JUSTIN P. DOLLMAN

Professional Experience

Co-Director | Cambridge AI Safety Hub (CAISH)

• Co-directing operations and strategic initiatives for an AI safety field-building organization
• Director of MARS (Mentorship for Alignment Researchers): executed MARS 4.0 and organized MARS V
• Designed curricula for Alignment Fellowship (technical and governance cohorts)
• Co-organizes "How Do We Solve Alignment?" — weekly reading group of visiting researchers and Cambridge locals
• Founded the Alignment Desk

Lead Organizer | AI Safety Evals Group (BlueDot Impact)

• As the sole paid organizer, directed a weekly forum for discussing cutting-edge research in AI Evaluations
• Secured guest speakers from leading AI safety organizations, including METR and MATS
• Revived a struggling group: attendance grew to its highest-ever levels under my leadership

Course Facilitator | ENAIS & AI Safety Hungary

• Course: Deep Dive: AI Policy 201
• Led weekly discussion groups on intermediate AI governance topics

Course Facilitator | Center for AI Safety

• Course: Introduction to AI Safety, Ethics, and Society (see: here)
• Lead weekly meetings for two cohorts and advised on participants' projects

NSF Researcher | Stony Brook University (Dept. of Technology and Society)

• Conducted bibliometric analysis and regression modeling on large datasets of NSF grant outcomes
• Cleaned, merged, and analyzed data using R to identify factors associated with successful broader societal impacts
• Contributed analyses and visualizations to grant reports and presentations

Graduate Teaching Instructor | SUNY Stony Brook (Dept. of Political Science)

• Instructor of record for Experiments in Political Science and Introduction to Statistics courses
• Effectively communicated complex statistical and programming concepts (R, causal inference) to diverse graduate and undergraduate audiences
• Developed and led workshop series: "Introduction to R for Social Sciences"

Wyzant Tutor | Remote

• Provided tutoring in statistics, linear algebra, calculus, research methods, and data visualization

Education

PhD Candidate, Political Science | Stony Brook University

• Coursework focuses: machine learning and big data, causal inference, experimental design, American political behavior
• Dissertation research: urban-rural ideological divide, immigration attitudes, and subnational policy making
• Status: On hold to pursue full-time work in AI safety

BA, Psychology and Philosophy | University of Arkansas

• Summa Cum Laude
• Thesis: The SNARC effect and the cognitive representation of a mental number line
• Recipient of the J. William & Elizabeth W. Fulbright Scholarship (2014–2015)
  • Studied at Universität Karl-Franzens (Graz, Austria)
  • Specialized in philosophy of science

Projects

Automated AI News Tracker & Visualization | May 2025

• Developed automated pipeline using GCP Cloud Functions and Scheduler to track AI news coverage
• Integrated with NYT Developer APIs to retrieve and analyze news data
• Implemented text classification using LLM API to identify AI-related content

R Package: callm | Creator & Maintainer | 2024 – Ongoing

• Developed an R package providing a provider-agnostic interface for LLM API calls, simplifying workflows for integrating LLMs into research

Immigration Opinion and Policy Analysis | 2023 – 2024

• Estimated state-level public opinion on immigration using MRP techniques on decades of Cooperative Election Study data
• Modeled the impact of opinion shifts on state policy adoption using time-series cross-sectional analysis
• Received the 2023 NYAAPOR/PANJAAPOR Young Public Opinion Star award for ‘Immigration Opinion in the 50 States’

AI Dislocation and Economic Ideology | Jan 2025

• Designed and analyzed survey experiment linking economic ideology (status vs. equality concerns) to public perceptions of AI's economic impact

Debunk Bot Study Reanalysis | Jan 2025

• Conducted quantitative reanalysis of experimental data on AI chatbot effectiveness in countering misinformation

Broader Impacts and Researcher Productivity | GTM2021 Conference

• Presented analysis of factors associated with broader societal impacts of NSF-funded research

Publications

• Technical Reviewer: Deep Learning for Biology (O'Reilly, 2025) — co-authored by DeepMind researcher Natasha Latysheva
• Feldman, S., Mérola, V., & Dollman, J. (2021). The Psychology of Authoritarianism and Support for Illiberal Policies and Parties. Routledge Handbook of Illiberalism.
• Dollman, J. & Levine, W. H. (2016). The Mental Number Line Dominates Alternative, Explicit Coding of Number Magnitude. Quarterly Journal of Experimental Psychology, 69(3), 403–409.

Skills

Certifications

⚠️ LinkedIn-style Cheap Credential Flaunting