspection reports using natural language processing, developed modified human reliability frameworks for helicopter underwater escape training, and established bibliometric foundations for maritime inspection research. His investigations consistently bridge theoretical models with practical industry applications, particularly in risk prediction and preventive maintenance systems. Analysis of his 2022-2025 publications reveals a strategic integration of computational intelligence—deep learning, bibliometrics, and hybrid risk assessment—into maritime safety workflows. This trend demonstrates increasing sophistication in handling multi-source data (inspection databases, near-miss reports, operational metrics) to generate actionable safety insights, with notable emphasis on compressed air systems reliability, AI-automated regulatory compliance, and human-system interaction in emergency scenarios. As Principal Investigator, Dr. Bicen leads significant research initiatives including the "Advanced Risk Assessment System for the Maritime Field" (2025) and "Technological Evaluation and Improvement Strategies in the Maritime Sector" (2024), securing institutional funding to advance maritime safety infrastructure. His collaborative work with Dr. M. Celik across eight publications since 2021 underscores a productive research partnership focused on operational risk mitigation and smart inspection technologies.

• Data Imputation
• Urban Mobility
• Transportation Policy
• +7 more

Sanaz Nikfalazar is a Teaching Fellow in the Department of Human Centred Computing at the Faculty of Information Technology, Monash University. Her work contributes to advancing knowledge in urban mobility, data imputation, and sustainable city development, with a strong alignment to UN Sustainable Development Goals, particularly Sustainable Cities and Communities. Research Interests: Her research spans missing data imputation using machine learning , urban mobility modeling , transport policy for autonomous vehicles , and informal housing practices in Australia . She employs computational techniques such as decision trees and fuzzy clustering to address data challenges in city planning. The trend in her recent publications shows a consistent focus on urban data analytics , combining data science with public policy to inform sustainable urban development. Her work bridges technology and societal challenges, particularly in transportation and housing. Scientific Contributions: Developed hybrid imputation methods for city mobility indices Analyzed transport policies for autonomous vehicles in Australian cities Explored informal housing patterns and their urban implications She has co-authored multiple peer-reviewed articles and conference papers. While no formal advising or grant information is available, her collaborative research indicates active engagement with experts in urban planning, data science, and policy analysis.

Eric TurkheimerView profile

Professor

• Behavior Genetics
• Quantitative Psychology
• Genetics of Intelligence
• +7 more

Eric Turkheimer is the Hugh Scott Hamilton Professor of Psychology at the University of Virginia, affiliated with the Department of Psychology in the College of Arts & Sciences. His work bridges empirical research, quantitative methods, and philosophical inquiry in psychology. Institution: University of Virginia School: College of Arts & Sciences Department: Department of Psychology Academic Rank: Professor Email: ent3c@virginia.edu Twitter: @ent3c His research centers on the genetics of complex behavior, especially how genes and environments interact to shape intelligence, personality, and family dynamics. He is particularly interested in sibling differences, parenting, marriage, and divorce, and he develops innovative methods for assessing personality while controlling for self-esteem bias. His philosophical work examines the implications of behavior genetics for human agency and free will. The most recent articles reflect a sustained focus on polygenic scores, heritability, gene-environment interactions (e.g., the Scarr-Rowe effect), and the philosophical foundations of behavioral genetics. His publications span topics from technical statistical methods to broader social and ethical implications, indicating a deeply interdisciplinary approach that integrates psychology, genetics, and social science. While no specific awards are listed in the provided texts, his leadership of a research lab, editorial contributions, and public scholarship through his blog Gloomy Prospect and online presence underscore his influence in the field. He actively mentors graduate students, seeking those with strong quantitative and writing skills interested in genetics, intelligence, personality, and philosophy of science. He teaches courses on the Nature-Nurture debate and Experimental Psychopathology. His lab, the Turkheimer Lab, conducts empirical and theoretical research on behavior development.

• Higher Education
• E-learning
• Data Mining
• +3 more

Dr Kay Penny is a Lecturer at the Business School , Edinburgh Napier University , with over 15 years of research activity in higher education and health informatics. Her work focuses on: E-learning participation and technology integration in Scottish/Croatian higher education Student retention analysis via propensity score matching Knowledge management systems and ICT competency assessments Research includes medical data mining applications for: Trauma audit data analysis Health-related quality of life indicators Record linkage techniques in Scottish health research She supervises doctoral research and has developed the Dissertation Maze e-toolkit for direct entry student transition support.