Mission Hall - 3rd Floor - Room 3700

CAPS Town Hall presents: Vincent Muturi-Kioi, MBChB, DTM&H, MS -- African Centered Science, Engagement and Testing: The Road to an HIV Vaccine and Other Solutions for Global Health Challenges

Dr. Vincent Muturi-Kioi is Medical Director at International AIDS Vaccine Initiative (IAVI), stationed at the IAVI Africa regional office in Kenya.  In this role, Dr. Muturi-Kioi is responsible for medical monitoring of clinical trials in Africa and involved in the design and implementation of epidemiological studies aimed at providing data to be used for the design of efficacy trials. He also participates in the development and implementation of training activities with clinical partners.

CAPS Town Hall presents: Lindsay Young, PhD -- Social Network Analysis and Machine Learning: Computational Partners in the Study of HIV Prevention and Risk Online

As transmitters of information and progenitors of behavioral norms, social networks are critical mechanisms of HIV prevention and risk in impacted populations like men who have sex with men (MSM), people who inject drugs (PWID), and homeless youth. Today, widespread use of online social networking technologies (e.g., Facebook, Instagram, Twitter) yield unprecedented amounts of relational and communication data far richer than anything previously collected in offline (physical) network settings.

CAPS I&I Core presents: Harsha Thirumurthy, PhD -- Behavioral Economics in HIV Research

Harsha Thirumurthy is Associate Professor in the Department of Medical Ethics an Health Policy at the University of Pennsylvania. He is also Associate Director at the Center for Health Incentives and Behavioral Economics, where he leads global initiatives and a Research Associate at Penn's Population Studies Center. Professor Thirumurthy's interest lie at the intersecton of economics and public health.

CAPS Methods Core presents: Maya Petersen, MD, PhD, Co-Chair, Graduate Group in Biostatistics, UC Berkeley

Abstract: Targeted Maximum Likelihood Estimation (TMLE) provides an approach for estimating the causal effects of longitudinal interventions with several attractive properties. TMLE uses estimates of both the propensity score (as used in inverse probability weighting) and of a series of outcome regressions (as can be used in parametric G-computation). Machine-learning methods, such as Super Learning (an ensemble approach) can be used to estimate both the propensity score and outcome regressions.