Brook Tilahun

Brook Tilahun

Computational Biology Scientist

Genovac

I am a Computational Biology Scientist working at an antibody discovery company, where I apply machine learning and AI approaches to accelerate in silico antibody discovery and protein engineering.

My work focuses on leveraging protein language models, structure prediction tools, and ML pipelines to identify and engineer therapeutic antibody candidates. I graduated from Concordia College with a B.A. in Neuroscience, Data Science and Psychology.

This site showcases my research projects, technical skills, and thoughts on applied ML in biologics discovery.

I believe in the power of collaboration. If you see an opportunity for us to work together or need support, please reach out.

Interests
  • Generative AI for Antibody/Protein Design
  • Protein Structure Prediction (AlphaFold, ESM)
  • Machine Learning for Therapeutic Discovery
  • Multi-omics Data Integration
  • Bioinformatics & NGS Analysis
Education

  • B.A. in Neuroscience | Minor in Data Analytics and Psychology, 2022

    Concordia College

Technical Skills

Python
Machine Learning
Protein Structure Prediction
Bioinformatics
Data Science
R

Experience

 
 
 
 
 
Genovac
Scientist I, Antibody Discovery
Genovac
July 2025 – Present Fargo, ND
Lead the design and deployment of custom tools and workflows for improving antibody affinity, stability, and manufacturability leveraging internal training data. Engineer de novo antibody sequences using large diffusion models preconditioned on internal structure-function datasets to drive rational therapeutic design. Conduct exploratory computational research to identify high-impact protein regions for targeted antibody engineering and validation.
 
 
 
 
 
Genovac
Associate Scientist II, Sequencing
Genovac
March 2024 – July 2025 Fargo, ND
Spearheaded implementation of NGS pipelines reducing analysis time by 40%. Antibody repertoire sequencing and analysis using next-gen sequencing technologies. Prepare and analyze antibody sequencing data to support discovery efforts. Collaborate with computational biology team to develop analysis pipelines.
 
 
 
 
 
Aldevron
QA Specialist 2
Aldevron
January 2023 – March 2024 Fargo, ND
Streamlined documentation review process improving turnaround time by 25%. Review quality systems, SOPs, and production documentation.
 
 
 
 
 
Harvard Medical School, Beth Israel Deaconess Medical Center
Research Consultant
Harvard Medical School, Beth Israel Deaconess Medical Center
August 2022 – March 2023 Boston, MA
Developed automated RNA-seq pipeline processing 500+ samples weekly. Analyzed and implemented an RNA sequencing analysis pipeline.
 
 
 
 
 
Harvard Medical School, Beth Israel Deaconess Medical Center
Research Student
Harvard Medical School, Beth Israel Deaconess Medical Center
May 2022 – July 2022 Boston, MA
Conducted NK cell experiments contributing to one publication. Conducted experiments on NK cells to support research goals.
 
 
 
 
 
Concordia College
IT Support Student Manager
Concordia College
August 2021 – December 2021 Moorhead, MN
Managed team of 5 technicians providing campus-wide tech support. Oversaw and guided IT support technicians in resolving technical issues.
 
 
 
 
 
Concordia College
Undergraduate Student Researcher
Concordia College
June 2021 – May 2022 Moorhead, MN
Presented zebrafish research findings at two regional conferences. Conducted research on Danio rerio (zebra fish) to support research goals.

Certificates

Coursera
Genomic Data Science Specialization by John’s Hopkins University
Coursera Nov 2023
See certificate
Coursera
Data Analytics Specialization by Google
Coursera Dec 2022
See certificate

Recent Posts

Building a Variant Effect Predictor for Antibody Affinity
Introduction Antibody affinity maturation—the process of improving the binding strength between an antibody and its target antigen—is a critical step in therapeutic antibody development. Natural affinity maturation occurs in vivo during B cell development, where somatic hypermutation generates antibody variants with improved binding.
Last updated on Mar 10, 2026 7 min read
Introduction to Graph Neural Networks for Protein-Protein Interactions
Introduction Proteins rarely act in isolation; instead, they participate in complex networks of physical and functional interactions that orchestrate cellular processes. Understanding these protein-protein interactions (PPIs) is fundamental to elucidating cellular pathways, identifying drug targets, and developing therapeutic interventions.
Last updated on Mar 10, 2026 6 min read
Machine Learning in Antibody Discovery: From High-Throughput Screening to AI-Driven Selection
Introduction The development of therapeutic antibodies has transformed modern medicine, with over 100 monoclonal antibodies now approved for clinical use and hundreds more in development. Traditional antibody discovery pipelines have relied heavily on experimental approaches—hybridoma screening, phage display, and more recently, single B-cell sorting followed by VDJ sequencing.
Last updated on Mar 10, 2026 6 min read
Protein Language Models: Bridging Sequence and Function in Computational Biology
Introduction The intersection of artificial intelligence and molecular biology has witnessed a transformative breakthrough with the emergence of protein language models (PLMs). These sophisticated deep learning architectures have fundamentally altered our approach to understanding protein sequence-structure-function relationships, offering unprecedented capabilities in protein property prediction, design, and engineering.
Last updated on Mar 10, 2026 6 min read
Unveiling the Statistical Genomics Landscape
Introduction Embarking on the Genomic Data Science Specialization at Johns Hopkins has been a profound journey, weaving together the tapestry of statistics and genomics. Over the course of my journey, I’ve immersed myself in the captivating topics within these two disciplines, reinforcing my decade-long engagement with statistical genomics and sparking a crucible for continuous learning.
Last updated on Nov 25, 2023 3 min read
Unveiling the Statistical Genomics Landscape
Navigating Genomics: A Dive into Command Line Tools
Introduction In my Coursera genomics class, I delved into the world of bioinformatics, mastering command line tools to explore genomic data. This blog post summarizes the key insights gained from tackling a range of genomics projects.
Last updated on Nov 25, 2023 2 min read
Navigating Genomics: A Dive into Command Line Tools

Projects

*
All In-progress Completed
Zebrafish enrichment analysis
Experiment summary of my undergraduate research internship at Concordia college.
PDF Code
Zebrafish enrichment analysis
Decoding Heart Disease, A Comprehensive Data Analysis
A project demonstrating and comparing the predictive abilities of different ML models in associating different measures to heart disease
PDF Code
Decoding Heart Disease, A Comprehensive Data Analysis
Lupus Mice Model
Experiment summary of my undergraduate research internship at Concordia college.
PDF Code
Lupus Mice Model

Contact

Feel free to contact me via email or LinkedIn.