About
In my own words
Hi — I'm Venika Kakarla, a student researcher working at the intersection of artificial intelligence and cancer biology. My work lives mostly in hepatocellular carcinoma (HCC, or liver cancer) — I'm interested in the gap between the molecular data we already have and the treatment decisions that data could be informing, if it were modeled the right way.
I currently do computational research with the Dhanasekaran Lab at Stanford School of Medicine, where I work on liver cancer datasets alongside researchers and clinicians. Independently, I've built generative and predictive models — a graph neural network for protein-DNA binding, and a dual-inhibition drug discovery model for HCC recurrence pathways — that have placed at science fairs and been recognized nationally.
Outside the lab, I run a tutoring company I founded, help lead operations for a 1,700+ member AI community in Austin, and co-lead two science clubs at my high school. I think the best research explains itself clearly enough that a stranger could follow the logic — that's what I try to do here.
My path
Science started for me as a habit of asking one more question. Biology and chemistry gave that habit a direction: cells, proteins, and the small molecular changes that decide whether a body stays healthy.
Medicine draws me because its questions have people on the other end of them. A better model matters only if it helps a patient get an answer sooner, gives a clinician a reason to trust it, or makes a treatment decision less of a guess.
Code gave me a way to test ideas faster than I could by hand. I learned Python, PyTorch and TensorFlow by building things, and I found that machine learning is at its best when it is pointed at a real biological question.
I start college in Summer 2027. I hope to become a physician-scientist: someone who treats patients and also does the research, and builds the technology, that improves how they are cared for.
2023 — 2027 (Expected)
Round Rock, TX · Unweighted GPA 4.0 / 4.0 · Weighted 5.85 / 6.0
Coursework and extracurricular leadership centered on biology, chemistry, and computational research.
Nov 2025 — Jan 2026
Remote mentorship via a University of Texas at Austin researcher
Received structured external mentorship extending dual-inhibition pathway research toward cancer stem cell behavior in HCC.
Jun 2025 — Present
Stanford, CA (remote/hybrid research collaboration)
Ongoing computational research placement applying machine learning to spatial and molecular liver cancer datasets, with direct exposure to wet-lab procedure (IHC, cell culture).