Bayesian inference is a popular and practical tool for statistical inference. However, practitioners must make two major modeling choices when using Bayesian inference: the choice of the prior and likelihood. Uncertainty in these choices gives rise to the study of Bayesian robustness, which in part seeks to answer how posterior inferences would change had a practitioner made different modeling choices. I will give an overview of the field of Bayesian robustness with an emphasis on sensitivity to the specification of the prior and sensitivity to likelihood misspecification. To highlight

Recent advances in voltage imaging have opened the door to high-speed recordings of neural activity. In combination with spatially patterned optogenetic stimulation, one can measure the response of a neural circuit to nearly arbitrary spatiotemporal input patterns. These tools have been applied to studies on cultured neurons (primary and human stem cell-derived) in models of health and disease; to recordings of circuit dynamics in awake, behaving mice and fish; and to measurements on engineered cell lines. With the incredible power of these tools comes great computational challenges. I will

How can multiple biobanks perform a genome-wide association study (GWAS) without sharing the underlying data? Jon will review multi-party linear regression as a two stage process: compressing big data within party, and combining small data between parties. From a geometric perspective, he'll then to derive a simple, efficient distributed algorithm for testing millions of variants in multi-party GWAS ( To add provable security to stage two, Hoon will introduce key concepts from cryptography (secret sharing and secure multiparty computation) that enable privacy

In biomedical research, computational models are often used to infer biological knowledge from limited data (e.g. a given tissue, cell line, patient population, etc) with the intention of generalizing findings. In some cases the data can be successfully repurposed to answer a question it was not necessarily collected to answer, while in others, it falls short of its intended purpose. This talk will serve as a primer to Dr. Goldenberg’s discussion of prediction tasks across domains. First I will describe how we elucidate tissue-specific vs tissue-agnostic patterns of regulation using predictive

The development and application of methods for the laboratory evolution of biomolecules has rapidly progressed over the last few decades. Advancements in continuous microbe culturing and selection design have facilitated the development of new technologies that enable the continuous directed evolution of proteins and nucleic acids. These technologies have the potential to support the extremely rapid evolution of biomolecules with tailor-made functional properties. Continuous evolution methods must support all of the key steps of laboratory evolution — translation of genes into gene products