ML/AI · DESIGN · PRINCETON UNIVERSITY

Building
virtual minds

I research AI systems and design the interfaces that make them human — finding the seam where intelligence meets experience.

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Projects

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Machine Learning

Bollywood Movie Recommender

A machine learning algorithm built with Python using pandas, numpy, and PyTorch to recommend Bollywood movies based on user preferences. Features an interactive web interface for seamless user experience.

PythonPyTorchPandasNumPyReact
2025

Mobile Development

NutriLens

An innovative iOS app that transforms your fridge into a smart nutrition guide. Users can snap photos of their fridge contents and receive personalized, sustainable recipe recommendations powered by AI.

SwiftXCodeiOSGemini APIComputer Vision
2024

AI/Healthcare

DentalConnect

An AI-powered diagnostic platform for dental care that makes preventative care and accurate diagnostics more accessible. Patients can upload photos to receive instant visual reports highlighting potential issues like cavities, gingivitis, or discoloration. Built with computer vision models trained on 10,000+ dental images.

PythonReactFastAPIAWSSageMaker
2026

Web Development

ProteinArchitect

A web platform for protein architecture analysis and visualization. Explore protein structures, analyze molecular interactions, and visualize complex biological data through an intuitive interface.

ReactTypeScriptPythonWebGLBioinformatics
2025

Web Development

SimplyResearch

A Ruby on Rails web application where users can upload PDF files of research papers and generate custom slides, summaries, and infographics in seconds. Features Google OAuth authentication and automated content generation.

Ruby on RailsJavaScriptHTMLSCSSDocker
2024
// ranveer.circt
func buildVirtualMind() void {
  let ptr = allocate(sizeof(NeuralNet));
  train(ptr, dataset);
  // TODO: free(ptr) ← you forgot this
  // ⚠ memory leak at 0x000000
}

Ranveer. Compiled.

I am an ML/AI researcher and designer at Princeton who builds at the seam between model intelligence and human experience.

Across research labs, production tools, and student communities, I focus on making ambitious systems legible, useful, and quietly elegant.

When I'm not coding, you'll find me cooking with my family, strategizing over board games, or making tech related instagram reels @rsingh.daily.

parsing ranveer.circt...

Based inPrinceton, NJ
StudyingPrinceton University
CurrentlyML/AI systems + interface design
Open toResearch, product, and design engineering roles

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Sola researchers

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Research team

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HackUTD attendees

Experience

  • Deployed 3 critical CI/CD tests within the TigerData storage service, elevating codebase test coverage by 8%.
  • Resolved 4 high-priority bugs within TigerData ensuring functionality and data integrity for 25+ researchers.
  • Collaborated with DevOps to design and implement PostgreSQL schemas, increasing dataset capacity by 15%.
  • Provided direct technical support to 50+ researchers on GPU/CPU utilization, system access, and debugging.
  • Diagnosed critical bugs in Python code and scripts, improving program efficiency for 90% of users.
  • Clarified complex technical concepts to improve user self-sufficiency, reducing repeat inquiries by 20%.
  • Generated 3 Python projects to teach OOP fundamentals to 3,000 students.
  • Collaborated with tutors to standardize GitHub and Markdown best practices, reducing dev overhead by ~150 hours/year.
  • Enhanced online quiz platform with React-based animations to improve engagement and learning.

Blog

March 2026

Why design taste is the rarest skill in ML

A field note on taste, judgment, and why better interfaces often unlock better research outcomes.

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February 2026

What Magic: the Gathering taught me about system design

How card interactions shaped my thinking on modularity, balancing, and compositional complexity.

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January 2026

Interpretability as a design problem

Treating interpretability outputs like products can make safety tooling dramatically more useful.

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December 2025

Building Sola: an honest postmortem

What worked, what didn't, and what I would redesign now that the system has seen real users.

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Let's build something together

If you are exploring ML systems, productized research, or interaction design for intelligent tools, I would love to collaborate.

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