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Vincenzo NicosiaView profile

Senior Lecturer

• Complex Networks
• Multilayer & Multiplex Networks
• Network Dynamics
• +7 more

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.

Kaie KubjasView profile

Associate Professor

• Algebraic Statistics
• Nonlinear Algebra
• Matrix and Tensor Decompositions
• +5 more

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.

Wolfgang WiechertView profile

Professor

• Bioprocess Optimization
• Metabolic Engineering
• Systems Biotechnology
• +3 more

Prof. Wolfgang Wiechert is a Universitätsprofessor at Forschungszentrum Jülich GmbH, leading the Computational Systems Biotechnology group within the Institute of Bio- and Geosciences (IBG-1). His research focuses on integrating computational methods with experimental biotechnology to optimize bioprocesses, particularly in metabolic engineering and systems biology. Key areas include metabolic flux analysis, high-throughput screening, and Bayesian statistical methods for process optimization. Research interests span Bioprocess design and scale-up (e.g., itaconate production, fungal cultivation) Automated microbioreactor systems and robotic workflows Development of computational tools for metabolic modeling (e.g., hopsy, pyFOOMB) Application of machine learning in bioprocess analytics Recent publications emphasize Bayesian approaches for flux inference, strain characterization automation, and integration of omics data with process analytics. Collaborations span industries and academic partners, driving innovations in sustainable biomanufacturing and enzyme production systems.

Aurélien TellierView profile

Associate Professor

• Plant evolution
• Population genetics
• Adaptation of plants to environmental factors
• +4 more

Prof. Aurélien Tellier is an Associate Professor of Population Genetics at the TUM School of Life Sciences within Technical University of Munich (TUM). His research focuses on plant evolution and population genetics, particularly the adaptation of plants to environmental factors like climate and parasites. He investigates coevolution between plants and parasites, long-term seed dormancy mechanisms, and develops population genetic theories for genome evolution. His empirical work uses DNA sequence analysis to study South American wild tomato species. Prof. Tellier holds degrees in agronomy, plant biology, genetics, and statistics from ENITA (Bordeaux) and INAPG (Paris). After completing his PhD at the John Innes Centre/University of East Anglia (UK), he conducted postdoctoral research at LMU Munich, supported by the Volkswagen Foundation and DFG. He joined TUM as Associate Professor in 2012. His research explores how biotic and abiotic factors drive plant adaptation, with a focus on evolutionary transitions in reproductive strategies, demographic history reconstruction through genomic data, and the interplay between seed banking and genetic diversity. His work bridges theoretical population genetics with empirical genomic studies to address fundamental questions in plant evolution. Prof. Tellier's recent publications highlight advancements in integrating genomic and epigenomic data for population history inference and understanding host-pathogen interactions. His awards include the Ctibor Blattný Medal (2022) and a VW Foundation Fellowship (2007–2010).

• Applied Statistics
• Machine Learning
• Sequential Decision-Making
• +6 more

Amitis Shidani is a DPhil in Statistics student at the Department of Statistics, University of Oxford, supervised by Prof. Arnaud Doucet and Prof. George Deligiannidis. She is broadly interested in Applied Statistics and Machine Learning, with a focus on Sequential Decision-Making, Bandit Learning, and the integration of optimization, robust statistics, fairness, and causal inference for real-world applications. Education: Bachelor’s in Electrical Engineering and Computer Science (double major) from Sharif University of Technology, where she worked on Causal Inference in Computational Genomics with Babak Khalaj. Professional Background: Lead data scientist at CafeBazaar (Iranian Android app-store with 40M+ users) for two years prior to Oxford. Her research interests span Sequential Decision-Making, Game Theory, Optimization, Robust Statistics, Conformal Prediction, and Causal Inference. She is affiliated with the Computational Statistics and Machine Learning and Statistical Theory and Methodology research groups. Recent publications highlight her work in diverse areas, including martingale concentration inequalities, AI deployment in healthcare, transformer architectures, and theoretical machine learning. Her office is located at G.01, and she uses she/her pronouns.

Tracy A HeathView profile

Associate Professor

• Evolutionary Biology
• Phylogenetics
• Computational Biology
• +5 more

Dr. Tracy A. Heath is an Associate Professor at Iowa State University, specializing in evolutionary biology and computational phylogenetics. Her research focuses on developing Bayesian statistical methods and software tools like RevBayes to analyze molecular, morphological, and fossil data. She leads the Heath Lab, advancing phylogenetic inference and integrating paleontological evidence into evolutionary studies. Tracy holds a B.A. in Biology from Boston University (2000) and a Ph.D. in Ecology, Evolution, and Behavior from the University of Texas at Austin (2008). She actively contributes to open-source initiatives such as the Taming the BEAST educational platform and GitHub repositories for phylogenetic software development. Her work bridges theoretical frameworks with applied computational methods, emphasizing the importance of fossil data and model-based approaches in understanding biodiversity patterns and evolutionary processes. Key Contributions: RevBayes: Bayesian phylogenetic inference software Taming the BEAST: Community-driven BEAST 2 tutorial resource Fossil integration methodologies Phylogenetic network simulation (SiPhyNetwork) Her research spans topics including adaptive evolution in waterfowl, genomic studies of penguin adaptation, and resolving phylogenetic relationships in diverse taxonomic groups. Tracy advocates for inclusive educational practices, integrating museum collections and computational training to enhance biodiversity science accessibility.

Laura ScottView profile

Research Professor

• Genetic Epidemiology
• Statistical Genetics
• Type 2 Diabetes Research
• +3 more

Laura Scott is a Research Professor in the Department of Biostatistics at the University of Michigan School of Public Health. She holds a PhD in Cell Biology from Johns Hopkins University (1993) and an MPH in Epidemiology from the University of Michigan (1995). Her career includes postdoctoral work at the Joslin Diabetes Center and the University of Michigan, where she has been a faculty member since 2003. Dr. Scott chairs the Biostatistics Diversity, Equity, and Inclusion Committee and actively promotes racial equity in academia and community settings. Her research focuses on genetic and epigenetic factors influencing Type 2 Diabetes (T2D), psychiatric disorders, and metabolic traits. Key projects include the FUSION study (integrating multi-omic data in T2D-related tissues) and the InPSYght project (whole-genome sequencing of African American bipolar disorder/schizophrenia cases). Methodological contributions include eQTL analysis, imputation quality in diverse populations, and meta-analysis techniques. Dr. Scott’s work bridges computational genetics and clinical applications, with studies on ACE2 expression in adipose tissue, genetic variants in β-globin loci linked to pneumonia susceptibility, and polygenic risk scores for blood pressure. She collaborates with global initiatives like the Michigan Genomics Initiative and leads the Center for Statistical Genetics.

Maik KschischoView profile

Professor

• Artificial Intelligence in Healthcare
• Machine Learning
• Biomedical Data Analysis
• +6 more

Maik Kschischo - Professor of AI in Healthcare Prof. Dr. Maik Kschischo holds a professorship at the Institute of Computer Science (Department 4) of the University of Koblenz. He leads the AI in Healthcare research group, focusing on developing artificial intelligence methods to address challenges in biomedicine. His work integrates machine learning, statistical modeling, and mathematical frameworks to analyze high-dimensional biomedical datasets, including cancer genome data and electronic health records. Collaborations with clinicians, biologists, and medical experts drive his research toward improving medical decision-making and understanding biological complexity. Research Focus Machine Learning for Causal Dynamic Models: Developing methods to infer causal relationships from biomedical data. Cancer Biology: Investigating replication stress, chromosomal instability (CIN), and their roles in tumor progression. Key projects include the DFG Research Unit 2800 on chromosome instability mechanisms. Healthcare AI Applications: Creating AI tools for precision medicine (e.g., antibiotic selection frameworks for sepsis) and pandemic response (e.g., SARS-CoV-2 mutagenesis analysis). Grants & Collaborations Data2Health Research College (2022–2025): Explores trustworthy IT systems for medical data analysis, funded by the Rhineland-Palatinate Ministry of Science and Health. DFG Research Unit 2800 (2018–2026): Investigates chromosomal instability mechanisms in cancer, collaborating with institutions like the University of Göttingen. Publications Recent work emphasizes AI-driven solutions for clinical challenges (e.g., dynamic dose optimization, antibiotic selection) and cancer biology (e.g., replication stress-CIN links, genomic instability profiling). Publications span machine learning, systems biology, and translational medicine, reflecting interdisciplinary strengths. Labs & Teams Leads the Artificial Intelligence Research Group at the University of Koblenz, fostering collaborations between computer science, medicine, and biology to advance healthcare innovation.

Dylan KiltschewskijView profile

Research Fellow

• Bioinformatics
• Epigenetics
• Genomics
• +5 more

Roles & Affiliations: Dylan Kiltschewskij is a Postdoctoral Research Fellow in the Molecular Neurobiology department at the University of Newcastle's School of Biomedical Sciences and Pharmacy. He also holds a Casual Academic appointment at the same institution. His primary affiliation is with Professor Murray Cairns’ Complex Disorder Group. Education: Bachelor of Biomedical Science (First Class Honours), University of Newcastle (2015) PhD in Medical Genetics, University of Newcastle Research Interests: Dylan’s work focuses on the intersection of neuroscience, genetics, and psychiatric disorders. He employs multidimensional molecular techniques, systems biology, and statistical genetics to investigate: Genome-wide association studies (GWAS) for psychiatric disorder mechanisms Epigenetic profiling (DNA methylation) for schizophrenia risk stratification Noncoding RNA (microRNA, circRNA) roles in neuronal differentiation Drug repurposing opportunities through biochemical exposure studies Publications Trends: His 15 most recent articles (2020–2025) emphasize: Cardiovascular toxicity in cancer therapies Cortical anatomy-genetic biomarker correlations Federated neuroimaging analysis in psychiatric disorders Molecular mechanisms of neurodevelopmental disorders Awards: 2016 Australian Postgraduate Award 2015 Faculty of Health and Medicine Medal Multiple undergraduate prizes (2014–2015) Grants & Supervision: Principal supervisor for a current PhD student researching cancer genetics Total grant funding: $39,000 across 7 grants (2018–2022) Highlighted grant: 'Utilising Epigenetic Risk for Schizophrenia as a Platform for Precision Medicine' ($10,000, 2022) Labs/Teams: Active in the Cairns lab, focusing on psychiatric disorder mechanisms and precision medicine applications.

Laurie S. StevisonView profile

Associate Professor

• Evolutionary Genomics
• Speciation
• Recombination
• +10 more

Laurie S. Stevison is an Associate Professor in the Department of Biological Sciences at Auburn University, where she also serves as Coordinator for the Graduate Certificate in Computational Biology. Her research is centered on evolutionary genomics, using model systems such as primates and fruit flies to investigate speciation, recombination, and introgression. Education: Postdoctoral Fellow – University of California San Francisco, Institute for Human Genetics (2011–2015) Ph.D. – Duke University, Biology (2011) M.A. – Rice University, Ecology and Evolutionary Biology (2007) B.S. – Centenary College of Louisiana, Biophysics (2005) Her research interests include evolutionary genomics, speciation, recombination, population genetics, comparative genomics, and bioinformatics . She employs population genetic approaches to study how chromosomal inversions reduce recombination in hybrids, thereby contributing to species maintenance despite gene flow. Her work also explores recombination rate plasticity in response to environmental factors like temperature and maternal age. The selected publications reflect a strong trend in comparative and population genomics , with a focus on recombination rate evolution across species, hybridization in primates and Drosophila, and the role of structural variation in speciation. Her research spans taxa including great apes, gibbons, baboons, and Drosophila, contributing to broader understanding of genomic architecture and evolutionary processes. Scientific Contributions: Participation in the Great Ape Genome Project First evidence of higher Neanderthal ancestry in East Asians vs. Europeans Development of fine-scale recombination maps in multiple species Pioneering work on temperature-induced recombination plasticity Dr. Stevison advises graduate students and leads the Stevison Lab, which is actively involved in projects linking climate change to genetic variation in Drosophila robusta . She teaches courses in computational biology and bioinformatics at both undergraduate and graduate levels. Her lab uses both direct (genetic crosses) and indirect (linkage disequilibrium) methods to estimate recombination rates, and is expanding research to non-great ape primates.

Diego F. Alvarado-SerranoView profile

Assistant Professor

• Evolutionary Biology
• Population Genomics
• Bioinformatics
• +7 more

Diego F. Alvarado-Serrano is an Assistant Professor in the Department of Biological Sciences at Ohio University, part of the College of Arts and Sciences. His research integrates genomics, bioinformatics, spatial analysis, and population genetics to investigate how environmental heterogeneity and change shape evolutionary processes and biodiversity. Dr. Alvarado-Serrano earned his Ph.D. in Ecology and Evolutionary Biology and a Graduate Certificate in Spatial Analyses from the University of Michigan, following a Bachelor’s degree from Pontificia Universidad Católica del Ecuador. His research focuses on developing and applying novel bioinformatic and statistical tools to integrate genomic, phenotypic, and environmental data for forecasting species and community responses to environmental change. His work spans diverse taxa—including mammals, plants, and lizards—across temperate and tropical regions, particularly in the Andes. He emphasizes methodological innovation in population genomics, landscape genomics, and phylogeography to improve historical demographic inference and evolutionary forecasting. The 15 most recent publications highlight a consistent trend in advancing methodological frameworks for evolutionary inference, particularly through spatially explicit models, ecological niche modeling, and coalescent-based demographic analysis. His research spans plant mating systems, urban evolution in rodents, invasive species connectivity, and climate-driven divergence in birds and lizards, reflecting an interdisciplinary and systems-agnostic approach grounded in genomic data and statistical rigor. While no formal scientific awards are listed in the provided text, his work has been featured in prominent outlets such as Nature , LA Times , and The Molecular Ecologist , indicating recognition and impact in the scientific community. Dr. Alvarado-Serrano has mentored research and collaborated extensively, as evidenced by his co-authored publications with interdisciplinary teams. His lab integrates empirical data with rigorous modeling, supported by grants enabling genomic and field-based research, though specific grant details are not mentioned. His move to Ohio University in August 2019 marked the establishment of his independent research program focused on methodological development and environmental response forecasting. He leads a lab that is intrinsically interdisciplinary, question-oriented, and multi-faceted, combining genomic tools with spatial and environmental data to explore the interplay between selection, drift, and gene flow under changing landscapes.

Ava KhamsehView profile

Lecturer

• Biomedical Genomics
• Cancer Evolution
• Causal Machine Learning
• +3 more

Dr. Ava Khamseh is a Lecturer in Biomedical AI at the Institute of Genetics and Cancer , University of Edinburgh. She leads a research group at the intersection of cancer biology, population biomedicine, and causal machine learning, focusing on quantifying molecular interactions in biological systems. Research spans cancer initiation , evolutionary dynamics , and causal machine learning for biomedical data analysis. Specializes in single-cell biology , transcriptional trajectories , and higher-order genetic interactions . Key research themes include: Developing experimental-computational models to trace early cancer evolution via mutational and transcriptional dynamics. Designing non-parametric probabilistic ML methods for causal inference in UK Biobank and scRNA-seq data. Collaborations with experts in experimental biology , mathematics , and machine learning . Recent publications highlight her work on: Causal modeling of single-cell gene expression to resolve cryptic states in cancer progression. Investigating MITF-low melanoma subtypes and residual disease mechanisms. Machine learning applications in quantitative biomedicine , including Myalgic Encephalomyelitis biomarker discovery. Physics-inspired computational approaches, such as Ising model parameter estimation for biomedical applications. Projects funded by Cancer Research UK , MRC , and GlaxoSmithKline support her work on cancer inception, research culture, and biomarker prediction.

David A. KnowlesView profile

Assistant Professor

• Statistical Machine Learning
• Computational Genomics
• Bayesian Nonparametric Models
• +4 more

David A. Knowles is an Assistant Professor of Computer Science at Columbia Engineering and a Core Faculty Member at the New York Genome Center. He is also an Interdisciplinary Appointee in Systems Biology at Columbia University and an affiliated member of the Foundations of Data Science and Health Analytics centers. Education: Natural Sciences and Information Engineering, University of Cambridge MSc in Bioinformatics and Systems Biology, Imperial College London PhD in Machine Learning, University of Cambridge Engineering Department His research focuses on advancing statistical machine learning methods for computational genomics, addressing challenges in managing and analyzing large-scale genomic datasets generated by DNA/RNA sequencing technologies. Current work emphasizes modern data management solutions to improve error-prone, file-based analysis pipelines.

• Psychiatric Genetics
• Statistical Genetics
• Neuroimaging
• +5 more

Honorary Professor Sarah Medland is a distinguished Psychiatric and Statistical Geneticist at the University of Queensland's School of Psychology. She also serves as the Coordinator of the Mental Health Research Program and Group Leader (Psychiatric Genetics) at QIMR Berghofer Medical Research Institute. Professor Medland chairs the genetics working group of the ENIGMA neuroimaging consortium and is an active member of the Psychiatric Genomics Consortium. Professor Medland earned her PhD in Psychology from the University of Queensland in 2006, receiving the Dean's Award for Outstanding Research Higher Degree Thesis. She completed her BA Hons in Psychology (with Psychology Double Major and English Minor) at the University of Queensland in 2000. Professor Medland's research bridges Genetics, Psychology, Neuro-Imaging, Health Economics and applied Statistics, with a focus on understanding the genetic and environmental contributions to human behaviour and disease. Her work spans psychiatric genetics, statistical genetics, neuroimaging, and mental health genetics. She has published extensively with 5 book chapters, 419 journal articles, 64 conference papers, and 1 thesis to her name. Her recent publications demonstrate a strong focus on genetic contributions to brain structure and function, psychiatric disorders, and neurodevelopmental conditions, frequently employing genome-wide association studies, neuroimaging techniques, and advanced statistical methods. Order of Australia Medal (OAM) Fellow of the Australian Academy of Social Sciences (FASSA) Fellow of the Australian Academy of Health and Medical Sciences (FAHMS) Dean's Award for Outstanding Research Higher Degree Thesis Professor Medland leads the Psychiatric Genetics group at QIMR Berghofer Medical Research Institute, where she coordinates the Mental Health Research Program. Her research program involves large-scale genetic studies, neuroimaging analyses, and collaborations with international consortia including ENIGMA and the Psychiatric Genomics Consortium. She has supervised numerous students and early-career researchers in the fields of psychiatric genetics and neuroimaging.

• Biostatistics
• Neuroimaging
• Functional Connectivity
• +7 more

Dustin Pluta is an Assistant Professor in the Department of Biostatistics, Data Science, & Epidemiology at the School of Public Health, Augusta University. His academic work bridges biostatistics, neuroimaging, and data science, with a focus on developing advanced statistical methods for neuroscience and public health applications. Education: Ph.D. in Statistics, University of California, Irvine, 2020 M.S. in Applied Statistics, California State University, Long Beach, 2015 M.A. in Mathematics, University of California, Davis, 2008 His research interests lie at the intersection of biostatistics , functional brain connectivity , Bayesian modeling , and imaging genetics . He develops and applies statistical techniques to improve the quality and interpretability of neuroimaging data, particularly in the context of mental health and addiction behaviors. His work often involves Gaussian processes, penalized regression, and multivariate association testing. The recent publications reflect a strong trend in neuroimaging statistics , with emphasis on enhancing trial-level ERP analysis, modeling dynamic functional connectivity, and integrating genetic and brain imaging data. These works span disciplines including neuroscience, psychiatry, and biostatistics, demonstrating methodological innovation and application to real-world health problems such as e-cigarette use vulnerability. Scientific Awards and Honors: Distinguished Student Paper Competition Award, ENAR, International Biometric Society, 2018 Student Paper Competition Award, Joint Statistical Meetings, Statistics in Imaging, 2018 Robert L. Newcomb Memorial Endowed Graduate Student Award, UC Irvine, 2016 Summer Fellowship, UC Irvine, 2016 Chair’s Award, UC Irvine Department of Statistics, 2015 Dustin Pluta is actively involved in academic service, including serving as Committee Chair of the Journal Club and participating in faculty hiring and departmental meetings. He is also a reviewer for the journal Psychophysiology . He teaches graduate courses such as Applied Linear Models , Modern Methods of Multivariate Analysis , and the Data Science Capstone . While specific grant funding is not listed, his research output suggests active engagement in sponsored scholarly work. He contributes to a collaborative research environment within the Department of Biostatistics, Data Science, & Epidemiology, where interdisciplinary projects involving neuroscience, public health, and data science are central to the academic mission.