Scientific Computing PPFP Mentors

Dr. Peter Beerli


Title of Research: Computational population genetics

Description of Research Area: We develop Bayesian inference software for practical population biologists. This software is based on Markov chain Monte Carlo methods using coalescence theory. These methods use genomic data to analyze potentially complex population models used in biogeography or epidemiology. Research opportunities include algorithm development and improvements, probabilistic model comparison techniques, simulation testing the methods, and analysis of real data.

Special Research & Career Skills: Our group provides training in population genetics, in particular coalescence theory and software development. We will also provide mentoring opportunities including proposal writing, manuscript writing, and presentation preparation.

Website Links: LabMigrate Site

Dr. Alan Richard Lemmon


Title of Research: Uncovering the Tree of Life Using Big Data

Description of Research Area: My research group is interested in developing and implementing computational tools for enhancing the collection and analysis phylogenomic data. We develop resources for isolating genomic regions in non-model organisms and novel algorithms for assembly of these genomic regions from next-generation sequencing data. By leveraging genomic resources from model species scattered across the Tree of Life, we identify conserved genomic regions and develop molecular probes representing the natural variation in a group so that the same regions can be isolated and sequenced from any non-model species.

I am the director of bioinformatics at FSU’s Center for Anchored Phylogenomics (, which has enhanced the research of hundreds of collaborators by providing access to genome-scale data for non-model systems. Undergraduates, graduates, and postdocs in my group benefit from having access to vast amounts of DNA sequence data from across the Tree of Life. These data provide not only a valuable resource for meta-studies but also for raw material for which new computational methods can be developed.

Postdoctoral researchers could leverage the data collected by the Center to lead ambitious studies to evaluate the accuracy and effectiveness of currently-utilized computational approaches, develop novel computational approaches, or study broad-scale patterns of evolution by comparing genome-scale data across the Tree of Life. In addition to having access to the latest equipment for generating next-generation sequence data (e.g. liquid handling robots and the Illumina NovaSeq6000 sequencer) and over 200 Tera bases of genomic data from tens of thousands of species, postdocs would have access to the computation facilities that include six linux workstations each with dual 8-core Intel processors, four TB of solid state storage, and 256GB of RAM.

Special Research & Career Skills: The postdoctoral researchers will be provided training in project and data management, programming, bioinformatics, genomic library preparation, probe design for hybrid enrichment, as well as professional training to include manuscript writing, applying for jobs, and preparing grant proposals. The postdoctoral researcher will be immersed in a diverse environment created through frequent international workshops that the Center provides for collaborators worldwide.

Website Links: Department Profile; Lemmon Lab; Google Scholar
Center for Anchored Phylogenomics

Dr. Jose Mendoza-Cortes


Title of Research: Materials discovery and Materials Design for energy applications by machine learning algorithms

Description of Research Area: The Mendoza-Cortes groups has have been pioneering the Materials by Design over the last 12 years using numerous mathematical concepts in combination with new atomistic simulations and experiments. Our lab currently has 19 publications which have been cited around 4,912 times (source: Google Scholar). This is perhaps the highest citations number for someone who is under 32 years old.

Theoretical and computational studies are integral parts of research in interdisciplinary areas of science and technology. The advent of powerful modern computers, developments in sophisticated algorithms and theories, and access to a large amount of data from previous studies suggest that in the future, computational techniques would continue to play a dominant role in both fundamental and applied research. However, currently used computational methods have well-known limitations. Although a few groups have introduced automated reaction search algorithms and high-throughput studies with some success, myriads of unique possible pathways and combinations should be investigated by using accurate theoretical methods to furnish a reliable theoretical prediction of the reaction outcome. This makes the calculations prohibitively expensive, highly time consuming, and tedious compared to the actual experiments.

Inspiring from the recent success of deep-learning and artificial neural networks, we propose to apply them for the designing of novel materials for energy related applications. We would like to apply the principles of machine leaning to design solar energy materials, batteries, and energy storage devices. We would use existing machine learning algorithms and also develop our own code to tackle with the challenging problems in chemistry and materials science. A combination of fields would help us to analyze, understand, and rationalize the structure-activity relationships of numerous candidate systems and select the optimum ones for the experimental realization.

Special Research & Career Skills: 


Expertise on electronic structure calculations for both molecular and periodic systems, scripting/programming expertise, multiscale and atomistic simulation techniques, engineering devices, Monte-Carlo methods, reactive molecular dynamics, and force-field based simulations.

Career Skills:

Assistance with Job opportunities, training to submit proposals, formal coaching to present research findings.

Website Links: Maglab Condensed Matter Science; Mendoza-Cortes Lab

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