Humishka Zope

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I’m a graduating MS/BS student in Computer Science at Stanford University, where I do research in the Stanford NLP Group. My prior work has focused on large language model alignment and evaluation, with a broader interest in how AI systems reason, collaborate, and interact with people.

This summer, I was at Microsoft Research, where I worked on improving LLM reasoning capabilities through post-training and test-time scaling with domain-specific knowledge. I also co-led Future of Work with AI Agents at Stanford, research which developed a novel framework for evaluating human–AI collaboration potential and quantifying misalignment between human needs and AI capabilities across diverse domains. My work has been featured in Forbes, Stanford HAI, and Yahoo Finance.

I’ve also spent time in industry at Joby Aviation and Rippling, where I built transformer-based models leading to the company’s first AI product launch featured in Bloomberg.

I am actively seeking opportunities in research and engineering - reach me at zope at stanford dot edu!

Updates

  • (Jan, 2026) Our work on synthetic data generation for LLM Safety was presented at Amazon Science Trustworthy AI Symposium in NYC!
  • (Nov, 2025) The WORKBank database has 340+ monthly downloads! We instantiate a novel framework for measuring human-AI collaboration potential and misalignment. Check it out here.
  • (Sept, 2025) Had a great summer at Microsoft Research as a Research Intern, working on post-training LLMs for optimization modeling! See our paper here.
  • (June, 2025) Co-presented at Stanford NLP Group on Future of Work with AI Agents.
  • (March, 2024) Honored to co-host the annual Faculty Dinner for Stanford Women in Computer Science!

Projects

*:Equal Contribution

Optimization

OptiMind: Teaching LLMs to Think Like Optimization Experts

Zeyi Chen*, Xinzhi Zhang*, Humishka Zope*, Hugo Barbalho, Konstantina Mellou, Marco Molinaro, Janardhan Kulkarni, Ishai Menache, Sirui Li*

Preprint (in submission) · September 2025

A self-improving LLM framework that enhances optimization reasoning through post-training and test-time scaling with domain expertise, achieving SoTA performance in optimization modeling.

WORKBank

Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce

Yijia Shao*, Humishka Zope*, Yucheng Jiang, Jiaxin Pei, David Nguyen, Erik Brynjolfsson, Diyi Yang

Preprint (in submission) · Website · HuggingFace (~300+ monthly downloads) · June 2025

Developed a framework to measure human–AI collaboration potential and quantify misalignment between human needs and AI capabilities across occupational tasks in diverse domains. Built the WORKBank database to map desire–capability misalignment, define the Human Agency Scale, and identify trends in shifting demand for core human skills.

Featured in Forbes, Stanford HAI Report, Yahoo Finance.

Talent Signal

Talent Signal: AI-Based Performance Management System

Developed at Rippling as a Machine Learning Engineering Intern.

Launched publicly as a product in 2024 · Blog Post · Bloomberg coverage

An AI-based performance management system leveraging transformer-based classification models to assess employee performance.