About

Joseph Rich Cohen is an Associate Professor of Psychology and an Affiliate of the Social & Behavioral Sciences Institute at the University of Illinois. He earned his Ph.D. from Rutgers - The State University of New Jersey, New Brunswick. His research centers on developmental psychopathology, with a particular focus on understanding how and why certain children and adolescents develop internalizing symptoms such as depression. Cohen investigates the prospective interplay between cognitive, interpersonal, and physiological vulnerability factors and the onset and maintenance of depression in both domestic and international youth samples. In recent years, Cohen has increasingly focused on translating basic developmental psychopathology findings into practical screening procedures aimed at better identifying youth living in adverse family contexts who are at elevated risk for psychological distress. His future research plans involve adopting an applied developmental psychopathology approach to develop more objective psychosocial screening methods based on novel, longitudinal, laboratory studies with at-risk youth. Cohen's work contributes to advancing the understanding of risk factors and improving early identification and intervention strategies for youth mental health.

Research topics

• Psychology
• Clinical psychology
• Medicine
• Psychiatry
• Developmental psychology
• Medical emergency
• Demography
• Social psychology

Selected publications

• Moving toward equitable juvenile justice risk assessments for adolescents: Considering clinical, community, and statistical fairness.

  Psychological Assessment · 2026-01-26

  articleOpen access1st authorCorresponding

  Risk assessments are often mandated within the juvenile justice system (JJS). Yet, it is unclear whether these protocols reflect equitable clinical tools, and little is known about the community's perspectives on commonly assessed risk domains. In response, we introduced, and subsequently tested, a multifaceted definition for fairness in risk assessment. An embedded mixed-method study was conducted, such that Study 1 informed Study 2's methods, and the studies were subsequently integrated. In Study 1, caregivers (N = 22) and adolescents (N = 21; Mage = 14.28; 42.9% identified as Black, 42.6% White; 66.7% Male) involved with a JJS-diversion or probation program completed qualitative interviews on risk domains for offending behavior. Next, we examined the statistical fairness of salient risk domains from Study 1 in a sample of JJS-involved adolescents (N = 1,354; Mage = 16.04; 41.4% Black, 33.5% Hispanic, 20.2% White; 86.4% as male). An evidence-based medicine analytic approach, which was compared to artificial neural networks, tested subpopulation differences across performance indices. Overall, temperament, peer relations, cognitive styles, and school functioning emerged as salient risk domain themes across identities and informants. Subsequently, moral disengagement and delinquent peers emerged as equitable predictors of prospective violent and nonviolent rule-breaking behavior. A model comprised of these predictors was acceptable (i.e., areas under the curves ≥ 0.70; diagnostic likelihood ratios ≥ 2.0) and equitable. Artificial neural network models did not improve prediction. Risk assessments focused on moral disengagement and peer delinquency may lead to community-aligned and statistically fair assessment processes. These findings can lead to more equitable and engaging JJS risk-management approach. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

  PublisherDOI

• Alcohol Use Disparities Among Transgender and Nonbinary Adults: An Intersectional Investigation

  Drug and Alcohol Review · 2025-05-13

  articleOpen accessSenior author

  INTRODUCTION: This study examined an intersectional perspective on alcohol use disparities within transgender and nonbinary (TGNB) adults. METHODS: We examined the data from the 2015 U.S. Transgender Survey (N = 27,715), a cross-sectional, nationwide survey of TGNB adults. The number of drinking days and the number of binge-drinking days were primary outcomes. Analyses followed a multilevel analysis of individual heterogeneity and discriminatory accuracy approach to examine alcohol disparities across gender identities (transgender, nonbinary and crossdresser) and intersections with race/ethnicity, age, sex assigned at birth and dis/ability status. RESULTS: Significant identity-related differences existed within TGNB communities across all facets of identity. Relative to the sample average, individuals at the intersection of White and crossdressers reported elevated levels of alcohol use. Further, effects were most pronounced across combinations of transgender, nonbinary, White and Black participants to accurately describe alcohol risk in subpopulations relative to examining risk associated with each one of these identities independently. DISCUSSION AND CONCLUSIONS: Disparities in alcohol use among TGNB adults are best understood from an intersectional perspective. Affirming public health initiatives for alcohol use should consider identity-related differences across TGNB communities. SCIENTIFIC SIGNIFICANCE: Results provide the first evidence that alcohol use disparities exist across gender and intersecting identities in a large sample of TGNB adults. Findings lay the groundwork for future research examining mechanisms responsible for these disparities.

  PublisherOA PDFDOI

• A Trauma-Focused Screening Approach for Teen Dating Violence Prevention

  Prevention Science · 2025-01-01 · 2 citations

  article1st authorCorresponding
  PublisherDOI

• Disparities in Dating Violence Perpetration and Victimization in Justice-Involved Emerging Adults: A MAIHDA Study

  Journal of Family Violence · 2025-04-21 · 1 citations

  articleOpen accessSenior author

  Abstract Purpose Dating violence (DV) victimization and perpetration in emerging adulthood is a significant criminal justice and public health concern. Yet, research is mixed as to who is most at-risk for experiencing DV during this developmental period. In response, we examined (a) which dimensions of identity (e.g. college attendance, gender, race/ethnicity, SES) were the most salient predictors of DV experiences and (b) if an intersectional model provided incremental insight into DV disparities. Methods We conducted secondary data analyses using Pathways to Desistance , a longitudinal study with a racially/ethnically diverse, justice-involved sample of emerging adults ( N = 1,111). Both a traditional logistic regression approach and Multilevel Analysis of Individual Heterogeneity and Discriminatory Analyses (MAIHDA) were leveraged to examine DV disparities. Results Overall, for physical forms of DV, those who identified as Black, female, and non-college attending had higher rates of perpetrating DV and those who were Hispanic and who did not attend college had higher rates of victimization. For psychological DV experiences, non-college, Black, Hispanic, and female-identifying individuals had higher rates for perpetration, while those who identified as Hispanic had lower rates for victimization. Individuals possessing multiple, marginalized identities (e.g., Black and female, Hispanic and college-attending) had higher rates for DV-perpetration and victimization across subtypes. Conclusions Results provide preliminary support for subpopulations who can benefit most from dating violence prevention efforts. Translational implications of our findings, including the potential for the MAIHDA framework for uncovering violence disparities at the population health level, are discussed.

  PublisherOA PDFDOI

• Beyond Adverse Childhood Experiences: What Should be Considered for Trauma-Focused Adolescent Mental Health Risk Assessments?

  Journal of Interpersonal Violence · 2025-07-18 · 1 citations

  article1st authorCorresponding

  To align with emerging policies for adolescents, feasible, accurate, and equitable trauma-focused assessment protocols need to be developed. To date, most research on this topic has focused on whether traditional adverse childhood experiences (i.e., maltreatment, impaired caregiving) can adequately index mental health risk. Yet, there are noted clinical and statistical drawbacks to this approach. Instead, examining threat and reward biases, two subtypes of cognitive biases stemming from interpersonal trauma exposure, may provide a reasonable alternative to adversity screening. Thus, the aim of this study was to examine the accuracy and fairness of self-reported, trauma-informed cognitive vulnerabilities for classifying concurrent and prospective adolescent mental health risk relative to more commonly assessed childhood adversities. In a diverse adolescent sample ( N = 584; M Age = 14.43; 48.9% female; 35% African American; 38.5% White; 40% Hispanic) youth completed measures for adversity exposure (family, dating, and community violence), threat biases (posttraumatic cognitions, hostility), and reward biases (anticipatory, consummatory) during an initial assessment, as well as symptoms of posttraumatic stress (PTS), depression, and violent behavior at baseline and 1 year later. Indices of statistical discrimination, calibration, and statistical fairness were examined using an evidence-based medicine analytic approach, which was subsequently compared to a machine learning approach. Overall, posttraumatic cognitions emerged as an accurate and statistically fair predictor of prospective PTS (area under the curve [AUC] 95% CI = [0.63, 0.78]; diagnostic likelihood ratio [DLR] 95% CI = [1.32, 3.52]), and to a lesser extent depression (AUC 95% CI = [0.56, 0.70]; DLR 95% CI = [1.25, 2.98]), and both models were well calibrated (i.e., p -value >05 for Spiegelhalter’s Z test). Meanwhile, community violence (CV) exposure best classified the risk for prospective violent behavior (AUC 95% CI = [0.62, 0.73]; DLR 95% CI = [2.68, 5.49]), especially in males, and was well calibrated. The machine learning algorithms added limited incremental validity to our predictions. Our study suggests that focusing on posttraumatic cognitions and less invasive adversity items (i.e., CV exposure) may lead to trauma screening and assessment protocols that are accurate, equitable, and feasible to implement within applied settings serving diverse youth.

  PublisherDOI

• Searching for specificity in teen dating violence and distress: A network analytic approach.

  Psychology of Violence · 2025-10-16

  articleOpen accessSenior author
  PublisherDOI

• Between and within-person relations between psychological wellbeing and distress in adolescence: A random intercept cross-lagged panel examination

  Development and Psychopathology · 2025-05-28 · 2 citations

  articleOpen accessSenior author

  Abstract Holistic frameworks of mental health outline that a focus on psychopathology does not represent an optimal approach to defining, measuring and treating mental health. Rather, theoretical, empirical, and applied psychological efforts should incorporate psychological well-being (PWB). Studies of PWB have overwhelmingly focused on adult populations, rendering a translation down to adolescence difficult. The current study explores the between-person, as well as within-person short-term, prospective relations between psychopathology and wellbeing within a community sample of adolescents (i.e., 553 youth aged 12 – 18, mean age: 14.97 years, 51.2% Male, 40.7% of participants identified as Hispanic (225 individuals), 38.5% identified as White (213 individuals), and 35.6% identified as Black (197 individuals), 3-wave, 1-year survey). Results demonstrated significant, negative between-person relations between psychopathology and PWB ( b PHQ = −0.25, SE = 0.11, p = 0.021, b VDS = −0.39, SE = 0.15, p = 0.011). At the within-person level, consistent positive prospective relations were identified for violent-delinquent behaviors and PWB, such that increases in individual levels of violent-delinquent behaviors tended to forecast higher levels of PWB at the next follow-up ( b PWBW2 = 0.21, SE PWBW2 = 0.076, p < 0.01; b PWBW3 = 0.14, SE PWBW3 = 0.051, p < 0.01). At the within-person level, prospective relations between depressive and PWB were not identified. Gender and racial/ethnic identities did not moderate findings.

  PublisherDOI

• Data-Driven Prediction and Uncertainty Quantification on Chemical Concentration in Electroless Plating Process

  2025-06-23

  article

  Abstract Electroless plating is a chemical process commonly used in semiconductor manufacturing that deposits a uniform metal coating onto a substrate through an auto-catalytic reduction reaction without using electricity. High quality deposition hinges on the precise control of bath composition and operating conditions, which in turn requires accurate and accessible monitoring of the bath chemical concentration. While chemical analyzers present an effective monitoring technique, they are generally limited by high costs, complexity, and maintenance. Time-delayed measurements also impose challenges on real-time operations, and any reduction to the monitoring lag would be highly beneficial. We introduce a data-driven machine learning (ML) approach able to achieve fast concentration predictions directly from operating conditions, and able to compute the prediction uncertainty that is valuable for subsequent robust control and optimization and for improving transparency and trustworthiness of ML tools in manufacturing settings. Notably, our ML procedure overcomes challenges of asynchronous time-series training data with missing values, and where available data are often noisy and sparse. Our ML approach begins with a data preprocessing step to handle asynchronous measurements and missing values by engineering features rooted in the underlying physical processes. Notably, a systematic feature selection is performed to down select features that are most correlated with the prediction target, thereby reduce model overfitting. We then compare a number of regression model architectures for capturing the sequential data relationships, including linear models, random forest, extreme gradient boosting, and fully connected, long short-term memory, and transformer neural networks. We quantify the model uncertainty by training them in a Bayesian manner using scalable Stein variational gradient descent, to compute the posterior probability distributions conditioned on the training data that reflect the uncertainty in the models induced by the quality and quantity of the available observations. We demonstrate the overall ML approach on a real-world dataset from a semiconductor manufacturing plant that exhibits the aforementioned data challenges. The best performing models exhibited 1, 5, 12% accuracy (defined as achieving within a 3% margin) improvements on predicting the metal ion, alkali, reductant concentrations, respectively, over a naive feature extraction technique. It also achieved 13, 10, 32% accuracy improvements over an autoregressive integrated moving average (ARIMA) baseline. These results show the approach’s effectiveness in providing reliable predictions with quantified uncertainty.

  PublisherDOI

• Maltreatment-Exposure Screening and Adolescent Mental Health: An Evidence-Based Medicine Perspective on Accuracy and Fairness

  Journal of Child & Adolescent Trauma · 2025-11-08

  articleOpen accessSenior author

  Abstract Universal trauma-informed screening is increasingly recommended across pediatric settings, yet it remains unclear which forms of adversity most accurately and equitably identify youth at risk for psychopathology. In response, the current study tested whether defining maltreatment by cumulative quality (i.e., number of subtypes) versus cumulative frequency (i.e., number of experiences regardless of subtypes), as well as potential differences within each subtype, yielded more accurate and fair predictions of trauma-related psychological distress. Participants included 839 adolescents from the LONGSCAN study (50.5% identified as female; 55.2% identified as Black) who completed self-reports of maltreatment exposure and psychological distress at ages 12, 16, and 18. Statistical accuracy was assessed via statistical discrimination (area-under-the-curve [AUC]) and calibration (Spiegelhalter’s Z; calibration curves), while statistical fairness was evaluated through differences in accuracy across adolescent subgroups (i.e., gender; race/ethnicity). Results across cumulative risk models revealed equivocal results regarding accuracy, while demonstrating important patterns of unfairness. In contrast, analyses by maltreatment subtype identified emotional abuse quality and frequency as both accurate (AUC > 0.64; non-significant Spiegelhalter’s Zs; 95% confidence intervals for intercept and slope included 0 and 1, respectively) and statistically fair in identifying concurrent risk for trauma-related distress. Cumulative frequency and emotional abuse frequency also demonstrated accuracy and potential fairness in predicting risk at age 16, whereas no indicators demonstrated clinical utility at age 18. Overall, findings suggest that emotional abuse may be useful in screening for concurrent psychological distress, while adding to the literature questioning the continued reliance on cumulative ACEs scores in clinical and public health contexts.

  PublisherDOI

• The Prospective Impact of Perceived Social Support Profiles on Mental Health for Justice-Involved Youth

  Research on Child and Adolescent Psychopathology · 2025-04-07 · 1 citations

  articleSenior author
  PublisherDOI

Recent grants

• NIH Grant F31MH096430

  NIH · $47k · 2014

Frequent coauthors

• John R. Z. Abela

  18 shared

• Jami F. Young

  Philadelphia University

  17 shared

• Jeff R. Temple

  The University of Texas Health Science Center at Houston

  15 shared

• Hena Thakur

  University of Illinois Urbana-Champaign

  14 shared

• Benjamin L. Hankin

  University of Illinois Urbana-Champaign

  14 shared

• Carla Kmett Danielson

  Medical University of South Carolina

  13 shared

• Ryan C. Shorey

  University of Wisconsin–Milwaukee

  13 shared

• Zachary W. Adams

  Indiana University – Purdue University Indianapolis

  9 shared