Causal inference, graphical models, machine learning in public policy and biomedicine
Daniel is a postdoctoral fellow working with Ilya Shpitser (JHU) and Eric Tchetgen Tchetgen (UPenn) on causal inference with graphical models. His primary interest has been causal structure learning, i.e., data-driven methods for selecting causal graphical models from observational data. Currently, he is thinking about missing data, semiparametric inference, and stochastic processes. Daniel has a BA from Columbia University and defended his PhD at Carnegie Mellon University in December 2017.
Medical image analysis; statistical shape modeling; deformable registration; disease detection
Ayushi is a Provost’s Postdoctoral Fellow working with Russ Taylor and Greg Hager on enhancing endoscopic navigation during clinical endoscopic exploration and endoscopic surgeries. Her primary interests include statistical analysis of large population data to understand anatomical variation and to improve deformable registration between different modalities. She is currently exploring methods to assign confidence to computed registrations which will allow clinicians to gauge when a registration is reliable. She is also interested in seamlessly integrating these navigation systems into clinical and surgical settings. Ayushi has a BS and BA from Providence College and an MSE and PhD from the Johns Hopkins University.