Tabula Rasa personnel served alongside an Early Psychosis Intervention (EPINET) team to support young adults and their families during the first onset of serious mental health challenges. Our role emphasized compassionate engagement, careful listening, and coordination with clinicians so that youth felt heard, respected, and supported. By helping translate clinical insights into practical next steps for treatment and family support, we reinforced a trauma-informed environment that promoted safety, dignity, and hope in the recovery process.

On a school-based adolescent therapy initiative in Frederick County, Tabula Rasa staff worked directly with youth, families, and school partners to create a safe, welcoming space for healing. We used trauma-informed counseling approaches that honored each student’s story, reduced stigma, and strengthened family involvement. Emphasis was placed on empathy, helping young people navigate anxiety, depression, and family stress while maintaining their connections to school and community.

For a mentoring and faith-based support program at Cheltenham Youth Detention Center, Tabula Rasa helped build a consistent, caring presence for justice-involved youth. Our work emphasized trauma-responsive mentoring, ethical manhood development, and emotional support that affirmed each young person’s worth and potential. By coordinating with chaplains, counselors, and community partners, we walked alongside youth as they prepared for reentry, focusing on healthy relationships, accountability, spiritual grounding, and realistic plans for education, work, and family reconnection.

Tabula Rasa contributed to a Howard University Center for Applied Data Science and Analytics (CADSA) project focused on modeling drivers of recidivism among justice-involved individuals. The effort involved designing and supporting a secure, cloud-hosted analytics environment for sensitive criminal-justice and social-service datasets, and developing data pipelines using Python. The team used modern AI tools, including open-source libraries such as Hugging Face, to support feature engineering and advanced modeling.