Gautam Machiraju

Bio

I am a Member of the Technical Staff at Radical Numerics, an SF- & Tokyo-based startup building frontier biological sequence models. My research efforts focus on (a) building modeling primitives for long-context attribution and interpretability, (b) test-time model steering, and (c) methods applications for AI-driven knowledge acquisition — ultimately aiding in our understand of biological systems and the natural world.

I completed my PhD in Biomedical Informatics at the Stanford's department of Biomedical Data Science and Stanford AI Lab (SAIL), where I was extremely fortunate to be co-advised and mentored by Parag Mallick (Radiology) and Christopher Ré (CS). My undergraduate training was in Applied Mathematics and Bioengineering at University of California, Berkeley. My CV can be found here.

My name is most similarly pronounced as Batman's city of residence, "Gotham" (i.e. GAW-thum). There are many intonation variants for my Sanskrit-originating name across India, but this pronunciation is the closest to the North Indian variant. I also go by "Machi" if that's easier.

Research Artifacts

Before working on biological sequence models, I've had the pleasure of building models for multiple token types and modalities in the biomedical domain:

pixels in images — pathology slides, edge devices, video frames, satellite imagery
cells in tumors — spatial proteomics
text spans in natural language — scientific literature, EHRs
monomers in biological sequences — DNA, proteins

My most recent publications are documented on Google Scholar, with a representative sample here:

Selected

Smarter by Design: How SmarterDx Engineered Clinical AI for the Complexity of Healthcare

Company whitepaper (2025)

Paper

Prospector Heads: Generalized Feature Attribution for Large Models & Data

Gautam Machiraju, Alexander Derry, Arjun Desai, Neel Guha, Amir-Hossein Karimi, James Zou, Russ Altman, Christopher Ré, Parag Mallick

International Conference on Machine Learning (ICML), 2024

Paper

Development and Evaluation of an Image-based Deep Learning Algorithm to Classify Skin Lesions from Mpox Virus Infection

Alexander Henry Thieme, Yuanning Zheng, Gautam Machiraju, et al.

Nature Medicine (2023)

A Dataset Generation Framework for Evaluating Megapixel Image Classifiers & their Explanations

Gautam Machiraju, Sylvia Plevritis, Parag Mallick

European Conference on Computer Vision (ECCV), 2022

Developing Machine Learning Models to Personalize Care Levels among Emergency Room Patients for Hospital Admission

Minh Nguyen, Conor Corbin, Tiffany Eulalio, Nicolai Ostberg, Gautam Machiraju, Ben Marafino, Michael Baiocchi, Christian Rose, Jonathan Chen

Journal of the American Medical Informatics Association (2021)

Multicompartment Modeling of Protein Shedding Kinetics During Vascularized Tumor Growth

Gautam Machiraju, Parag Mallick, Hermann Frieboes

Nature Scientific Reports (2020)

Etcetera

In my creative down-time, I enjoy exploring graphic/visual design, architectural photography, and sound design.

Selected

Detecting salient objects
toy examples to show class differential features, presented in behind our ICML IMLH 2023 paper

Systems for Foundation Models
describing advances on chip design + distributed training

New architecture to learn salient objects
depicting graphical data structures behind our ICML IMLH 2023 paper

Nash Equilibria for joint optimizers
multiple choice grid depicted for each training step

Mpox mobile surveillance
graphical abstract behind our Nature Medicine (2023) paper

Increasing context lengths
comparison of lab's architectures

Detecting salient objects
toy examples to show class differential features, presented in behind our ICML IMLH 2023 paper

Systems for Foundation Models
describing advances on chip design + distributed training

New architecture to learn salient objects
depicting graphical data structures behind our ICML IMLH 2023 paper

Nash Equilibria for joint optimizers
multiple choice grid depicted for each training step

Mpox mobile surveillance
graphical abstract behind our Nature Medicine (2023) paper

Increasing context lengths
comparison of lab's architectures

Evaluating model explanations
graphical abstract behind our ECCV 2022 paper

Vision for instructive AI
talking points for where models could help humans

Global health monitoring
graphical abstract behind remote sensing for health surveillance

Hallmarks of cancer
background information on cancer progression

Visual grounding
desiderata to create more expressive Foundation Models

Physics-based model of cancer
graphical abstract for our Nature Sci Reports (2021) paper

Acknowledgement

This website uses the website design and template by Martin Saveski. Some stylisitc alterations were made with inspiration from Tatsunori Hashimoto.