Computational Biology · AI Systems

Dhruv Khatri

Building computational systems for biological discovery.

Computational biology and machine learning applied to scientific discovery.

Current focus

Applied machine learning
Computational biology
Scalable research systems
San Diego, CA
01Profile

Applying computation to biomedical and biological research.

I develop computational and machine-learning systems for biomedical research, combining expertise in computer science, data engineering, and molecular biology.

My work focuses on scalable biological data analysis, AI-driven discovery, genomics, and predictive modeling.

Domain

Biology + Healthcare

Methods

ML + Statistical Modeling

Output

Models + Data Systems

02Experience + Capabilities

2025 — Present

Scientist

Eli Lilly and Company

Working on Artificial Intelligence, Machine Learning, and Bioinformatics for Large Molecule Discovery.

2024 — Present

Research Assistant

Bafna Lab · UC San Diego

Studying ecDNA and gene knockout results across cancer cell lines. Building co-amplification networks and statistical analysis tools.

Summer 2024

Research & Development Intern

Eli Lilly and Company

Applied AI and computational methods to protein design and engineering for early large-molecule discovery.

2023 — 2024

Research Assistant

Rana Lab · UC San Diego School of Medicine

Developed single-cell RNA-seq pipelines, analyzed TCGA and GTEx datasets, and optimized bioinformatics workflows for HPC environments.

Technical domains

Machine Learning

PyTorch · TensorFlow · Scikit-learn

Computational Biology

Genomics · Single-cell · Protein design

Data Systems

Python · R · SQL · HPC · Cloud

Software Engineering

APIs · Pipelines · Visualization

03Selected Systems
01 / AI SYSTEM

Health xAI

A wellness intelligence system that turns Apple Health signals into personalized daily guidance using a generative AI pipeline.

Input
Apple Health biometrics
System
Contextual insight pipeline
Stack
Swift · Flask · Python · Claude
02 / COMPUTER VISION

Melanoma Detection

A deep-learning image classifier designed to identify visual patterns associated with malignant skin lesions.

Input
Dermoscopic imagery
Model
Convolutional neural network
Stack
TensorFlow · Python
03 / PREDICTIVE MODELING

Connect Four Prediction

A spatial machine-learning model that predicts game outcomes from incomplete early-game board states.

Input
Eight-turn board state
Task
Outcome classification
Stack
Python · Jupyter · ML
04 / CLASSIFICATION

Breast Cancer Detection

A diagnostic classification model that distinguishes malignant and benign tumors from cell-nuclei features.

Input
Tumor cell features
Task
Binary classification
Stack
Scikit-learn · Python
04Contact

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