Fillip Kosorukov — Research & Academic Work

Fillip Kosorukov is a behavioral psychology researcher and University of New Mexico alumnus (B.S. Psychology, Summa Cum Laude, 2020). His peer-reviewed work — conducted at UNM’s Center on Alcoholism, Substance Abuse, and Addictions (CASAA) — focused on motivational interviewing (MI) and protective behavioral strategies (PBS) as mechanisms of change in brief interventions for heavy-drinking college students.

As a research assistant in the MATEO Lab (Motivation, Alcohol, and Treatment Outcomes) under Dr. Margo Villarosa-Hurlocker — part of UNM’s Center on Alcoholism, Substance Abuse, and Addictions (CASAA) — Fillip Kosorukov contributed to NIH-funded intervention research published in the Journal of Substance Use (2023) and presented at the annual meeting of the Research Society on Alcoholism (2020).

Education

Bachelor of Science, Psychology — Summa Cum Laude
University of New Mexico
August 2018 – May 2020

Publications

Villarosa-Hurlocker, M. C., Kosorukov, F. D., Graham, J., & Hatch, M. (2023). The effects of protective behavioral strategies on heavy drinking following a pure motivational interviewing intervention. Journal of Substance Use, 28(3), 349–354. https://doi.org/10.1080/14659891.2022.2047806

PubMed: PMID 37275205
Open access: PMC10237272

This study examined whether college students who received a “Pure Motivational Interviewing” alcohol intervention — one that did not explicitly discuss protective behavioral strategies — would still increase their use of those strategies and reduce heavy drinking in social situations. Results showed that increases in PBS use partially mediated reductions in heavy drinking, supporting the model that client-centered MI can indirectly mobilize cognitive-behavioral mechanisms of change without didactic content.

Kosorukov, F. D., Graham, J., & Villarosa-Hurlocker, M. C. (2020). Testing the mediating role of protective behavioral strategies on hazardous drinking behaviors using a pure motivational interviewing intervention [Conference abstract]. Alcoholism: Clinical and Experimental Research, 44(Suppl 1), 154.

First-authored conference abstract (Research Society on Alcoholism annual meeting) that laid the methodological foundation for the 2023 journal publication, proposing the within-participant mediation model later tested in the full study.

Research Interests

Fillip Kosorukov’s research focuses on motivational interviewing, protective behavioral strategies, and college-student drinking interventions. His work examines behavioral intervention design, mechanisms of change, decision-making under uncertainty, and the translation of psychological science into scalable consumer technology.

Methodology & Training

Training and research experience at UNM included within-participant mediation modeling (Montoya & Hayes framework), bootstrap-based inference, pilot intervention design for college populations, and structured clinical interviewing measures for hazardous drinking and social anxiety (Mini-SPIN, AUDIT).

The 2023 paper’s within-participant mediation design was chosen to address a practical constraint familiar in brief-intervention research: group-level mediation tests often demand sample sizes that college-drinking trials struggle to recruit. Modeling change within each participant across sessions provided a more statistically efficient path to mechanism-of-change evidence. Supporting measures included the Alcohol Use Disorders Identification Test (AUDIT) to classify hazardous drinking and the Mini Social Phobia Inventory (Mini-SPIN) to control for trait social anxiety — a known confound in college-drinking research that can inflate apparent intervention effects.

Academic Profiles

Current Work

Fillip Kosorukov now applies behavioral-science training to founder-led technology products:

LocalMention.io — AI visibility audit platform for local businesses, measuring how brands appear in generative AI responses (ChatGPT, Gemini, Perplexity).
FixMyRecord.io — consumer reputation and data-broker-removal service, automating personal privacy remediation across 80+ data brokers.

The thread across this work — research and products — is the same one Fillip found at the MATEO Lab: behavior changes when the conditions around it change, not when information alone is added. That principle shows up in LocalMention’s audit framing (a business reworks its content only after it sees what AI is saying about it) and in FixMyRecord’s design (privacy outcomes improve when the friction of removal becomes someone else’s job).

Contact

Collaboration or research-related inquiries: fkosorukov@unm.edu