Christina Gancayco, Computational Consultant

Profile

When Christina Gancayco joined Stanford Research Computing in July 2023, she brought a wealth of experience and expertise as she began helping researchers to use our systems for their research. 

With a consultative approach that's grounded in her history with — and understanding of — researchers' needs and objectives, Christina is always prepared to help Stanford researchers troubleshoot problems, develop custom scripts for data analysis, and learn best practices on our HPC systems. 

One such researcher is bioinformatician Aastha Pal from the Kalbasi Lab in Radiation Oncology at the School of Medicine, who has written to us that she’s “especially grateful to Christina and [Research Computing’s code optimization expert] Zhiyong Zhang for their invaluable assistance” during the Sherlock compute cluster’s twice-weekly Zoom office hours.

Pal’s work “revolves around harnessing clinical data from patients with Sarcoma, as well as experimental data in the lab that requires me to explore various computational methodologies to demystify tumors and identify key targets, enabling the design of novel therapeutic interventions. During office hours I’ve learned a lot from Christina about effective programming, containerization, and setting up open source pipelines.”  

Beyond hitting the ground running as a data analysis and computation consultant, Christina and another recent arrival, Sara Cook, played pivotal roles in the expansion and improvement of the department's training curriculum and researcher communications (Slack channels and newsletter) efforts.

Before coming to Stanford, Christina worked at the University of Virginia, where she demonstrated her commitment to empowering lab-mates and faculty members in their research pursuits. 

"I have a soft spot for neuroscience and psychology projects since that’s where I got my start!", said Christina.

"My first exposure to high performance computing (HPC) was working in a neuroscience lab. It was my first week on the job and the lab manager was about to go on vacation for two weeks. He tasked me with analyzing the cortical thickness of ~30 patients’ brains in MATLAB. He said that each patient took about a day to process, so I might be halfway done with the analysis by the time he got back. I felt that was unacceptable, so I spent the first week figuring out how to use the University’s cluster. After a lot of Googling, debugging, and learning way more about PBS than I needed (which is to say, I didn’t need it at all), I had my first jobs running and finished analyzing all 30 subjects long before the lab manager got back."

Christina's dedication to mentorship and community engagement reflect a commitment to fostering a culture of collaboration and knowledge sharing. Here at Stanford she is a Research Computing lead training instructor. She coordinates and teaches “Lunch & Learn” (previously “Code & Coffee”) sessions and is a regular Slack chat contributor on the channels dedicated to our respective compute clusters. Previously, in Charlottesville, she volunteered with organizations such as Computers4Kids and FEMMES to inspire next-generation researchers — especially young women.

Stanford Research Computing is proud to have Christina on our team continuing her path of excellence in research computing support. With a focus on providing expert guidance, fostering collaboration, and empowering researchers to achieve their goals, Christina embodies the qualities of a trusted advisor and collaborator in the academic research landscape.

Select Consultation Projects

• Angiotensin II induces coordinated calcium bursts in aldosterone-producing adrenal rosettes 
  PI: Paula Barrett, UVA School of Medicine, Department of Pharmacology
  Role: Wrote a custom MATLAB GUI for performing quantitative analysis, wrote custom SLURM scripts for performing clustering analysis on the UVA HPC system, resulting in publication in Nature Communications
• PlaqView 2.0: A comprehensive web portal for cardiovascular single-cell genomics
  PI: Clint Miller, UVA Center for Public Health Genomics
  Role: Containerized R Shiny app using Docker and deployed on DCOS cluster, wrote GitHub actions for CI/CD
• An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models 
  PI: Matthew Panzer, UVA Mechanical and Aerospace Engineering
  Role: Wrote SLURM scripts for template creation and registration of brain MRI scans