CCS Student Mentors


Keep Calm and Analyze Data

The CCS Student Mentors program is a peer mentorship program in computational science, coordinated and sponsored by CCS.  Mentors are a network of computationally advanced students, available first by email, then by appointment as needed to obtain help with specific questions regarding data and its analysis.


Our Vision for the Program

We believe that a vibrant and thriving computational community at UM can accelerate research workflows for wet labs as well as dry labs, turn data into knowledge, and open up questions that become tangible with advanced computing tools.

Our vision for the CCS Student Mentors Program is to operate as a nucleus of computational activity at UM by fast-tracking computational experiments towards results and, perhaps more importantly, providing a mechanism for the UM computational community to flourish through the connection of researchers, the exchange of ideas and formation of collaborations.


Benefits of the Program

The CCS Student Mentors program:

  • operates as a unified nucleus of computational activity for the University, contributing to the creation of a culture of utilizing the power of computational approaches in solving problems
  • offers an avenue for student mentors to gain valuable experience and training in mentorship before they graduate
  • provides a framework in which to monitor problems that come up consistently, therefore enabling the development of trainings that address those problems
  • gives structure to the peer mentorship that is already happening, and formalizes giving credit to the student mentors who dedicate time to serving their community
  • accelerates computational troubleshooting and therefore the pace of research for those who tap into the Program.


-Need help with your script?

-Are you getting error messages that don’t make sense?

-Do you have data and don’t know where to start?

Student Mentors can help!

Although we understand that (especially when you are just getting started) the documentation alone may not be written in an accessible way, we encourage you to come to the Mentors with a specific question, a specific error message, or a specific problem you are trying to solve.

The CCS Student Mentors GitHub Wiki, written by the founding Mentors, is currently being updated.


Areas of Expertise

The CCS Student Mentors Program extends its services to all Schools and Colleges at UM, and welcomes the connections between diverse sets of disciplines based on the common ground of computational tools and methods. The program currently includes mentors from computational biology and marine science, and it is our hope that it will expand into more disciplines as the computational methods employed in these fields become more commonplace.

In case you’re wondering . . . Should I learn Python or R or both?



Get in Touch

Email with your questions. This will create a ticket in our system, someone will respond within a day or so. If your query is not resolved by email, and you need a meeting, we’re happy to find a time to meet with you.



Prospective Student Mentors need to have expertise in a minimum of 3 computational techniques. Once selected, Mentors will be expected to attend a training session to go over the guidelines and the resources available. To apply, please fill out the CCS Student Mentors Application Form.  The program coordinator, Dr. Athina Hadjixenofontos, will contact your academic advisor or PI. Cleared students will be selected as CCS Student Mentors by the Steering Committee (which is made up of current CCS Student Mentors and Dr. Hadjixenofontos).






 Our Current CCS Student Mentors

Languages / Environments/ Tools

Dana Bis, University of Miami Center for Computational Science Student Mentor 2016-2017

 “Every bioinformatician knows
how frustrating searching through forums can be—so I would love to help you find solutions to an issue, even if I do not know the answer myself! “

Dana Bis

UMMSM  |  Human Genetics and Genomics

Area of Focus:  Whole Exome Sequencing, Whole Genome Sequencing, RNA-Seq, network analysis

  • Python
  • bash
  • R
  • bwa
  • samtools
  • GATK
  • freebayes
  • cytoscape
 Eric Bray, University of Miami Center for Computational Science Student Mentor 2016-2017

Eric Bray

UMMSM | Neuroscience/Medicine

Area of Focus:  RNA-seq, microarrays, visualization, data wrangling

  • Python
  • R
  • bash scripting
  • SAS
  • Matlab
Matthew Field University of Miami Center for Computational Science Student Mentor

Matthew Field

UMMSM |  Cancer Biology

Area of Focus:  Whole Exome Sequencing, Whole Genome Sequencing, RNA Seq, CNVs, SVs, methylation

  • Python
  • R
  • bash scripting
  • Linux
  • GATK
  • STAR
  • Novoalign
  • and most tools related to area of focus
 David Sant and family

 “You remember that guy in your computer class that just didn’t seem to get it? That was me.
After countless hours of reading forums and manuals I have learned how to use many of the available tools for the analysis of big data.
If I can figure this out, I know you can too. It is much easier with a little guidance, so I am glad to help show anyone how to get their analysis under way.”

David Sant

UMMSM | Human Genetics and Genomics

Area of Focus:  RNA-seq, hMeDIP-seq, Bisulfite Sequencing, ChIP-seq, Whole Exome Sequencing

  • R
  • Visualization
  • Bash scripting
  • Linux
  • GATK
  • Peak Calling (ChIP-seq)
  • Differential Expression (RNA-seq)

 Past CCS Student Mentors

Languages / Environments/ Tools
 Gino Chen, University of Miami Center for Computational Science Student Mentor 2016-2017

“It’s a pleasure to learn from other students’ work, so feel free to discuss any issues here!”

Gino Chen

RSMAS  |   Meteorology and Physical Oceanography (MPO)

Area of Focus:  Numerical Climate Modeling

  • matlab
  • fortran
  • bash
  • ncl and netcdf files
  • Matlab tools (e.g., svd, fft, kmeans/kmedoids, and hierarchical clustering tools)
Matt Danzi University of Miami Center for Computational Science Student Mentor

Matt Danzi

UMMSM |  Neuroscience

Area of Focus:  RNA-seq, ChIP-seq, ATAC-seq, methylation-seq, machine learning, network/graph analyses

  • Java
  • Matlab
  • R
  • Python
  • bash scripting
  • Javascript
  • some Perl
  • Tools related to next generation sequencing data


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