Sebastian HöhnaI’m a computational biologist with focus on Bayesian phylogenetic inference. Natural selection by means of adaptation and genetic inheritance are key principles in evolutionary biology. Evolutionary relationships are commonly depicted by phylogenetic trees. Using phylogenetic methods we can learn about the evolutionary history of species and the processes that have contributed to present diversity. I’m developing statistical and computational methods to infer phylogenies from molecular sequence data. These methods additionally identify periods of adaptive genetic evolution at lineage or genes. Furthermore, I develop mathematical models to study macroevolutionary patterns, such as, diversification rate variation over time and among lineages and episodes of global mass extinctions.
John ClarkeI'm an evolutionary biologist and palaeontologist interested in testing drivers of phenotypic and species diversity using phylogenetic comparative methods. Having first applied these methods to Mesozoic fishes, I now apply them to large datasets of extant fishes, plants and insects.
Postdoc (Humboldt fellow)
Alessio CapobiancoI am a paleontologist and evolutionary biologist investigating how biological diversity evolves in concert with changes in the Earth system, mainly focusing on fishes. My research explores how fossils reveal fundamental and entirely unexpected aspects of the evolutionary history of living organisms, and examines diversity and biogeographic patterns through the lens of paleontological data. I am also interested in how sampling and coding of morphological data impacts phylogenetic inference and the estimation of evolutionary timescales.
Nicola HeckebergAs an evolutionary palaeobiologist, I am interested in morphological character changes of vertebrates, especially mammals, through time. Functional trait evolution in ruminants and other artiodactyls are the focus of my research, integrating fossil and extant taxa and molecular and phenotypic data in comprehensive time calibrated analyses.
Luiza FabretiI'm interested in computational and evolutionary biology. Specially, improving the way we model evolution. My research involves Bayesian phylogenetic inference and underlying topics such as, model selection, testing the adequacy of the most commonly used substitution models and assessing convergence of the Markov Chain Monte Carlo.
Ronja BillensteinI’m a biologist working on statistical methods for inferring the evolutionary history of closely related populations. These methods are based on coalescent theory and a mutation process. I am using simulated as well as empirical data to test and to validate these methods, hoping to provide an accurate, powerful and computationally feasible approach.
Killian SmithI am a computer scientist working with Bayesian phylogenetics and substitution models, and am interested in applying concepts and theory from CS in this domain. In my research I will be exploring covarion models, mixture models, and Markov modulated continuous Markov chains (MMCMC), and their effects on phylogenetic tree construction. Some of my other research interests include type theory, functional programming, de novo sequencing, multiple sequence alignment, genome assembly, and machine learning.
Bjørn T. KopperudI am an evolutionary biologist who is interested in estimating speciation- and extinction rates from phylogenetic trees. In my research, I have been investigating the challenge associated with non-identifiability in the time-variable reconstructed birth-death model, as presented by Louca & Pennell (2020) in Nature. Specifically, we have found that rapidly changing speciation and extinction rates can be robustly inferred in spite of non-identifiability. Additionally, I am working on developing a fast algorithm for a lineage-specific birth-death model with finite categories, which is used to infer shifts in rates along the phylogeny. Previously, this model has relied on Monte Carlo methods for estimating the rate and number of shifts along each branch. With our new approach, we do not need to use the Monte Carlo approach. Consequently, our method is much faster, there is no stochasticity in the estimation procedure, and we can readily fit the lineage-specific birth-death model to large trees with tens of thousands of taxa. My other interests include data mining using natural language processing, brain size evolution and phylogenetic comparative models of adaptation. My favorite organisms are bryozoans and ruminants!
Allison HsiangMy research uses statistical modeling, Bayesian inference, Big Data, and machine learning to understand morphological evolution and macroevolutionary patterns and processes. I am particularly interested in bringing a ‘next-generation’ approach (e.g., high-throughput automated data generation) to studying fossils and morphology in a phylogenetic context. I am also interested in the interplay between morphology and genetics, including methodological considerations during phylogenetic inference.
Independent Researcher (VR Grant) at Stockholm University