Collage of a cell, robotic micromanipulation and DNA read by machines Collage of a chromosome, a cell and molecular structures

Government of Ireland Postgraduate Scholar · RCSI

Decoding the non‑coding.

Making the 98% of the genome we usually ignore finally legible.

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Collage of a chromosome, microscope, robotic arm and lab glassware Chromatin beads-on-a-string structure trailing in

The premise

Reading the genome's dark matter.

Most of the genome doesn't code for proteins. I'm convinced that's exactly where the most interesting answers in cancer are hiding.

So I build the tools to read it: deep learning, epigenomics and large-scale multi-omics, turned on the 98% we usually skip.

Explore the research
Faith Ogundimu

About me

Building the bridge between code and cure.

I'm a PhD researcher at the Royal College of Surgeons in Ireland, investigating non-coding driver mutations in breast cancer using large-scale genomic datasets including TCGA, ICGC and ENCODE.

My work applies deep learning and integrative computational approaches, from CNNs and multilayer perceptrons to Hidden Markov Models, to find functional variants in regulatory elements and uncover new therapeutic vulnerabilities.

I completed my B.Sc. in Genetics and Cell Biology at Dublin City University with First Class Honours, ranking 2nd in my cohort (Salutatorian) and earning a place on the Dean's Honours List.

2nd Salutatorian
€150k+ Research funding
won
35+ AI agents
built
Hackathon
winner

Research focus

My research themes.

Non-Coding Driver Mutations

Investigating the 98% non-coding genome to find regulatory mutations that drive breast cancer progression, using >5,000 whole genomes from TCGA and ICGC.

Deep Learning for Genomics

Developing CNNs, GNNs, MLPs and Hidden Markov Models to predict chromatin accessibility and prioritise candidate driver mutations from multi-omics data.

3D Regulatory Mapping

Building breast tissue-specific regulatory maps by integrating single-cell ATAC-seq, spatial transcriptomics and ENCODE regulatory elements.

Surgical Robotics & Embedded AI

Exploring Arduino and ESP32 prototyping as a practical bridge from machine learning research into surgical robotics and autonomy workflows.

Currently exploring
Collage of a chromosome, regulatory tracks and a circuit board

Latest

Recent dispatches.

Jul 2026 First place, Built with Claude: Life Sciences · NCypher Researcher track winner · Anthropic × Cerebral Valley, with Gladstone Institutes · 1 of 299 projects · an honest MCP triage tool for non-coding cancer variants: a regulatory-activity score, the mechanism a variant breaks, and a confidence flag
Jul 2026 First place, Google Cloud Rapid Agent Hackathon · Unravel Fivetran track winner · sponsored by Google (Devpost) · a five-agent system on Gemini 3.1 and the Agent Development Kit that recomputes a calibrated ACMG probability when a variant is reclassified, then drafts patient recontact, clinician in the loop, nothing auto-sends
Jul 2026 Onkydra opens its waitlist The in-silico stratification workspace for rare-cancer drug development · drop in a target, it simulates the cohort, predicts the responder subgroup with confidence intervals, ranks resistance pathways and cites every claim · beachhead H3 K27M diffuse midline glioma · a Gemini XPRIZE 2026 entry · research use only
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Let's talk

Let's decode something.

Collaborations, questions, or just curious about the non-coding genome? My inbox is open.

Research groupGenomic Oncology Research Group
SupervisorProf. Simon J. Furney
InstitutionRCSI University of Medicine and Health Sciences, Dublin