Gautam Machiraju

AI-driven Data Copilots for Scientific Discovery
PhD Student, Biomedical Informatics @ Stanford

gmachi [AT] {stanford, cs.stanford} [DOT] edu
he/him

Bio

I am a final year PhD Candidate in Biomedical Informatics at Stanford University's department of Biomedical Data Science. My work centers around developing AI that can expand our understanding of scientific and biomedical data — perhaps to better inform our decision-making (e.g. in the clinic, or for drug development) or perhaps to (re)discover phenomena in high-dimensional unstructured data (e.g. large images, time-series, graphs, or text corpora). We refer to such AI as data copilots. I am extremely fortunate to be advised and mentored by Parag Mallick (Radiology) and Christopher Ré (Computer Science) and spend much of my time with the Hazy Research group at the Stanford AI Lab (SAIL). My graduate training and research have been graciously funded via the NIH (BD2K, NLM), the Stanford Data Science Institute (Data Science Scholarship), the International Alliance for Cancer Early Detection (Canary-ACED Graduate Fellowship), and Stanford's Institute for Human-Centered Artificial Intelligence (HAI).

I am on the industrial job market for Research Scientist roles, particularly for AI4Science, AI4Healthcare, or Responsible AI teams. Please feel free to contact me about opportunities!

Life bits: On weekends, you can find me and my partner road-tripping to one of California's numerous regional, state, or national parks to spend time on the water and trails. Outside of hiking and camping, my hobbies include painting, gardening, and hosting regular themed dinner and cocktail parties. I also love city trekking with friends and popping into cafés and roasteries, museums, used bookstores (hunting for vintage maths sections), and outdoor pubs with live music sessions. Reach out if you'd like to chat!

As a side note: 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 and is the one I go by. I also go by "Machi" in some circles.

Thesis Research

My research interests broadly span AI-human collaboration and AI-driven concept discovery to expand our understanding of science and biomedicine. Methodologically, I currently work at the intersection of architectures for long-range modeling, foundation models (FMs), geometric ML, and interpretability & explainability. My thesis work particularly focuses on bettering our understanding of spatial systems and the key actors that distinguish such systems from one another. To this end, we seek to equip models with feature attribution capabilities to extract class-specific regions captured in high-dimensional imagery. My thesis aims include:

Evaluating feature attribution
For deployment in human-centered applications, evaluation of classifiers can't be anchored on accurate predictions alone. Are some architectures or explanation methods inherently better at detecting class-specific regions? We design an evaluation framework for improved architecture and attribution selection. Check out our work at ECCV'22.
Building performant feature attribution methods
We reason that richer data representations will enable this capability. For high-dimensional, unstructured data (e.g. images, graphs, text), this involves leveraging the expressivity of FM embeddings and learning multi-scale spatial dependencies. We developed interpretable architectures called "prospector heads" to mine for class-specific regions using FM-discovered semantic concepts (examples below for histopathology). Check out our work at ICML'23 and arXiv'24.
Pathology
Deploying feature attribution to spatial biology for biomarker discovery

We seek to improve cancer prognostics by discovering novel histo-molecular biomarkers of cancer progression in the tumor microenvironment. To do this, we are building data copilots for spatial biology, including public transcriptomics datasets and proprietary proteomics datasets provided GE Global Research.

Preprints & Publications

While my thesis research focuses on interpretable and explainable AI in the context of high-dimensional biomedical data, I've been privileged to work in several methods and application domains. A copy of my CV can be found at the bottom of this page. Stay tuned for future work on:

  1. Multimodal FMs that pass medical board exams
  2. Zero-shot segmentation with long text captions
  3. Data copilots for spatial biology: in silico discovery of biomarkers of cancer progression
  4. Fact-checking multimodal FMs to improve image tagging & retrieval
  5. Satellite imagery FMs fine-tuned to predict measures of maternal health & child mortality
  6. Satellite imagery FMs fine-tuned for few-shot detection of human labor trafficking encampments in the Amazon Rainforest


Most recent publications on Google Scholar.
indicates equal contribution.
§ indicates authorship associated with consortium.

  • Selected
  • All

Grammar Matters: Exploring Grammatical Variation’s Role in Improving Fine-Tuned LLMs for Biomedical Relation Extraction

Varun Tandon, Gautam Machiraju, Parag Mallick

In review.

Spatial Statistics for Spatial Biology

Hunter Boyce, Gautam Machiraju, Parag Mallick

In review.

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

In review

Prospectors: Leveraging Short Contexts to Mine Salient Objects in High-dimensional Imagery

Gautam Machiraju, Arjun Desai, James Zou, Christopher Ré, Parag Mallick

International Conference on Machine Learning (ICML) 3rd workshop on Interpretable Machine Learning for Healthcare (IMLH) 2023.

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).

Grammar Matters: Exploring Grammatical Variation’s Role in Improving Fine-Tuned LLMs for Biomedical Relation Extraction

Varun Tandon, Gautam Machiraju, Parag Mallick

In review.

Spatial Statistics for Spatial Biology

Hunter Boyce, Gautam Machiraju, Parag Mallick

In review.

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

In review

Prospectors: Leveraging Short Contexts to Mine Salient Objects in High-dimensional Imagery

Gautam Machiraju, Arjun Desai, James Zou, Christopher Ré, Parag Mallick

International Conference on Machine Learning (ICML) 3rd workshop on Interpretable Machine Learning for Healthcare (IMLH) 2023.

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.

A Community-based Approach to Image Analysis of Cells, Tissues and Tumors

CSBC/PS-ON Image Analysis Working Group§, Juan Carlos Vizcarra, Erik A. Burlingame, Yury Goltsev, Brian S. White, Darren Tyson, Artem Sokolov

Computerized Medical Imaging and Graphics (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).

Small Molecule Property Prediction via Proxy Labeling and Multi-scale Learning

Gautam Machiraju, Parag Mallick

Preprint (2021).

Multicompartment Modeling of Protein Shedding Kinetics During Vascularized Tumor Growth

Gautam Machiraju, Parag Mallick, Hermann Frieboes

Nature Scientific Reports (2020).

Multi-omics Factorization Illustrates the Added Value of Deep Learning Approaches

Gautam Machiraju, David Amar, Euan Ashley

Preprint (2019).

Vitæ

More details (projects, collaborators, talks, academic service, relevant coursework) can be found on my CV and LinkedIn page.

  • Stanford University 2018 - present
    PhD Student
    Biomedical Informatics
    Data copilots for scientific discovery
  • IBM Research Summer-Fall 2023
    Student Researcher
    Fact-checking Foundation Models
  • Canary Center at Stanford Sep 2016 - Aug 2018
    Bioinformatics Research Assistant
    Mathematical modeling of tumors,
    NLP of biomedical literature,
    & anomaly detection in multi-omic time-series
  • Strateos Summer 2016
    Bioengineering SDE Intern
    Software engineering for robotics:
    queueing, optimization, search
  • UCB Center for Comp Bio Sep 2015 - June 2016
    Undergraduate Researcher
    Genomic sequence alignment algorithms
  • Strand Life Sciences Summer 2015
    Bioinformatics Intern
    Genomic rare variant identification
  • Lawrence Berkeley National Lab July 2013 – May 2015
    Undergraduate Researcher
    Computational Biophysics &
    structural biology of enzymes
  • Berkeley BioLabs Summer 2014
    Bioengineering Intern
    Gene transfer experimentation
  • University of California, Berkeley 2012 - 2016
    BA Student
    Applied Mathematics
    (emphasis in Mathematical Biology)
    Minor in Bioengineering

Process

One of my favorite aspects of research is thinking about aesthetic and design when communicating technical ideas. This drive to understand ideas by visually communicating them (often to myself) sparked as a dyslexic Maths undergraduate. Despite my numerous interests in Maths, I struggled to parse and conceptualize blocks of textual abstraction in modern mathematical presentation, typical of standard teaching materials. I thus relied heavily on intuition and visual proofs as mental anchors. Thanks in part to training as a CIR Scholar at Stanford's Hasso Plattner Institute of Design, I cartoon-ify almost everything I work on and often spend Friday afternoons reflecting on, mocking up, and refining any discussed concepts.

  • Selected
  • All
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.