Sponsored by the LSU College of Science, the Department of Biological
Sciences, the Center for Computation & Technology and the Louisiana Biomedical Research Network
The primary goal for the seminar series is to increase student awareness of the importance of computational approaches to modern biological research. Traditional biology majors may receive no exposure to computational techniques, yet such skills are fast becoming de facto requirements for many careers in the life sciences. Conversely, majors in computational disciplines may receive little or no exposure to modern biological research that would make them aware of the pressing biological problems to which their skills could be applied.
We hope that this seminar series...
- Encourages students to take classes, do research, and seek out resources that allow them to cross the boundaries between biology and computation
- Raises awareness of the computational biology research being done at LSU and around Louisiana
- Encourages biology students to consider careers that leverage computational methods
- Encourages students in computational disciplines to consider careers in biological research.
LSU Computational Biology Seminar Media: Seminar Videos
When & Where
Talks are being held at the LSU Digital Media Center, at the Center for Computation and Technology
LSU Digital Media Center
Current Seminar Series Speakers
April 8, 2019, 4:30 PM, DMC Rm 1034
Dr. Lex Flagel
University of Minnesota, Adjunct Professor, Department of Plant and Microbial Biology and Bayer Crop Sciences, Research Scientist
An introduction to deep learning and its applications in evolutionary biology
Abstract: Deep learning is an exciting new technology that powers things like self-driving cars and voice assistants. The success of deep learning methods comes from that fact that they are exceptionally powerful at pattern recognition. These methods are starting to catch on in biology too, especially in genomics where we often want to detect patterns in DNA sequences. In this talk I first provide a primer on deep learning methods. The goal of this primer will be to give you a gentle introduction to how deep learning models are built and trained. Then I will present some recent examples where colleagues and I used deep learning to make inferences in population genetics.
Bio: I am an adjunct professor in the Plant and Microbial Biology Department at the University of Minnesota. I am also a research scientist at Bayer Crop Sciences. At Bayer my work is focused on developing new methods to accelerate crop breeding, and in my faculty role I work on problems in deep learning and population genetics. In both cases I use genomic technologies to create large amounts of data, and computational and statistical approaches to gain insights.
February 27, 2019, 4:30 PM, DMC Theater
Dr. April Wright
Southeastern Louisiana University, Faculty
Estimating phylogenetic trees from discrete morphological data- Modeling evolution to understand the past
Abstract: Phylogenetic trees display the relationships between organisms, and if scaled to absolute time, can tell a reader how long lineages have been evolving independently of one another. These trees are crucial components of comparative biological studies, and are used in analyses of organismal function, ecology, and evolution. Comparative studies of organismal and population biology often require the use of phylogenies that have been scaled to absolute time. The most important source of information to perform this scaling is the fossil record. However, most phylogenetic trees are built with DNA sequence data, whereas most fossils are known from morphological data, such as the presence or absence of a skeletal feature in a group of organisms.
As fossils comprise our only direct observations of biodiversity, incorporating them into phylogenetic studies is imperative. In recent years, there have been many advances in how researchers understand the evolution of morphological traits, and how fossils can be included in phylogenetic analyses. In this talk, we will discuss models of morphological evolution, and what assumptions those models make about how morphological characters evolve. We will also discuss divergence time estimation, and how mechanistic models of diversification and fossil sampling can be used to incorporate morphological data into inference of dated phylogenies.
Bio: April Wright has been faculty at Southeastern Louisiana University since fall of 2017. Prior to this, she was a National Science Foundation Natural History Collections Postdoctoral Fellow with Drs. Tracy Heath (Iowa State University) and Corrie Moreau (The Field Museum). Her main research interest has always been the incorporation of fossil information into phylogenetic inference, though she also works on phylogenetic comparative methods, and use of phylogenetic trees to answer questions about evolution. When Dr. Wright is in the classroom, she enjoys teaching computational biology, evolution and systematics. Outside of work, Dr. Wright enjoys reading science fiction, running, and playing with her children and dogs.