Emma Tosch
Northeastern University, MA·Computer Science
Experiments are how people and organizations learn what causes what, from testing a new app feature to judging whether a news-feed algorithm shapes how users understand current events. Much of this experimenting now happens inside software, yet software is rarely built to support it.
- Fields
Computer Science
Mathematics & Statistics
- Topics
formal methods
domain-specific language
programming languages
- Grants
- 1 known grant, last ended Oct 2026
What they work on
- Design a domain-specific language for software experiments that enforces experimental-design rules on hypotheses, analyses and treatment assignment, so that experiments are correct by construction.
- Ensure that hypotheses and treatment assignments written in the language are consistent with the effects an experiment can actually identify, using formal methods reasoning.
- Integrate the experimentation language into existing educational software, using its gradual typing support to build a separate type system that tracks experimental interventions.
- Build a public, searchable repository of experiments to evaluate the tools and support further formal-methods research on the experiment-to-analysis pipeline.
More about the research
Developers usually write one-off experiment code that fits a single task and is disconnected from the hypotheses it is meant to test and the analyses that follow. This makes cause-and-effect conclusions harder to trust and harder to inspect.
Why it matters
Organizations run experiments in software to decide on products, policies and designs that affect many users, but hand-built experiment code can produce mismatched hypotheses and analyses. Tools that check experimental design automatically could help developers and researchers draw more reliable cause-and-effect conclusions, including in educational software. A public experiment repository could also make such studies easier to inspect and compare.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Minxuan Zhou
Illinois Institute of Technology, IL·Computer Science
Design a domain-specific programming language that makes it easier to write general fully homomorphic encryption (FHE) applications across different algorithmic schemes.
Milijana Surbatovich
University of Maryland, MD·Computer Science·Early-career
Develop the first formal model of an intermittence-safe operating system, with defined abstraction layers that separate tiers of trust and allow each component to be verified on its own.
Andrew Hirsch
University at Buffalo, NY·Computer Science
Develop open choreographies, choreographic programs whose co-designed components can communicate safely with independently written, off-the-shelf software.
The school
Northeastern University: 27 labs on PI Pages, $347M a year on research, 251 research doctorates a year.
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