Pronouns: He/Him/His
Licensed Psychologist (New York)

Editor-in-Chief, Clinical Psychological Science (effective January 1, 2026)
President-elect, Academy of Psychological Clinical Science
DSM Externalizing Disorders Review Committee
Founding Executive Committee Member, Hierarchical Taxonomy of Psychopathology (HiTOP)

ADMISSIONS
Dr. Eaton does not know yet whether he will recruit a graduate student to begin in Fall 2026. Final decisions about recruitment depend on Graduate School funding lines, departmental priorities, and applicant fit. Typically, this is finalized in late October or early November. Applicants do not need to email Dr. Eaton to inquire if he will be admitting a student in Fall 2026. This page will be updated as soon as the decision is made, and every application will be read in full, if so.

In general, applicants with interests in (a) psychopathology classification and psychometrics; (b) AI/LLMs and their applications to mental health; (c) DEIJ-related issues and mental health disparities; and/or (d) the application of advanced quantitative methods to questions related to mental health are encouraged to apply. Similarly, applicants from historically underrepresented groups, first‑generation college graduates, those with lived experience of disability (broadly defined), and so on are also strongly encouraged to apply and to discuss in their applications, to the extent they feel comfortable, the personal impact of these experiences. Our laboratory embraces our differences.

RESEARCH INTERESTS
Conceptualization, classification, assessment, and structure of psychopathology; comorbidity; mental health disparities; minority stress; marginalized identities (including those defined by sexual orientation, gender identity, race, ethnicity, and their intersections); the use of artificial intelligence (particularly large language models [LLMs]) to better understand and improve mental health; quantitative methods and psychometrics; normal-range personality and personality psychopathology; individual differences.

SELECTED CURRENT RESEARCH AREAS
1) Classification, Psychometrics, and Computational Psychiatry
We study the conceptualization, structure, and assessment of psychopathology with a strong emphasis on HiTOP and allied quantitative nosology. A major line of work has examined the structure and classification of transdiagnostic internalizing psychopathology (e.g., mood and anxiety problems) and externalizing psychopathology (e.g., harmful substance use, antisociality/antagonism, impulsivity‑related problems), clarifying the utility of dimensional, categorical, and/or hybrid models, particularly with regard to their congruence with the observed data. We use tools including confirmatory and exploratory factor analysis, structural equation modeling, item response theory, latent class and factor‑mixture models, network psychometrics, multilevel models, and various other approaches to evaluate competing theories and improve measurement. We treat hierarchy as construct (i.e., putatively different solutions/structures may just represent the same phenomenon at different levels of resolution, from fine-grained to diffuse), and we attempt to synthesize and integrate “competing” models into a single hierarchy of related models whenever possible to unify the literature.

2) Studies Of AI (and Particularly LLMs)
(i) Scalable psychological solutions to social problems. Despite the existence of a robust arsenal of evidence‑based assessments, treatments, and even classification systems, the global burden of mental health problems continues to rise. That is, although the scientific literature provides ample evidence for the efficacy and effectiveness of many aspects of applied psychological practice, psychopathology persists and, in many cases, societal mental health is even deteriorating. In that context, we must thus ask ourselves: If what we are doing works, why is what we’re doing not working?

While there are many answers to the question posed above, we believe a central reason is the lack of scalability of current approaches. The modalities we use—such as one-on-one, in-person psychotherapy, and even fully human-delivered interventions at that—seem incapable of allowing services to reach all those who need them. Our lab investigates the use of technology to provide scalable psychological solutions to societal problems—most notably we focus on how AI (e.g., LLMs) can help form ethical, socially responsible, and equitable pipelines that scale research, clinical practice, and knowledge mobilization while safeguarding privacy, mitigating model bias, and prioritizing accessibility and service utilization for all people who need it.

(ii) LLMs as a mirror for humanity. LLMs are trained on vast corpora of human language, and the metaphorical universe in which they exist is one defined not by time or space but by the lexicon. Thus, through their training LLMs learn and encode societal knowledge and notions about mental health as they are encapsulated in how we communicate about them. By analyzing how LLMs respond to novel (not in their training data) psychometric scale items, for instance, and comparing these responses to human responses, we can test to what extent the language‑based representations of psychopathology that LLMs learn to approximate the structural and dynamic symptom representations that humans report. These efforts help us understand the role language itself may play in how people conceive of and communicate about psychopathology. This sort of understanding is often not easily ascertained when studying human participants directly, because it is difficult to separate how we think about our thoughts, feelings, behaviors, and the world from how we communicate about them. (Take for instance the period in antiquity prior to the introduction of zero as a symbol and concept: It was generally not considered a quantity or the presence of absence—rather, it was considered something more akin to “empty.”) In this way, studying LLMs, trained on our own language, holds up a metaphorical mirror to humanity and provides a highly unique and potentially very informative perspective—a perspective on ourselves we might not get otherwise. Further, these efforts involve the pursuit of intriguing possibilities, such as if LLMs learning to mimic realistic human responses on items, because that could revolutionize measure development and allow for the in silico creation of synthetic data, which would likely be more generalizable for simulation research than the data currently simulated.

(iii) LLMs as entities worthy of inquiry in their own right. We probe how LLMs learn and represent mental-health concepts via tokenization and high-dimensional numerical encodings. Put simply, computers do not “speak” any human language; instead, they operate through numerical operations. When someone prompts an LLM like ChatGPT or Gemini with text, the system converts that language into smaller units called tokens, which may be whole words, subwords, or punctuation. Each token is then mapped to a list of numbers, a vector, called an embedding. Embedding vectors are often very large. In some widely used embedding models released by OpenAI and Google, each token or word is represented as a vector containing 3,072 numbers—thousands of digits that together capture its meaning in a mathematical space. This is similar to how GPS coordinates locate a particular place on the Earth with two numbers (latitude and longitude), but embeddings locate the geometric position of tokens using over three-thousand digits rather than just two. The token for the word “cat” might be represented as something like [-0.23, 4.21, 3.01, …, -11.27], totaling 3,072 digits, just used to embed the token “cat.”

By mapping the geometric closeness of psychopathology concepts, and by linking lexical embeddings of items to human responses on those same items, we attempt to understand the structures of mental health problems that LLMs learn from our language. In particular, we are interested when what LLMs learn about mental health concepts aligns with, or challenges, the understandings that emerge from human data. Although it is commonly understood that LLM embeddings—used to support billions of internal computations—are not linked to human language concepts on a one-to-one basis, preliminary findings from our lab suggest this common wisdom may not necssesarily be accurate. Using a novel cross-validated methodology we recently developed, we have begun linking model-based embeddings of the text of questionnaire items to actual human self-reported responses to those same items with near-perfect fidelity. If this finding proves robust, it may have groundbreaking implications. It would challenge foundational assumptions underpinning psychometric assessment and the meaning of measures, construct validity, and latent variable theory; it would also introduce exciting new ways to align neural networks’ embeddings with semantic and behavioral dimensions that humans understand.

3) DEIJ, Minority Stress, and Mental‑Health Disparities
We study mental‑health disparities affecting individuals and communities with minoritized and intersecting identities (e.g., race, ethnicity, sexual orientation, gender identity, disability status). Our work examines how minority stress processes (e.g., prejudice, discrimination) and structures of inequality and oppression cause and perpetuate mental health disparities—and how classification and measurement choices scientists make when doing research on these issues can obscure or reveal these processes. Our findings could support more just and equitable assessment, intervention, and policy.

4) Quantitative Modeling Advances
We apply, develop, and teach new and sophisticated quantitative methods increasingly essential for clinical psychological science, including machine learning, Bayesian analysis, and causal modeling, alongside classical and modern statistics and psychometrics. We apply techniques from other fields to provide novel answers to mental‑health questions and build new tools when existing methods fall short—always with an eye toward open, reproducible workflows and practical utility for clinicians and researchers.

RECENT REPRESENTATIVE PUBLICATIONS
* denotes a current or former undergraduate, graduate student, or postdoctoral fellow for whom Dr. Eaton served as primary advisor
† denotes a current or former departmental graduate student or postdoctoral fellow
^ denotes joint first-author position
# denotes joint senior-author position for Dr. Eaton (when otherwise unclear)

For a full list of Dr. Eaton’s publications, please see his Google Scholar profile.

2025
Shen, J.*, Schleider, J. L., Nelson, B. D., Richmond, L. L., London, B., & Eaton, N. R. (2025). Disparities in COVID-19-related trauma and internalizing symptoms across sexual orientation, race/ethnicity, and their intersection during the pandemic. Psychology of Sexual Orientation and Gender Diversity, 12, 26–41. https://doi.org/10.1037/sgd0000655

2024
Rajesh, A., Zhang, K. E., Kelly, C., Silvan, Y. A., Diehl, C. K., Obee, A. F., …, & Eaton, N. R. (2024). Diversity, equity, inclusion, justice, and anti-racism statements by clinical psychological science programs: A mixed-methods analysis of public commitments. Training and Education in Professional Psychology, 18(3), 183–193. https://doi.org/10.1037/tep0000431

2023
Eaton, N. R., Bringmann, L. F., Elmer, T., Fried, E. I., Forbes, M. K., Greene, A. L.*, Krueger, R. F., Kotov, R., McGorry, P. D., Mei, C., & Waszczuk, M. A. (2023). A review of approaches and models in psychopathology conceptualization research. Nature Reviews Psychology, 2(10), 622–636. https://doi.org/10.1038/s44159-023-00218-4

Greene, A. L.*, Watts, A. L., Forbes, M. K., Kotov, R., Krueger, R. F., & Eaton, N. R. (2023). Misbegotten methodologies and forgotten lessons from Tom Swift’s Electric Factor Analysis Machine: A demonstration with competing structural models of psychopathology. Psychological Methods, 28(4), 957–979. https://doi.org/10.1037/met0000465

Lin, S.-Y.*, Schleider, J. L., Nelson, B. D., Richmond, L. L., & Eaton, N. R. (2023). Gender and racial/ethnic disparities in undergraduate and graduate students’ mental health and treatment use amid the COVID-19 pandemic. Administration and Policy in Mental Health and Mental Health Services Research, 50(3), 415–430. https://doi.org/10.1007/s10488-022-01241-5

McDanal, R.*, Schleider, J. L., Fox, K. R., & Eaton, N. R. (2023). Loneliness in gender- and sexual-orientation diverse adolescents: Measurement invariance analyses and between-group comparisons. Assessment, 30(3), 706–727. https://doi.org/10.1177/10731911211065167

Wang, M.†, & Eaton, N. R. (2023). Linking non-suicidal self-injury to psychopathology: The utility of transdiagnostic and DSM-based models. Journal of Affective Disorders, 330, 355–365. https://doi.org/10.1016/j.jad.2023.02.037

Zhang, Y.*, Silver, J. I.†, Perlman, G., Kotov, R., Klein, D. N., & Eaton, N. R. (2023). Longitudinal stability and interrelations of tonic and phasic irritability in adolescent girls. Research on Child and Adolescent Psychopathology, 51, 985–1000. https://doi.org/10.1007/s10802-023-01080-7

2022
Watson, D., Levin-Aspenson, H. F., Waszczuk, M. A., Conway, C. C., Dalgleish, T., Dretsch, M. N., Eaton, N. R., et al. (2022). Validity and utility of the Hierarchical Taxonomy of Psychopathology (HiTOP): III. Emotional dysfunction superspectrum. World Psychiatry, 21(1), 26–54. https://doi.org/10.1002/wps.20940

2021
Eaton, N. R., Rodriguez-Seijas, C.*, & Pachankis, J. E. (2021). Transdiagnostic approaches to sexual and gender minority mental health. Current Directions in Psychological Science, 30(6), 510–518. https://doi.org/10.1177/09637214211036455

Forbes, M. K., Greene, A. L.*, Levin-Aspenson, H. F., Watts, A. L., Hallquist, M. N., Lahey, B. B., … Eaton, N. R.#, & Krueger, R. F.# (2021). Three recommendations based on a comparison of the reliability and validity of predominant models used in research on the empirical structure of psychopathology. Journal of Abnormal Psychology, 130(3), 297–317. https://doi.org/10.1037/abn0000664

Krueger, R. F., Hobbs, K. A., Conway, C. C., Dick, D. M., Dretsch, M. N., Eaton, N. R., et al. (2021). Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): Externalizing superspectrum. World Psychiatry, 20(2), 171–193. https://doi.org/10.1002/wps.20842

2019
Greene, A. L.*, Eaton, N. R., Li, K., Forbes, M. K., Krueger, R. F., Markon, K. E., … Kotov, R. (2019). Are fit indices used to test psychopathology structure biased? A simulation study. Journal of Abnormal Psychology, 128(7), 740–764. https://doi.org/10.1037/abn0000434

Rodriguez-Seijas, C.*, Eaton, N. R., & Pachankis, J. E. (2019). Prevalence of psychiatric disorders at the intersection of race and sexual orientation: Results from the NESARC-III. Journal of Consulting and Clinical Psychology, 87(4), 321–331. https://doi.org/10.1037/ccp0000387

2017
Kotov, R., Krueger, R. F., Watson, D., Achenbach, T. M., Althoff, R. R., … Eaton, N. R., & Wright, A. G. C. (2017). The Hierarchical Taxonomy Of Psychopathology (HiTOP): a dimensional alternative to traditional nosologies. Journal of Abnormal Psychology, 126, 454–477. https://doi.org/10.1037/abn0000258

SELECTED LAB ACHIEVEMENTS & RECOGNITIONS

Selected Major Honors & Leadership (Dr. Eaton; see others above as well)
• Director of Clinical Training, Stony Brook University – 2019–2026
• Invited External Examiner, University of Hong Kong – 2023–2026
• Clinical Scientist Social Justice Impact Award, Society for a Science of Clinical Psychology (SSCP) — 2025
• Fellow, Association for Psychological Science (APS) — 2024
• Samuel M. Turner Early Career Award for Distinguished Contributions to Diversity, APA Division 12 — 2020
• Martin Mayman Award, Society for Personality Assessment (best theoretical contribution to Journal of Personality Assessment) — 2019
• Hugo G. Beigel Award, Society for the Scientific Study of Sexuality (best contribution to Journal of Sex Research) — 2018
• Robins/Guze Early Career Award, American Psychopathological Association — 2017
• Martin Mayman Award, Society for Personality Assessment (best theoretical contribution to Journal of Personality Assessment) — 2015

Selected Major Honors & Achievements (Lab Trainees)
• Jared Gabrielli, Cohere for AI Research Grant Program — 2025
• Yinghao Zhang, John Neale Endowed Graduate Student Excellence Fund (SBU) — 2025
• Jenny Shen, SSHRC Doctoral Fellowship (Canada) — 2022–2024
• Riley McDanal, COGDOP/APF Graduate Student Dissertation Research Award — 2024
• Riley McDanal, NSF Graduate Research Fellowship (GRFP) — 2022–2025
• Sin-Ying Lin, John Neale Endowed Graduate Student Excellence Fund (SBU) — 2021
• Riley McDanal, John Neale Endowed Graduate Student Excellence Fund (SBU) — 2021
• Riley McDanal, LifeData Award for Free Usage — 2020
• Qimin Liu, Smadar Levin Award, Society for Research in Psychopathology — 2019
• Sin-Ying Lin, Best Poster Award, ABCT Clinical Research Methods & Statistics SIG — 2019
• Tenille Taggart, Malyon-Smith Scholarship, APA Division 44 — 2019
• Craig Rodriguez-Seijas, President’s Award & Dissertation Grant, Society for Research in Psychopathology — 2019
• Bernie Chen, Provost’s Award for Academic Excellence (Stony Brook University) — 2019
• Hyunsik Kim, Graduate Student Organization Distinguished Travel Award (SBU) — 2019
• Craig Rodriguez-Seijas, Graduate Student Organization Distinguished Travel Award (SBU) — 2018
• Craig Rodriguez-Seijas, SSCP Dissertation Grant — 2018
• Tenille Taggart, NSF Graduate Research Fellowship (GRFP) — 2016–2019

Selected Trainee & Alumni Placements
• Craig Rodriguez-Seijas, Assistant Professor, University of Michigan (Ann Arbor, MI)
• Hyunsik Kim, Assistant Professor, Sogang University (Seoul, South Korea)
• Ashley Greene, Research Psychologist, James J. Peters VA Medical Center (NY)
• Alina (Sin-Ying) Lin, Data Scientist II, Qualtrics (Kirkland, WA)
• Rebecca Weber, Clinical Psychologist, Northport VA Medical Center (NY)