From the clinic to microgravity, I study how infection and immunity shape the brain — and how computation can make that knowledge actionable.

Where I come from

I trained as a medical doctor at Universidad Nacional de San Agustín in Peru, graduating with High Distinction. My clinical training spanned internal medicine, surgery, pediatrics, obstetrics and gynecology — and, crucially, a high-difficulty rural placement on Taquile, an isolated, high-altitude island in Lake Titicaca. There I delivered primary and preventive care with limited diagnostics, delayed transport, and no specialist backup. Reasoning carefully from incomplete, noisy data under constraint is the same discipline computational biology demands, and it is the thread that connects my clinical and computational work.

What I work on

My research sits at the intersection of infectious disease, neuroinflammation, and space health. Within the NASA Open Science Data Repository (OSDR) Brain Analysis Working Group, I develop VIRALL, a reproducible workflow for viral detection and characterization across sequencing platforms — Nanopore, PacBio, Illumina (paired and single-end), DNA-seq, RNA-seq, hybrid, and single-cell — applied to viral reactivation studies in Alzheimer's disease. Sparse viral signal against host background makes sensitivity and reproducibility the central engineering problems, and VIRALL addresses both by unifying assembly, classification, annotation, quality assessment, and abundance quantification in one path.

I also study microbial adaptation to spaceflight. Comparing Kalamiella piersoniiisolates from Earth and the International Space Station, I built a pipeline for lipopolysaccharide (LPS) gene detection, annotation, and pathway mapping, and identified differences in lipid A and LPS modification pathways that may relate to persistence in microgravity. In parallel, my master's thesis, MicroSeq16S Upgrade, modernized a microbiome pipeline for systemic autoimmune disease — an automated Snakemake workflow integrating QIIME2, DADA2, and PICRUSt2 with multi-step quality control, contamination filtering, and functional prediction — completed while I was serving in Peru's rural medical service.

How I work

I build reproducible, well-documented pipelines in Python, R, and SQL, using Snakemake, QIIME2, DADA2, and PICRUSt2, and I am equally comfortable at the bench — ELISA, cell culture, and immune profiling — and at the structural level, having worked on glycan 3D alignment and similarity visualization at the IMMEI in Bonn. That range lets me move between a biological question and the computational method it actually requires.

Where I am going

In a PhD, I want to develop precision tools for rapid pathogen detection and to uncover how immune–microbial interactions influence brain function. I am a co-inventor on a microfluidic chip for rapid pathogen detection developed with the German Aerospace Center (DLR), and I see diagnostics and computational biology as complementary: one generates timely data, the other turns it into decisions. Spaceflight offers an unusually clean testbed for these questions — immune dysregulation, microbial adaptation, and neuroinflammation are all accelerated and measurable in astronaut cohorts — while the same tools must work in the resource-limited clinical settings where I began.

I am applying to PhD programs in genomics, bioinformatics, and aerospace medicine because that is where my clinical intuition, computational training, and research questions converge. I bring a physician's sense of what matters at the bedside, a bioinformatician's rigor about what the data can support, and a demonstrated ability to do serious work under difficult conditions.