mentorship

Interested in working with me? Apply to join our team as a Research Intern or Predoctoral Researcher!

As an intern or predoctoral researcher, you’ll work directly with me and other full-time researchers on a research project tailored to your interests. We aim for high-impact research publications at top-tier computer science venues, and we make all project artifacts open and publicly accessible, including code, models and data.

See FAQs for more info about working with me.

Research Internship

⭐ Apply Here ⭐!

I’ve had the pleasure of working with some amazing interns:

2024

• Chaitanya Malaviya, PhD student, University of Pennsylvania
• Alexander Wettig, PhD student, Princeton University
• Niklas Muennighoff, PhD student, Stanford University
  • OLMoE: Open Mixture-of-Experts Language Models (arXiv)
• Lucy Li, PhD student, University of California Berkeley
  • MathFish: Evaluating Language Model Math Reasoning via Grounding in Educational Curricula (EMNLP Findings 2024)
• Rose Wang, PhD student, Stanford University
  • MathFish: Evaluating Language Model Math Reasoning via Grounding in Educational Curricula (EMNLP Findings 2024)

2023

• Orion Weller, PhD student, Johns Hopkins University
  • When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets (EACL Findings 2024)
• Hyunji Lee, MS ▸ incoming PhD student, KAIST
  • RouterRetriever: Exploring the Benefits of Routing over Multiple Expert Embedding Models (arXiv)
  • Back to Basics: A Simple Recipe for Improving Out-of-Domain Retrieval in Dense Encoders (arXiv)
• Fangyuan Xu, PhD student, University of Texas at Austin
  • KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questions (ACL Findings 2024)
• Hang Jiang, PhD student, MIT

2022

• John Giorgi, PhD student, University of Toronto
  • Towards multi-document summarization in the open-domain (EMNLP Findings 2023)
• Kalpesh Krishna, PhD student, University of Massachusetts Amherst
  • LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization (EACL 2023) 🏆 Outsanding Paper Award
• Catherine Chen, PhD student, University of California Berkeley
  • Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents (ACL Findings 2023)
• Hancheng Cao, PhD student, Stanford University
  • The Rise of Open Science: Tracking the Evolution and Perceived Value of Data and Methods Link-Sharing Practices (arXiv)

2021

• Tal August, PhD student, University of Washington
  • Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing (ACM Transactions on Human-Computer Interaction)
• Dustin Wright, PhD student, University of Copenhagen
  • Generating Scientific Claims for Zero-Shot Scientific Fact Checking (ACL 2022)

2020

• Anne Lauscher, PhD student, University of Mannheim
  • MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting (NAACL 2022)
• Marissa Radensky, PhD student, University of Washington
  • Exploring the Role of Local and Global Explanations in Recommender Systems (CHI Extended Abstracts 2022)

2019

• Andrew Head, PhD student, University of California Berkeley
  • Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols (CHI 2021)
• Benjamin Charles Germain Lee, PhD student, University of Washington
  • LIMEADE: From AI Explanations to Advice Taking (ACM Transactions on Interactive Intelligent Systems)
• David Wadden, PhD student, University of Washington
  • Fact or Fiction: Verifying Scientific Claims (EMNLP 2020)

2018

• Amandalynne Paullada, PhD student, University of Washington

Predoctoral Research

My former predoc researchers:

• Benjamin Newman → PhD student at University of Washington (2023)
  • A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents (EMNLP 2023)
• Zejiang Shen → PhD student at MIT (2022)
  • VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups (Transactions of the ACL 2022)
• Sonia Murthy → PhD student at Harvard (2022)
  • ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts (EMNLP System Demo 2023)
• Haokun Liu → PhD student at University of North Carolina at Chapel Hill (2021)
• Isabel Cachola → PhD student at Johns Hopkins University (2020)
  • TLDR: Extreme Summarization of Scientific Documents (EMNLP Findings 2020)

FAQs

For interns:

• Who?
  • Eligibility?
    • ✅ Current students,
    • ✅ Incoming students to a PhD program,
    • ❌ Non-students,
    • ✅ Domestic and international applicants welcome.
  • Experience?
    • Most interns are PhD students, but we’ve also hired exceptional Master’s and undergraduate students,
    • I tend to hire students with aligned interests as me and a track record of research contributions, whether that’s writing papers (published or preprints) or open-source contributions. Your total publication count or university affiliation doesn’t matter; I look more for a good collaboration fit and quality of work.
• When? All research internships are paid, full-time positions for 3-6 months. Start times are flexible, though I typically recommend May or June for summer internships as there is usually a large, fun cohort at the office.
• Where? Internships are typically in-person at the AI2 office in Seattle, though I’ve also hired remote interns before.
• How? Apply at the link above and mention my name in your application. I recommend taking a look at my recent publications to get a sense of what type of projects I’m interested in.

For predocs:

• Who?
  • Eligibility? This is a great position for:
    • ✅ Any prospective PhD applicant, including current students near graduation and former students already working,
    • ✅ Anyone already been accepted to a PhD program and looking to defer enrollment for a year,
    • ❌ Already enrolled in a PhD program,
    • ✅ Domestic and international applicants welcome.
  • Experience?
    • Some research experience required. You don’t need many papers, nor do they have to be published (preprints are OK).
    • Strong coding skills preferred. Especially if you can point to contributions to open-source projects or demonstrations of your work. You don’t need industry software development experience.
• When? All predoctoral research positions are paid, full-time positions for 1-3 years. Start times are flexible. Contracts must be renewed each year.
• Where? All predoctoral research positions must be in-person at the AI2 office in Seattle at least part of the week.
• How? Apply at the link above and mention my name in your application. I recommend taking a look at my recent publications to get a sense of what type of projects I’m interested in.