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Reed Cartwright is an Associate Professor in the School of Life Sciences at Arizona State University. His research focuses on evolutionary biology, population genetics, genomics, and bioinformatics. He contributes to developing computational tools for genetic analysis and simulations, with applications in primate evolution, microbiome studies, and conservation genomics.
Dr. Beckett Sterner is an Associate Professor in the School of Life Sciences at Arizona State University. His interdisciplinary research bridges philosophy of science and biological practice, focusing on biodiversity science, evolutionary theory, and data governance. Research integrates conceptual and empirical approaches to investigate: Epistemological foundations of model selection and statistical inference Data governance and ethical frameworks for biodiversity research Evolutionary theory development and classification systems Intersections of philosophy with ecology, virology, and paleobiology Publications demonstrate consistent engagement with methodological challenges in evolutionary biology and ecology, including statistical model selection, phylogenetic analysis, and data integration. Recent work emphasizes equitable participation in biodiversity science and critical examination of classification practices. Dr. Sterner develops computational tools like the Modular Petri Net Assembly Toolkit (MPAT) and contributes to theoretical frameworks for understanding scientific objectivity in data-intensive research contexts.
Zoltan Szabo is a Professor of Data Science at the Department of Statistics, London School of Economics and Political Science. His research focuses on statistical machine learning, particularly kernel methods, information theoretical estimators, and scalable computation, with applications spanning safety-critical learning, style transfer, hypothesis testing, distribution regression, econometrics, and gene analysis. Affiliation : Department of Statistics, LSE Academic Rank : Professor Key Expertise : Kernel Methods, Information Theoretical Estimators, Scalable Computation His work integrates theoretical rigor with practical applications, addressing challenges in safety-critical systems and developing robust nonparametric methods. Szabo has published extensively on topics like Nyström approximation, Stein discrepancy, and random Fourier features, contributing to advancements in hypothesis testing, distribution regression, and GPU-accelerated kernel techniques. He has served as an Area Chair for top conferences (ICML, NeurIPS, AISTATS), moderated arXiv's stat.ML, and contributed to editorial roles at JMLR and ACM Transactions on Probabilistic Machine Learning. His recent articles emphasize scalable kernel methods for high-dimensional data, with applications in climate science, finance, and neuroimaging. Scientific Awards : Best Paper Award, NeurIPS 2017 HDR (Habilitation à Diriger des Recherches) with distinction, 2019 Programme Director of MSc Data Science, LSE As an advisor, Szabo mentors PhD students and interns in machine learning and statistics. His work often involves interdisciplinary collaboration, including grants with institutions like the Turing Institute and European Research Council.
Dr. Tingting Zhang is a Professor in the Department of Statistics at the University of Pittsburgh's Dietrich School. She holds a Ph.D. in Statistics from Harvard University (2008), an M.S. in Statistics from Harvard (2005), and a B.S. in Mathematics from Peking University (2003). Her research focuses on neuroimaging data analysis, human brain mapping, and Bayesian statistics, with applications to neurological disorders. She collaborates with clinicians and neuroscientists to develop statistical methods for analyzing multimodal brain imaging data, including fMRI and EEG. Her methodological contributions include Bayesian network models, high-dimensional data analysis, and variational inference techniques. Recent work examines functional connectivity across lifespan stages using HCP datasets and epileptic seizure analysis through directional brain networks. She has developed software tools including the Bayesian Modular and Indicator-based Dynamic Directional Model (BMIDDM) and the Bayesian hierarchical model for state-space stochastic block analysis. Dr. Zhang has taught courses in Bayesian statistics, stochastic processes, and experimental design. Her work bridges statistical theory with clinical applications, emphasizing translational research in neuroimaging. She maintains an active research program with over 30 peer-reviewed publications in top journals like Annals of Applied Statistics and NeuroImage.
مدرس پارهوقت
Jie He is a Part-Time Lecturer in the Department of Statistics at the University of Pittsburgh's Dietrich School. Their academic role focuses on statistical education, contributing to undergraduate and graduate programs in statistics and data science. While no explicit research description is provided, their listed Google Scholar publications highlight a research background in biomedical sciences, particularly in cancer biology, immunology, and virology, with a focus on cellular mechanisms and disease pathways. Research interests inferred from publications include cancer metastasis mechanisms, inflammatory responses in asthma, respiratory viral infections such as RSV, and signaling pathways involving HSP proteins and cytokines. These studies bridge molecular biology, immunology, and clinical applications. Jie He's work has explored topics like epithelial-mesenchymal transition, chemokine receptor roles in asthma, and autophagy regulation in viral infections. Their publications span collaborations in both basic science and translational research contexts.
مدرس ارشد
Sandipan Roy is a Senior Lecturer in the Department of Mathematical Sciences at the University of Bath, affiliated with the Centre for Mathematics and Algorithms for Data (MAD) and the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa). His research focuses on statistics, machine learning, and optimization methods, particularly in modeling complex network structures in high-dimensional data with applications in biomedical and social sciences. Education: PhD in Statistics from the University of Michigan (2015), MSc in Statistics from the Indian Statistical Institute, Kolkata (2010), and BSc in Statistics from the University of Calcutta (2008). Research Interests: High-dimensional inference, network modeling, unsupervised learning, distributed optimization, and applications in healthcare and genomics. Recent work includes developing methodologies for analyzing genomic data and predicting clinical outcomes in elderly populations using machine learning. Projects: Includes AI-driven disease cluster identification in immune-mediated inflammatory conditions, fuzzy cognitive mapping of brain networks, and high-dimensional network change-point estimation funded by NIHR, EPSRC, and the London Mathematical Society. Scientific Awards: Honorary Lecturer in Statistics (2023). Advising & Grants: Leads and collaborates on projects involving machine learning in healthcare and network analysis, with notable grants from the National Institute for Health Research and the London Mathematical Society. Supervises doctoral students through the SAMBa CDT program. Labs & Teams: Active member of the MAD Centre and SAMBa, contributing to interdisciplinary research in statistical applied mathematics and data science.
پژوهشگر ارشد
Dr. Jake Carson is a Research Fellow at the University of Warwick's Mathematics Institute, specializing in statistical methodology for integrating genomic data into epidemiological analyses. His work bridges infectious disease modeling, Bayesian statistics, and climate science. Previously, he developed Bayesian approaches for Raman spectroscopy diagnostics and palaeoclimate reconstruction at the University of Nottingham. Education PhD in Statistics (2015): University of Nottingham, Thesis: Uncertainty Quantification in Palaeoclimate Reconstruction Research Interests His research focuses on: Statistical methodologies for genomic epidemiology Bayesian model selection in complex systems Applications of Raman spectroscopy in diagnostics Climate modeling with palaeoenvironmental data Key Projects Health Protection Research Unit in Genomics and Enabling Data Scalable Bayesian methods for infectious disease modeling Nanoparticle assemblies for healthcare diagnostics Awards & Grants No awards listed, but his work has been supported by grants from the University of Warwick and collaborative institutions. Labs & Teams Core member of the Mathematics Institute at Warwick and collaborates with cross-disciplinary teams in epidemiology, climate science, and spectroscopy.
Furqan Aziz is a Lecturer in the School of Computing and Mathematical Sciences at the University of Leicester since 2022. Previously, he served as a Research Fellow at the Institute of Cancer and Genomic Sciences, University of Birmingham. He holds a Ph.D. in Computer Science from the University of York, UK, focusing on interdisciplinary research. His research interests include Spectral Graph Theory, Complex Networks, Machine Learning, and Bioinformatics. He applies these techniques in healthcare informatics, network analysis, and computational biology. Notably, his work explores disease phenotype modeling, multimorbidity prediction, and drug response analysis using machine learning. Recent publications highlight trends in network science applications, including link prediction, graph characterization, and predictive modeling in healthcare. His bioinformatics research bridges computational methods with medical data analysis, addressing challenges in personalized medicine and public health surveillance. No awards or grants are explicitly listed in his profile. He currently advises students in computational science and mathematical modeling, though specific advisee names are not provided. His work spans collaborations in academia and industry, emphasizing interdisciplinary problem-solving.
Dr. Xinghua Lu is a Professor in the Department of Biomedical Informatics at the University of Pittsburgh. He specializes in computational methods for identifying signaling pathways in biological processes and diseases, and statistical methods for knowledge extraction from biomedical literature. His work focuses on translational bioinformatics, systems biology, and natural language processing for text mining. Education: PhD from University of Connecticut Health Center Research Interests: Dr. Lu develops computational models to simulate biological signaling systems and applies latent variable models to analyze genomic and transcriptomic data. His recent projects include establishing a Center/Institute in Translational Bioinformatics and advancing NLP techniques for biomedical text analysis. Publications: His research emphasizes genomic alteration analysis, causal discovery from big data, and integrative modeling of gene expression. Key themes include cancer mechanism identification, systems biology applications, and machine learning-driven biomedical informatics. Affiliations: Department of Biomedical Informatics School of Computing and Information McGowan Institute for Regenerative Medicine (implied via research context)
مدرس ارشد
Dr. V Anne Smith is a Senior Lecturer at the School of Biology, University of St Andrews. Her research focuses on interdisciplinary applications of Bayesian networks to address challenges in antibiotic resistance, ecological modeling, and exoplanet science. She leads projects investigating socio-environmental drivers of antimicrobial resistance in East Africa, poultry genetics, and science communication through science fiction analysis. Dr. Smith collaborates across disciplines, including with institutions like the HATUA and CARE Consortia, and actively engages in public science outreach through events like the World Science Fiction Convention. She advises three PhD students and has contributed to over 50 research outputs. Key honors include the 2010 Most Valuable Professional award in database operations. Her work integrates computational methods with biological, medical, and social data to inform policy and advance scientific understanding. Education details are not explicitly stated in the provided texts, but her extensive academic publications and supervisory roles indicate a strong academic background in biological sciences. Her research spans computational biology, epidemiology, and environmental science, with a focus on developing novel methodologies like Bayesian network modeling for complex systems. She is affiliated with multiple research centers at St Andrews, including the St Andrews Centre for Exoplanet Science and the Institute of Behavioural and Neural Sciences. Dr. Smith’s recent publications emphasize causal Bayesian network applications to combat AMR, poultry stress genetics, and exoplanet media representation. Her lab’s work bridges theory and practice, offering tools for policy analysis and public health intervention. She also contributes to open-access datasets and software, advancing reproducibility in ecological and biomedical research.
Francesca Bagnoli is a Researcher at the Institute of Biosciences and BioResources (IBBR), part of the National Research Council of Italy (CNR) in Florence. She has been with IBBR since October 2014, following prior research roles at CNR's Institute of Plant Protection (IPSP) in Florence from 2011 to 2014. Her work is centered on forest genetics, conservation, and molecular marker applications. Education: Ph.D. in Agricultural and Forest Genetics, University of Florence (2002). Thesis: 'Isolation and molecular characterization of genes coding antioxidant enzymes in trees: Prunus persica and Pinus pinea'. Her research focuses on population and conservation genetics of forest trees, phylogeography, identification and use of molecular markers (SSRs, SNPs), and the molecular basis of adaptation in forest species using Bayesian statistical methods. She has contributed extensively to understanding the genetic diversity and evolutionary history of Mediterranean and European forest trees. Her recent publications (2021–2025) reveal strong trends in forest genomics, conservation planning, and climate adaptation. Key themes include latitudinal genetic diversity patterns in oaks, resilience of genetic diversity over geological timescales, spatial conservation planning for forest genetic resources, and microgeographical selection in conifers. Her work often integrates genomic data with ecological and paleoenvironmental evidence to infer demographic histories and adaptive processes. Scientific Projects and Collaborations: EU H2020 Forgenius: Improving access to forest genetic resources information. EU H2020 B4EST: Adaptive breeding for resilient forests under climate change. EU H2020 GenTree: Sustainable use of forest genetic resources in Europe. LIFE IP GESTIRE: Technical services for managing Lombard oak forests. Bilateral Italy-Montenegro projects on adaptive capacity of beech and pine along altitudinal gradients. Bagnoli has advised or collaborated with numerous researchers and institutions across Europe. While no formal students are listed, her role in large collaborative projects suggests significant mentorship and team leadership. She has contributed to major data platforms such as the GenTree Leaf and Dendroecological Collections, enhancing data accessibility for the scientific community. Laboratories and Research Teams: She is part of the Florence Division of IBBR-CNR, where she conducts research in forest molecular genetics and genomics. Her work is embedded within international consortia and EU-funded projects, involving close collaboration with INRAE (France), University of Florence, and other European research institutions. Her laboratory focuses on genetic analysis of forest tree populations using molecular markers and genomic tools.
Stefanie Muff is a Professor in the Department of Mathematical Sciences at NTNU, specializing in Bayesian statistical methods, quantitative genetics, and ecological modeling. Her work bridges statistical methodology with applications in evolutionary biology and wildlife research. Expertise: Bayesian inference, missing data modeling, genomic prediction, and habitat selection analysis Key affiliations: NTNU's Department of Mathematical Sciences Research interests focus on developing statistical frameworks for ecological and evolutionary questions, including studies on animal movement patterns, inbreeding effects in wild populations, and genomic prediction in non-model organisms. She actively contributes to methodological advancements in handling missing data and measurement error in ecological datasets. Recent work emphasizes applying Bayesian models to real-world ecological problems, such as dispersal genetics in vertebrate metapopulations and spatial variation in metabolic traits. Her interdisciplinary approach integrates statistics with empirical studies on birds and other wildlife. Notable collaborations: Alpine ibex conservation, house sparrow metapopulation studies, and genomic prediction frameworks Advocates for transparent science through teaching courses like ISTT1003 - Statistics and TMA4268 - Statistical Learning. Active in academic outreach with documentaries and workshops on ecological data analysis.
Dr. Nicolás Hernández is a Lecturer in Statistics at Queen Mary University of London's School of Mathematical Sciences, affiliated with the Data Science, Statistics and Probability Centre. He previously held positions as Senior Research Fellow at UCL's Department of Statistical Science and PDRA at the University of Cambridge's MRC Biostatistics Unit. He earned his PhD in Statistics from Universidad Carlos III de Madrid focusing on 'Statistical learning methods for functional data with applications to prediction, classification and outlier detection'. His research develops statistical/machine learning methods for high-dimensional and functional data applications across energy, economics, environment, demography, business, finance, health, and genetics. Key focuses include predictive confidence bands for functional time series, domain selection/classification in functional data, and outlier detection using Information Theory. Teaching responsibilities include module leadership for Biostatistics and Medical Statistics in the MSc Applied Statistics and Data Science program, and instructing Time Series Analysis for Business in the MSc Business Analytics program. His work emphasizes ethical considerations in biomedical research and practical implementation using R. Research outputs include 7+ peer-reviewed publications in journals like Biometrical Journal , Communications in Statistics , Nature Communications , and Entropy . His methods address challenges in functional data analysis, time series prediction, and anomaly detection across diverse domains. Professional activities include maintaining a research website (https://nicolashernandezb.github.io/) and participation in collaborative projects. Office hours are held weekly at the School Social Hub (MB-B11) on Thursdays 11:30-12:30.
مدرس ارشد
Vincenzo Nicosia is Senior Lecturer in Networks and Data Analysis at the School of Mathematical Sciences, Queen Mary University of London, and a member of the Centre for Complex Systems. His research deciphers the structure and dynamics of complex networks, with particular emphasis on multilayer and multiplex systems, random-walk processes, synchronisation, and their applications to urban analytics, neuroscience and epidemic modelling. Education & early career: While explicit degrees are not listed in the supplied text, Dr Nicosia has built an extensive publication record (100+ papers) since 2006, indicating long-standing academic training and international recognition in network science. Research interests: Nicosia’s work revolves around three inter-related pillars: Fundamental theory: random walks, diffusion, opinion dynamics, synchronisation and percolation on single and multilayer networks Methodological development: visibility graphs, first-passage observables, metadata-dependent embeddings, spectral and entropy-based metrics Data-driven applications: quantifying urban segregation and epidemic disparities, modelling cancer-spatial evolution, mining musical harmony networks, and analysing brain multiplex motifs His recent publications (2020-2023) reveal a strong focus on spatial stochastic processes —using random walks to measure segregation, mutation clustering in tumours, and the impact of city layout on COVID-19 spread—and on algorithmic inference in multiplex structures, including optimal percolation and compressed network representation. Grants & awards: He currently holds / has led EPSRC grant "Assessing spatial heterogeneity through random walks on graphs" (£162,886, 2019-2021). No other awards or fellowships are mentioned in the supplied material. PhD supervision & team: He advises an active cohort of doctoral researchers: Liam Fahey (temporal knowledge graphs), Yuhan Li (adaptive epidemic modelling), and Tom Roberts (stochastic sampling on lattice animals), among others. Outreach & service: Nicosia serves on the Council and Executive Committee of the Complex Systems Society, contributing to the governance and strategic direction of the international community.
دانشیار
Kaie Kubjas is an Associate Professor at Aalto University in the Department of Mathematics and Systems Analysis, School of Science. Since 2024, she has held a tenured position, following a tenure-track role from 2017–2024. She earned her PhD in Mathematics at Freie Universität Berlin (2013) under Professors Christian Haase and Klaus Altmann, with postdoctoral research at institutions including the Max Planck Institute and MIT. Her research focuses on applied nonlinear algebra, algebraic statistics, and their applications in biology (e.g., phylogenetics and 3D genome reconstruction), as well as matrix/tensor decompositions. She has organized major events like the European Women in Mathematics General Meeting 2022 and the 2025 workshop on Algebraic Statistics and Multistate Models. Kubjas serves on editorial boards of journals like SIAM Journal on Applied Algebra and Geometry and Annales Fennici Mathematici . Recent work includes advances in log-concave maximum likelihood estimation, 3D genome reconstruction, and structured matrix decompositions. Her students, such as Olga Kuznetsova (Second Place MEGA 2021 Poster Award winner), have contributed to these areas. She regularly contributes to seminars like the Algebra and Discrete Mathematics at Aalto, fostering interdisciplinary collaboration.
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