Dr. Nicholas Cummins is a Lecturer in AI for Speech Analysis at King’s College London, specializing in applying machine learning to health conditions, particularly mental health disorders. He holds roles in the Department of Biostatistics & Health Informatics and is affiliated with the NIHR Maudsley Biomedical Research Centre. His research focuses on speech processing, affective computing, and digital phenotyping, with projects like RADAR-CNS and DE-ENIGMA. He earned his PhD from UNSW Australia (2016) and conducted postdoctoral work in Germany before becoming habilitation candidate at the University of Augsburg. Education: PhD in Electrical Engineering, UNSW Australia (2016) Habilitation Candidate, Chair of Embedded Intelligence for Healthcare, University of Augsburg Research Interests: Machine learning for mental health monitoring Speech-based biomarkers for depression and psychosis Wearable device integration for health tracking Multilingual speech analysis Key Projects: RADAR-CNS: Data analysis for neurological conditions DE-ENIGMA, TAPAS, sustAGE (Horizon 2020) National Science Foundation of China project on speech-based depression diagnosis Grants & Awards: Over 100 peer-reviewed publications (h-index: 23) NSFC-funded project on speech analysis for depression Reviewer for IEEE, ACM, ISCA journals Labs & Teams: Involved in the NIHR HealthTech Research Centre in Brain Health and the EMBRACE initiative for maternal/child health with AI.
Samsung Lim serves as an Associate Professor of geographic information systems (GIS) in the School of Civil and Environmental Engineering at the University of New South Wales (UNSW) Sydney. With expertise spanning data science, artificial intelligence, and machine learning, Lim applies geospatial technologies to critical real-world challenges in natural disaster management and public health research. Lim's interdisciplinary work bridges engineering, computer science, and public health domains to develop practical decision-making tools for emergency response and disease surveillance. Ph.D. in Aerospace Engineering and Engineering Mechanics, University of Texas, Austin, TX, USA M.A. in Mathematics, Seoul National University, Seoul, South Korea B.A. in Mathematics, Seoul National University, Seoul, South Korea Lim's research focuses on applying GIS to natural disaster management and public health challenges. Key areas include machine learning methods for bushfire susceptibility mapping, spatial clustering for landslide susceptibility analysis, city-scale evacuation management in flood scenarios, and social media-based natural disaster assessment. In public health, Lim investigates geo-correlations between environmental factors and asthma occurrence, computational approaches to avian influenza outbreaks, emerging hot spot analysis of COVID-19, and early detection systems for emerging infectious diseases. This work combines advanced spatial analytics with machine learning to address complex environmental and health challenges. The recent publication record demonstrates a clear interdisciplinary trajectory where geospatial science intersects with public health emergency response and natural hazard management. Lim's work consistently applies machine learning techniques to geospatial data, with particular emphasis on disaster susceptibility mapping, disease outbreak detection, and infrastructure monitoring. The research spans multiple continents and addresses both immediate emergency response needs and long-term environmental health challenges, reflecting a commitment to practical applications of geospatial science. Associate Editor of Geospatial Information Science National Delegate of Commission 3 of International Federation of Surveyors (FIG) National Representative of the International Cartographic Association (ICA) Commission on Sensor-driven Mapping Senior Member of Institute of Electrical and Electronics Engineers (IEEE) Lim actively contributes to the development of early warning systems for emerging infectious diseases through collaborations with public health researchers. The work on EPIWATCH demonstrates how AI can enhance surveillance capabilities for outbreak detection. Lim's research on cruise ship transmission of diseases and the spread of avian influenza through bird migration patterns and poultry trade networks shows strong engagement with real-world public health challenges. These projects often involve multidisciplinary teams spanning engineering, computer science, epidemiology, and veterinary medicine. Lim's work integrates multiple geospatial data sources and analytical techniques to address complex environmental and public health challenges. This includes developing frameworks for performance analysis of OpenStreetMap data, creating specialized road datasets for pedestrian navigation, and applying Persistent Scatterer Interferometry for land motion monitoring. The research combines traditional geospatial methods with cutting-edge machine learning approaches to extract meaningful insights from complex spatial datasets.
Dr. Erik Linstead is an Associate Professor and Senior Associate Dean at Chapman University, affiliated with the Fowler School of Engineering, School of Pharmacy, and George L. Argyros College of Business and Economics. His expertise spans Machine Learning, GPU Programming, Autism Spectrum Disorder, Assistive Technologies, Predictive Analytics, and Virtual Reality. Education: Bachelor of Science, Chapman University Master of Science, Stanford University Ph.D., University of California, Irvine Dr. Linstead's research integrates machine learning with diverse domains, including autism treatment, environmental monitoring, and software engineering. His recent publications focus on coral reef health, land surface temperature trends, and embedded machine learning systems. His scholarly work includes collaborations in remote sensing, medical informatics, and neurodiversity support. Articles highlight his interdisciplinary approach, applying AI to ecological challenges (e.g., Red Sea coral reefs, Nile Basin droughts) and human-centered technologies (e.g., VR therapy for autism, medication adherence analysis).
Dr. Kevin Gee is a Professor in the School of Education at the University of California, Davis, specializing in the School Organization & Educational Policy emphasis area. He serves as Director of the School Policy, Research, and Action (SPARC) Center and is a Faculty Research Affiliate with the Center for Poverty & Inequality Research. As a 2020-25 Chancellor's Fellow, Dr. Gee leads research initiatives focused on vulnerable youth populations and educational policy impacts. His work bridges education, public health, and social welfare systems to address structural inequities affecting children's development and academic success. Dr. Gee's educational background includes: Ed.D., Harvard Graduate School of Education, Quantitative Policy Analysis in Education (2010) Ed.M., Harvard Graduate School of Education, International Education Policy (2006) M.P.I.A., University of California, San Diego, Pacific & International Affairs (cum laude, 2004) B.A., University of California, Berkeley, City & Regional Planning (magna cum laude, 1994) Dr. Gee's research centers on the critical intersection between health and education systems, examining how schooling can influence children's well-being. He investigates policies addressing adverse childhood experiences including bullying, food insecurity, abuse, and neglect. His work employs rigorous quantitative methods including Hierarchical Linear Modeling, longitudinal analysis, and experimental/quasi-experimental designs. Dr. Gee focuses particularly on vulnerable populations such as children with disabilities, Asian American and Pacific Islander youth, and those involved in the child welfare system, seeking data-driven solutions to educational inequities. Analysis of Dr. Gee's recent publications reveals a strong focus on educational equity, with particular attention to vulnerable student populations. His work spans school absenteeism patterns, bullying and hate speech against AAPI youth, food insecurity impacts, and health-related educational outcomes. The research demonstrates increasing interdisciplinary collaboration, particularly with public health researchers, and shows a growing emphasis on pandemic-related educational disruptions and their disproportionate impacts on marginalized communities. Dr. Gee's notable scientific awards include: National Academy of Education (NAEd)/Spencer Postdoctoral Fellowship (2015) Foundation for Child Development (FCD) Young Scholars Program Award (2014-2017) UC Davis Hellman Fellowship (2015-2016) Chancellor's Fellowship (2020-2021) Outstanding Faculty Award, Asian Pacific American UC-Systemwide Alliance (2023) Distinguished Visiting Scholar, Advanced Research Collaborative, CUNY (2022) Dr. Gee serves as Principal Investigator for multiple significant grants, including the Heising-Simons Foundation project on districtwide family engagement strategies and chronic absenteeism (2024-2026), and the UC Davis SEED funding for research on how Asian American and Pacific Islander youth confront bullying. He also serves as Co-Investigator on the AAPI Data Grant examining school climate influences on bullying experiences. His grant portfolio demonstrates strong interdisciplinary collaboration, particularly between education and public health researchers, with a consistent focus on generating actionable insights for educational policymakers and practitioners. As Director of the School Policy, Research, and Action (SPARC) Center at UC Davis, Dr. Gee leads a research team focused on generating data-informed insights about underserved and overlooked youth in educational policy. The center's work specifically supports Asian American and Pacific Islander youth who have experienced bullying, children with chronic absenteeism, and child welfare-involved youth who have experienced maltreatment. The SPARC Center collaborates with various California school districts and state agencies to translate research into practical policy recommendations and implementation strategies.
Jesús Carro is an Associate Professor at Universidad Carlos III de Madrid in the Department of Economics . He specializes in micro-econometrics and labor economics, focusing on dynamic discrete choice models and unobserved heterogeneity analysis. Doctorate from CEMFI Active research since 2004 Key methodological contributions in econometric modeling His research spans: Labor supply dynamics Education policy evaluation Health economics Intergenerational preference transmission Recent publications highlight: Dynamic binary choice modeling with maximal heterogeneity (2014) State dependence in health outcomes (2014) Bilingual education program impacts (2016) Preference transmission mechanisms (2017) He teaches courses in: Micro-econometrics Economics of Education Policy Evaluation
Aiko Pras is a Professor at the University of Twente in Enschede, Netherlands. His research spans network and service management, cybersecurity, and distributed systems, with a focus on DNS security, DDoS mitigation, IPv6, and software-defined networking. Research Interests: Network and Service Management Cybersecurity and DDoS Mitigation DNS Security and IPv6 Software-Defined Networking (SDN) Internet Measurement and Performance Critical Infrastructure Protection (ICS/SCADA) Article Trends: Pras's recent work emphasizes collaborative DDoS defense, DNS security, and IPv6 vulnerabilities. His studies often involve large-scale measurements and propose practical solutions for internet resilience and security. Collaborations: He frequently collaborates with Anna Sperotto, Roland van Rijswijk-Deij, and other experts in network security and measurement.
Professor Deborah Loxton is a leading academic at the University of Newcastle , serving as Director of the Centre for Women's Health Research (CWHR) and the Australian Longitudinal Study on Women's Health (ALSWH) . Her work bridges quantitative and qualitative research in women's health, with a focus on reproductive health, domestic violence impacts, and social determinants of wellbeing. PhD in Health (University of New England, 2003) Bachelor of Psychology (Honours, University of New England) Loxton’s research examines lifelong health impacts of trauma, particularly domestic violence, and maternal/infant health disparities. She has pioneered the WWOMB program to reduce global maternal mortality and co-authored the #EndGenderBias survey report on healthcare discrimination. Her recent publications address topics like intimate partner violence in twin studies, contraceptive use among women with chronic diseases, and WASH services in Nepal. Awards include the CAPHIA Team Award (2015) and University of Newcastle Faculty Research Award (2008) . Secured over $50 million in grant funding Collaborated with WHO, USA Academy of Violence and Abuse, and Korean Women’s Development Institute She leads the HMRI Women’s Health Program , focusing on violence prevention, maternal health, and healthy aging. Her work has shaped Australia’s National Women’s Health Strategy and National Plan to End Violence policy.
Beat Rechsteiner is a Senior Lecturer and Postdoctoral Researcher at the Institute of Education, University of Zurich, specializing in educational processes within schools. He serves as Project Lead for the SNSF Research Project R2 (Regulation of Routines in Teaching Development) and contributes to theoretical and empirical research on teacher collaboration, school improvement, and social network analysis. His work emphasizes self-regulated learning, instructional capacity, and adaptive strategies for educational challenges. Doctoral Program in Education (University of Zurich, 2018–2022) Master’s in Educational Science (University of Zurich, 2014–2018) Secondary School Teacher Training (Zurich University of Teacher Education, 2005–2009) His research focuses on: Teacher collaboration networks and their impact on school improvement Professional development dynamics through experience sampling Brokerage mechanisms in educational change Adaptation of routines during crises (e.g., pandemic effects on math competencies) Recent publications highlight trends in social network analysis, school reform, and collective regulation. Key themes include boundary-crossing activities, data-driven school development, and stress management in collaborative environments. Awards include the GRC Travel Grant (2022). He actively reviews for journals like Teaching and Teacher Education and Journal of Educational Change , participates in international exchanges (University of Antwerp), and teaches graduate courses on systematic reviews, school improvement routines, and educational research.
Professor Karin Sanders serves as Associate Dean (Research) at the UNSW Business School, where she is affiliated with the School of Management and Governance. With over two decades of academic experience, she has established herself as a leading scholar in Human Resource Management, particularly focusing on the HR process approach and employee perceptions of HR practices. Her research has been published in top-tier journals including HRM (Wiley), Academy of Management Learning & Education, Human Resource Management Journal, and International Journal of HRM. Professor Sanders' research primarily centers on the HR process approach, examining how employees' perceptions, understanding, and attributions of HR practices influence their attitudes and behaviors. Her work explores critical areas such as informal learning activities, cooperative behavior, commitment, knowledge sharing, team learning, and absenteeism. She has made significant contributions to understanding the effects of alignment between line managers and HR managers on organizational climate, defined as shared perceptions of the organization among employees within a team. Her methodological approach often employs multi-actor, multi-level, and multi-method research designs, reflecting the complexity of HR phenomena in organizational contexts. As an influential academic leader, Professor Sanders serves as Editor for Special Issues at the International Journal of HRM and Associate Editor for Human Resource Management and Frontiers of Business Research in China. She sits on the Editorial Boards of six HRM and management journals. Previously serving as Head of School for Management between 2015 and 2019, she was also a member of the Executive Committee of the HR Division Academy of Management from 2013 to 2018. Her recent work has increasingly focused on HRM during crises, including the development of HR analytics and responses to the pandemic. Professor Sanders has secured substantial research funding, including an Australian Research Council Linkage Project on capacity building in the Not-For-Profit sector and multiple industry collaborations. She has edited several books, including the "Handbook on HR Process Research" (2021) with Huadong Yang and Charmi Patel. Her scholarly contributions bridge theoretical rigor with practical relevance, influencing both academic discourse and HR practice worldwide.
Dr. Gloria Roberts is a Research Fellow at the Black Dog Institute, affiliated with the University of New South Wales' Faculty of Medicine, School of Psychiatry. Her research focuses on identifying predictors of bipolar disorder development in high-risk populations, with particular emphasis on neural mechanisms of executive functioning and emotional processing. Location: Black Dog Institute, Hospital Road, Prince of Wales Hospital, Randwick NSW 2031 Contact: +61 2 9382 8324 | ORCID: https://orcid.org/0000-0002-1966-5120 Education Background: B.Sc in Applied Psychology (University College Cork, Ireland, 2002) M.Sc in Neuropharmacology (National University of Ireland Galway, Ireland, 2003) Diploma in Statistics (Trinity College Dublin, Ireland, 2006) PhD in Neuroscience (Trinity College Dublin, Ireland, 2008) Dr. Roberts' research program centers on the neural basis of emotional dysregulation characteristic of mood disorders, employing structural and functional Magnetic Resonance Imaging as her primary research tool. Her work integrates advanced neuroimaging analysis techniques including diffusion tensor imaging tractography, dynamic causal modeling, graph theory, and machine learning approaches. She maintains active collaborations with Queensland Institute of Medical Research (Brisbane), Neuroscience Research Australia (Sydney), and the Centre for Healthy Brain Ageing (Sydney). Analysis of Dr. Roberts' publication record (94 journal articles, 2 book chapters, 25 conference papers) reveals a consistent research trajectory focused on neurocognitive patterns in bipolar disorder. Her recent work increasingly incorporates machine learning techniques to identify predictive biomarkers, with a growing emphasis on longitudinal studies tracking high-risk populations. The interdisciplinary nature of her research bridges neuroscience, psychiatry, and computational methods to address fundamental questions about mood disorder development. Scientific Contributions: Extensive publication record across multiple formats (journal articles, book chapters, conference presentations) Development of innovative neuroimaging analysis techniques for bipolar disorder research Establishment of multi-institutional collaborations across Australia Integration of machine learning approaches with traditional neuroimaging methods Dr. Roberts actively mentors junior researchers and contributes to the broader scientific community through peer review activities and participation in research networks focused on mood disorders. Her work has significant implications for early intervention strategies and the development of novel therapeutic approaches for bipolar disorder.
Hulin Wu, PhD is a Professor at the University of Texas Health Science Center at Houston (UTHealth) School of Public Health and Chair of the Department of Biostatistics & Data Science. He holds the Betty Wheless Trotter Professorship and Dr. D.R. Seth Family Professorship in Biostatistics. Trained with an MS in Engineering and a PhD in Statistics, Wu specializes in statistical methods for longitudinal data analysis, differential equation models, and their biomedical applications in clinical trials, bioinformatics, and immunology. His work focuses on infectious diseases like HIV, influenza, and bacterial infections, alongside big data analysis and community-based health studies. Research Interests : Biostatistical modeling, dynamic systems in health data, clinical trial design, EHR integration, and -omics applications in infectious disease modeling. Leadership : Joined UTHealth in 2015 as Dr. D.R. Seth Family Professor and Associate Chair of Biostatistics, later becoming Chair in 2017. Previously held roles at the University of Rochester Medical Center. Funding : Continuously funded by NIH since 1998 as PI/Co-PI for R01, T32 training grants, and center grants, including directing the NIH-funded Center for Biodefense Immune Modeling (2005-2015). Publications : Authored 2 books and over 130 peer-reviewed papers in top journals like JASA , Biometrika , and Journal of Immunology .
Modhurima Dey Amin is an Assistant Professor at Texas Tech University's Department of Agricultural and Applied Economics within the College of Agricultural Sciences & Natural Resources. Her work bridges econometric methodologies with contemporary issues in food and agricultural markets, emphasizing precision agriculture, energy economics, and industrial organization. PhD in Economics (Econometrics & Quantitative Economics) – Washington State University MSc in Statistics – Washington State University MSc in Applied Financial Economics – Illinois State University BSc in Economics – University of Dhaka, Bangladesh Dr. Amin's research explores: Food safety in retail environments and equitable access to nutritious food Market structures of specialty crops, particularly apple varieties Structural change detection in high-dimensional datasets using statistical methods Machine learning applications in agricultural and environmental economics Key trends in her publications include: Machine learning for causal inference and predictive modeling in agricultural economics Food retail dynamics, including store closures and consumer behavior Environmental economics and sustainable agricultural practices Precision agriculture and technology-driven farm management Her teaching focuses on: Econometrics Agribusiness Finance Corporate Finance Economic theory and policy analysis Contact: modhurima.amin@ttu.edu | Personal Website
Matt Koslovsky is an Assistant Professor of Statistics at Colorado State University. He completed his PhD in Biostatistics at The University of Texas Health Science Center School of Public Health (UTHealth) in 2016 and served as a Post-Doctoral Research Associate at Rice University's Marina Vannucci lab from 2018-2020. Prior to joining CSU in 2020, he worked as a statistical consultant at Johnson Space Center's Biostatistics Lab. PhD, Biostatistics (2016), UTHealth School of Public Health Post-Doctoral Research Associate (2018-2020), Rice University Assistant Professor (2020-Present), Colorado State University His research spans Bayesian methodology and its applications across diverse domains: Theory: Bayesian modeling, variable selection, graphical models, nonparametric Bayes Applications: Cancer prevention, mental health, microbiome analysis, space health, ecological momentary assessment Recent publications demonstrate methodological advancements in: Bayesian variable selection for rare variants Integrated population modeling Compositional data analysis Continuous-time hidden Markov models mHealth data processing Microbiome mediation effects Current advisees include: Hyungjoon Kim (PhD candidate) Brody Erlandson (PhD candidate) Suppapat Korsurat (PhD candidate)
Michael Nothnagel is a Professor at the University of Cologne, where he leads the Department of Statistical Genetics and Bioinformatics within the Cologne Center for Genomics (CCG). His work spans statistical genetics, genetic epidemiology, and forensic genetics, focusing on methodological development and large-scale genomic data analysis. His research interests encompass theoretical and applied statistical genetics, with emphasis on human genetic diversity, disease etiology, and forensic applications. Key areas include Y-chromosomal phylogeography, genome-wide association studies for complex diseases, development of statistical methods for variant interpretation, and forensic marker optimization. His group leverages next-generation sequencing data and specialized forensic markers to address questions in population history, disease mechanisms, and identification systems. Recent publications reveal a strong focus on computational approaches to genetic analysis, including spatial frequency interpolation for haplogroup mapping, polygenic risk score applications for behavioral traits, and advanced methods for variant classification. His work demonstrates consistent integration of statistical theory with practical applications in medical and forensic genetics, often through international collaborations like the VISAGE Consortium. Nothnagel maintains active involvement in the Cologne Center for Genomics, contributing to seminars and collaborative projects including the upcoming 34th International Genetic Epidemiology Society meeting. His research group operates at the intersection of computational biology and medicine, with particular strengths in handling complex genomic datasets and developing novel analytical frameworks for genetic epidemiology.
Professor Ian Simpson is a faculty member at the University of Edinburgh's School of Informatics , holding the Chair in Biomedical Informatics and serving as Director of the UKRI Artificial Intelligence CDT in Biomedical Innovation . He teaches courses including Foundations in Biomedical Artificial Intelligence Research and supervises PhD students across multiple programs. Current Supervision: Aidan Marnane, Antoine Lain, Barry Ryan, Tariro Chatiza, Sebestyén Kamp, Maria Dolak, Hanane Issa, Juliana Rodriguez Cubillos Past Supervision: Xin He (2017), Maciej Pajak (2018), Emilia Wysocka (2019), Samuel Heron (2019), Alba Crespi (2022), Magdalena Navarro (2022), Michael Yates (2023), Jamie Campbell (2023), Nicholas Moir (2024) Research Focus: Developing computational and machine learning methodologies for biological systems analysis, particularly brain development and neurological diseases. Key interests include high-throughput brain data analysis, graph-based biological modeling, molecular phylogenetics, and brain disease molecular modeling. Publication Trends: Recent work emphasizes multi-omic data integration through graph neural networks (MOGDx), batch correction challenges in breast cancer datasets, and longitudinal stratification approaches for Parkinson's disease. His articles demonstrate applications of AI in heterogeneous disease diagnosis while preserving biological validity. Scientific Recognition: Fellow of the Alan Turing Institute (2021-) Member of RSE Young Academy of Scotland BBSRC Pool of Experts member (2021-) STEM Ambassador Laboratory Affiliations: Leads the Biomedical Informatics group within the Institute for Adaptive & Neural Computation, with affiliations to Edinburgh Neuroscience, Patrick Wild Centre, and the Simons Initiative for the Developing Brain.
