I'm often asked what my title is - right now (and likely forever) I'll have a hard time answering that question. And I don't know about you, but I think that's incredibly exciting.

After much rumination I've come to realize - that this is by design. I hope to carve out my niche in the world as a lifelong learner and a lifelong helper.

Hi there! My name is Emil - welcome to my little corner of the web. I received formal education in biochemistry and bioinformatics and have significant exposure to software engineering, genomics, proteomics, data science, machine learning, drug discovery/design, and healthcare informatics. I have worked as a clinician as an EMT across many different levels of care and currently serve my community at a level I trauma center. I am the founder of Byte-Sized Biology, my personal blog which I hope will grow to my digital library containing all my thoughts about biology, machine learning, and anything else my mind chooses to fixate on.

I'm also a deeply passionate jazz pianist and foodie. When I'm not talking shop, you'll find me trying out new restaurants, hanging out with my two dogs, or sipping on some coffee!

I'm so glad to have made your acquaintance wherever you might find yourself!

❓ What am I doing Right Now?

Computational Genomics Research - Loyola University Chicago
Working with Dr. Yoel Stuart

I'm currently contributing to computational and bioinformatics workflows supporting large-scale mitochondrial genome analysis and comparative genomics research. Work involves automation of genomic processing pipelines, data organization, workflow reproducibility, and scalable handling of biological datasets.

• Writing blogs for Byte-Sized Biology
• Currently looking for new roles in bioinformatics, computational biology, and machine learning
• Working in a level 1 trauma center as an ED Technician
• Trying to convince myself that sleep is somewhat optional

• Publish more technical writeups
• Contribute to open-source bioinformatics tools
• Learn advance single-cell workflows
• Perform statistical analysis on more data
• Build interactive biology tools
• Focus on projects that bridge healthcare, machine learning, and computational biology

🧬 Byte-Sized Biology

Byte-Sized Biology

A digital garden for biology, machine learning, and scientific curiosity.

Open Byte-Sized Biology →

Byte-Sized Biology is an evolving collection of projects, visualizations, essays, and scientific explorations focused on biology, computation, and healthcare.

Topics range from proteins and genomics to machine learning, healthcare systems, and the increasingly strange intersection between biology and computation.

🩺 Healthcare Experience

My background in healthcare started in prehospital medicine, where I worked as an EMT providing emergency and interfacility patient care across a wide range of clinical settings. This role eventually led me into hospital emergency medicine, where I now work as an Emergency Department Technician in a high-acuity emergency department environment.

Working in emergency medicine fundamentally changed the way I think about communication, design, efficiency, and human decision-making under pressure. It also gave me firsthand exposure to the operational realities of healthcare - the good, the bad, and the very very deeply confusing.

Day-to-day, my work involves supporting physicians and nurses with EKG acquisition, phlebotomy, patient monitoring, specimen collection, trauma/stroke/STEMI activations, and general emergency department operations. More importantly, it's taught me how much healthcare depends on systems that are not only tehcnically functional, but actually usable by exhausted humans at 3 AM.

A lot of my interest in healthcare informatics and computational biology ultimately comes from that perspective: the belief that technology in medicine should reduce friction, improve understanding, and make life easier for both patients and providers - not just generate more buttons to click.

🖥️ Featured Projects

Genomic Colocalization Pipeline

Go to GitHub

Developed modular statistical genetics workflows integrating GWAS and QTL datasets for scalable colocalization analysis. Pipeline work included LD matrix generation, SNP harmonization, allele matching and strand-flip handling, VCF preprocessing, PLINK integration, and coloc/SuSiE workflow preparation using 1000 Genomes reference datasets. Emphasis placed on reproducibility, automation, and maintainable pipeline architecture.

RNA-seq Transcriptomics Pipeline

Go to GitHub

Developed an RNA-seq transcriptomics pipeline for data ingestion, alignment, quantification, and downstream analysis using Python, R, and standard bioinformatics tools.

ALDH2 Structural Analysis

Go to GitHub

Structural analysis of the ALDH2 E487K mutation using PyMOL and PDB structures to evaluate dimer interface disruption, NAD+ binding effects, and catalytic function.

Machine Learning Classification Pipeline

Go to GitHub

Developed a machine learning classification pipeline with feature engineering, preprocessing, model evaluation, and deployment using scikit-learn.

Fetal Health Classification (R)

Go to GitHub

Statistical modeling and machine learning analysis of cardiotocography (CTG) data to classify fetal health outcomes and identify clinically relevant patterns.

COVID-19 Severity Clustering

Go to GitHub

Applied principal component analysis (PCA) and K-means clustering to COVID-19 epidemiological data to identify patterns in pandemic severity and group countries based on shared outcome metrics.

🖱️ GitHub Activity

A snapshot of my GitHub activity across bioinformatics, machine learning, and scientific software projects.