Santiago Papini

Background

My initial career was in education, teaching calculus and statistics at a high school in Brooklyn, New York. As my interests shifted to the mental health of my students, I pursued a Master’s in counseling but it wasn’t until I started working on clinical trials for PTSD and substance use that I realized I wanted to be a clinical scientist. This has led to diverse training experiences in academic, veteran, and civilian healthcare systems. I am passionate about research and clinical work that takes into account the rich and diverse complexity of individuals. As a teacher and mentor, I aim to stimulate students’ natural curiosity and impart tangible skills relevant to their goals. My philosophy recognizes that education opens doors to diverse careers, and I prepare students to become leaders in the field they choose to pursue.

Education

• NIMH T32 Research Fellow, Clinical Informatics, Kaiser Permanente Northern California, 2023
• UC President’s Postdoctoral Fellow, Predictive Modeling and Psychiatric Genetics, University of California San Diego, 2021
• PhD, Clinical Psychology, The University of Texas at Austin, 2021
• MA, Mental Health Counseling, City College of New York, 2013
• MS, Secondary Mathematics Education, Pace University, 2008
• BA, Philosophy, The University of Texas at Austin, 2006

Courses

• PSY 212: Research Methods
• PSY 675: Treatment Research

Research

My research interests arose from my clinical work with populations disproportionately impacted by trauma. Psychiatric comorbidity is the norm and symptoms are highly heterogeneous, even within the same disorder. This underlies the challenge of answering: What mechanisms should treatments target? How can interventions be personalized? I seek to address these challenges by developing targeted prevention and intervention strategies for addiction, anxiety, and stress-related psychopathology. To achieve this, my research applies the tools of data science and artificial intelligence with a focus on: 1) machine learning prediction models to guide scalable interventions, 2) data-adaptive methods to improve estimation of intervention effects, and 3) generative AI to enhance clinical and experimental protocols.