Selected recent presentations:
“AI-Driven Endpoint Optimization in Dose-Finding Studies”. 2025 Midwest Biopharmaceutical Statistics Workshop, [Link].
"Development of a Machine Learning Model Predicting Response to Aripiprazole Once-Monthly in Patients Diagnosed With Schizophrenia". 2025 Psych Congress, selected for Latest Discoveries & Emerging Trends in Psychotic Disorders.
“Enhancing HTA model adaptations with AI: Leveraging large language models and R-Shiny for local cost-effectiveness analyses”. 2025 R for Health Technology Assessment (HTA) workshop, [Link].
Short Course “Automated Health Economic Analysis using R Shiny”, 2024 ISPOR —The leading Professional Society for Health Economics and Outcomes Research (HEOR). [Course Info]
Selected earlier talks:
*Zhang, Z. and Gamalo, M. A novel win-odds based nonparametric test for dose finding studies using prioritized endpoints on efficacy and safety. ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop, Rockville, 2022.
*Lin, Y., Zhang, Z. and Liu, J. Probability of Study Success (PrSS) Evaluation Based on Multiple Endpoints in Immuno-Oncology (IO). ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop, Washington, DC, 2019. [Poster]
*Zhang, Z., Semiparametric Bayesian Analysis of Big Data with Censoring Observations. 9th International Purdue Symposium on Statistics, 2018, Purdue University. [Symposium]
*Zhang, Z., with Wan, H., Wang, Y., Xin, H., Yu, P., Li, Y. and Gehen, S. A clustering-based QSAR model for acute oral systemic toxicity. Invited speaker and panelist. Predictive Models for Acute Oral Systemic Toxicity Workshop, 2018, Natcher Conference Center, National Institutes of Health, Bethesda, Maryland. [NIH_VideoCast]
*Zhang, Z., Stein, M. L. and Anitescu, M., Multilevel Boundary Conditioning for Large Spatial Data Sets. Invited Poster, Joint Statistical Meetings, Seattle, 2015