Nithish Narasimman

Education

University of California San Diego B.S., Bioengineering: Bioinformatics — June 2023

University of California Los Angeles PhD, Bioinformatics: Systems Biology — Expected June 2030

Data Scientist at Octant — May 2024 - August 2025

Built computer vision segmentation pipeline to automatically quantify retinal layer thickness from thousands of IHC-stained mouse histological sections
Applied linear mixed effects modeling in R to account for experimental structure and detect treatment effects in adRP disease models
Developed population genetics simulations using literature data to model disease progression and treatment response and inform clinical trial design
Conducted deep mutational scanning analyses on GPCRs, mapping genotype-phenotype relationships to understand protein variant effects

Software Engineering Apprentice at Octant — June 2023 - May 2024

Developed React application with a Django backend for chemists to design experiments on Octant’s high throughput synthesis platform, speeding up design process from hours to minutes
Built and benchmarked chemistry machine learning models and prediction pipelines to estimate compound potency, reducing chemical search space and optimizing hit selection

Computational Biology Intern at Zentalis — June 2022 - August 2022

Developed computational pipelines to analyze 30GB+ of cancer genomics data, identifying mutational signatures that stratify patient populations by treatment response patterns

Undergraduate Researcher at The Alexandrov Lab — January 2021 - June 2023

Implemented HALS optimization in SigProfilerExtractor, reducing mutational signature extraction runtime from tens of hours to under one hour
Benchmarked SigProfilerAssignment against competing mutational signature tools, demonstrating superior performance and co-authoring published comparative analysis

Marcos Díaz-Gay, Raviteja Vangara, Mark Barnes, Xi Wang, S M Ashiqul Islam, Ian Vermes, Stephen Duke, Nithish Bharadhwaj Narasimman, Ting Yang, Zichen Jiang, Sarah Moody, Sergey Senkin, Paul Brennan, Michael R Stratton, Ludmil B Alexandrov, Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment, Bioinformatics, Volume 39, Issue 12, December 2023, btad756, doi:10.1093/bioinformatics/btad756

imshare — March 2026 - April 2026

Built a Rust-based JWT authentication service for generating signed, expiring share links for self-hosted Immich photo libraries
Implemented a three-tier verification architecture (Cloudflare Tunnel → imshare-verify middleware → Immich Public Proxy) with HMAC-signed tokens, revocation tracking, short-link generation, and an HTMX dashboard

fit-rs — June 2025 - August 2025

Built a high-performance Rust tool for Bayesian dose-response curve fitting, estimating EC50 values via a 4-parameter logistic model with MCMC sampling
Implemented dual MCMC backends (custom Metropolis-Hastings and Stan/BridgeStan), multi-chain convergence diagnostics (R-hat), and three interfaces: interactive TUI with real-time plotting, HTMX web UI, and a JSON API server

deepscan — December 2024 - January 2025

Built a high-performance web application for visualizing and analyzing deep mutational scanning (DMS) data, enabling researchers to explore protein variant effects across entire sequences
Architected backend in Rust using Axum and SQLx with PostgreSQL, and frontend with HTMX and D3.js for interactive heatmaps, scatter plots, and 3D protein structure visualization via PDBe Molstar

MotifFinder — September 2022 - June 2023

Developed Rust based utility that identifies latent motifs in a given genome
Utilized tool to discover de novo motifs in P. tricornutum microalgae as candidates for testing inducible promoter functionality in synthetic biology applications