Gautam Machiraju

Bio

I am a final year PhD Candidate in Biomedical Informatics at Stanford University's department of Biomedical Data Science. My work centers around building data copilots — AI systems that augment our capabilities in (1) making rational and evidence-based decisions, (2) discovering and acquiring knowledge, and (3) generating new data given our design preferences. I am especially motivated to develop data copilots to understand the natural world, particularly for biological and physical systems. I am extremely fortunate to be co-advised and mentored by Parag Mallick (Radiology) and Christopher Ré (CS).

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.

I am on the industrial job market for ML Research Scientist roles, particularly for Interpretability/Safety, AI4Science, AI4Bio, or AI4Med teams. Please feel free to contact me about opportunities!

Research

My research focuses on building data copilots, or AI systems that have the following interrelated capabilities:

1. Evidence-based & rational decision-making — pinpointing what is important (evidence) within inputs for a model's outputs, as well as why and how those outputs were created (rationale). Our work has built automated evidence-verification systems with synthetic data [ECCV'22] and new equippable, GPT-inspired architectural blocks that reveal AI evidence and rationale in text documents, pathology images, and protein sequences & structures [IMLH'23,ICML'24].

2. Knowledge discovery & acquisition — identifying new information and ensuring it is human-comprehensible. We used model rationale from capability (1) in scientific deployments to discover tumor regions associated with aggressive cancers via spatial proteomics data and single-cell encoders [upcoming preprint]. We are also making deployments in global health and justice, adapting vision-language models to mine covariates from satellite imagery to combat human labor trafficking in the Amazon Rainforest. Finally, we are using protein language models to learn the "functional language" of synthetic and natural proteoforms for viral capsids, metal binders, and circulating biomarkers of chronic diseases.

3. Controlled generation — using weak labels and machine-extracted knowledge (e.g. memory banks of salient motifs) to guide LLMs toward aligned and safe data generation. Primary applications are rooted in natural language, chess gameplay, and protein sequence design.

I work with foundation models (FMs) spanning multimodal embedding models to autoregressive models (LLMs, agents). We have found success in fostering capabilities (1)-(3) by building compound AI systems that adapt FMs for novel forms of inference & reasoning. Crucially, these systems must be data- and time-efficient to reason over relatively limited sets of high-dimensional unstructured data often found in biomedicine. Finally, our methods aim to increase AI interpretability to determine what our AI systems have learned and how they function.

Projects & Artifacts

Stay tuned for future publications and writings on:

• A multimodal FM that is trained on public data and passes medical board exams
• A review on spatial statistics for spatial biology
• Data copilots for spatial biology: in silico discovery of biomarkers of aggressive cancers
• A call to action for evidence-based and rational AI systems in biomedicine
• A new form of controlled generation that incorporates weak supervision, motivated by chess and protein design
• Adapting remote sensing FMs that teach us the drivers of human labor trafficking in the Amazon Rainforest
• A lecture introducing modern ML to life scientists: the history, developments, and opportunities

Most recent publications on Google Scholar.

• Selected

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

Fun: On weekends, you can find me and my partner grabbing some sun in urban-suburban East Bay. Or we're unplugging at 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 grab a coffee!

Design: 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 will carve out time to reflect on, mock up, and refine any discussed concepts.

• Selected

Acknowledgement

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).

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