Decoding behavioral signifiers for choice and memory can have far reaching implications for understanding actions and identifying disease. We use a four arm maze where we are able to observe choices and infer memory in mice, but have access to very few pre-determined behavioral signifiers. Several recent publications implemented computer vision to extract a variety of previously unreachable aspects of behavioral analysis, including animal pose estimation (Mathis et al., 2018) and distinguishable internal states (Calhoun et al., 2019). These descriptions allowed for the identification and characterization of dynamics, which then revealed an unprecedented richness to the behaviors that determine decision making. Applying such computational approaches to examine behavior in our maze in the context of behaviors that have been validated to measure choice and memory can reveal dimensions of behavior that predict or even determine these psychological constructs. DSI scholars would use pose estimation analysis to evaluate behavioral signifiers for choice and memory and relate it to our real time concurrent measures of neural activity and transmitter release. The students would also have opportunity to examine the effect of disease models known to impair performance on our maze task on any identified signifier.

Continue reading

This project will be focused on creating a deep learning framework for tracking individual molecules and proteins as they move within a cell under various conditions. Using total internal reflection (TIRF) microscopy, we have accumulated more than 10 million trajectories over dozens of experimental preparations with differences in both the imaging approaches as well as the biological context. In our experiments we have captured particles under a wide variety of conditions including increased protein expression level, and a range of drug concentrations. Our biggest challenge is being able to stably track the movement of a particle as it passes by other particles or groups of particles, and to do this in a way that generalizes over novel conditions. The Data Science Institute Scholar chosen for this project would work with scientists in the Javitch laboratory and others across the Columbia campus to conceive of an approach for efficiently and effectively tracking particles. The resulting work would be of great interest to an increasing number of scientists working in this field who currently rely on methods based on feature engineering that are often inaccurate or inflexible compared to modern deep learning methods.

Continue reading

All complex behaviors require animals to coordinate their perception and actions. To successfully achieve a goal, a decision maker (DM; be it a human, animal, or artificial agent) must determine which action to take and, faced with much more information than she can fully process, must decide which source of information to consult to best guide that action. But in contrast with natural tasks, traditional research has focused primarily on action selection but eschewed the process of information demand. We aim to fill this gap by investigating the factors that motivate people to become curious and seek information. We are collecting behavioral data from a large sample of participants on a battery of online tasks testing various aspects of curiosity, and seek a DSI scholar who can quantitatively analyze the data. The scholar will be supervised by two co-PIs: Jacqueline Gottlieb, in Columbia’s Neuroscience Department and Zuckerman Institute, and Vince Dorie, in the DSI. The scholar will obtain training with advanced data analytic methods and the opportunity to co-author what is expected to be a high impact paper with interdisciplinary appeal in economics, neuroscience, and psychology.

Continue reading

We have been studying bladder cancer in a mouse model of the disease and we are seeking to understand the molecular features of the mouse models as they relate to human bladder cancer.

Continue reading

Big data with temporal dependence brings unique challenges in effective prediction and data analysis. The complex high-dimensional interactions between observations in such data brings unique challenges which standard off-the-shelf machine learning algorithms cannot handle. Even basic tasks of clustering, visualization and identification of recurring patterns are difficult.

Continue reading

The funded study examines policies that regulate the PO supply, including those related to prescription drug monitoring programs, pain management clinic laws, and prescribing limits. In this supplement, we add a set of complementary opioid policies that affect access to treatment for opioid use disorder (e.g., Medicaid coverage of medication for opioid use disorder).

Continue reading

The Quadracci Sustainable Engineering Lab (qSEL) has several research efforts related to the low-carbon energy transition, including pathways to decarbonize building space heating. Recent work has produced large model data sets that have supported recent journal articles. Several maps have been produced using QGIS and data has been made public, but user functionality is limited. While we continue to build on these efforts, we also want to make our results and data available more widely for other researchers and policymakers. The large data sets (10 years of hourly data for more than 72,000 census tracts and six scenarios) and different spatial aggregations (e.g. states and electricity planning/operating regions) present challenges. In this project, the DSI Scholar would first work with qSEL researchers to develop an interactive web interface to display maps of relevant analyses and allow users to produce time series data from the underlying models. Additional research would include further analysis at a regional level – likely New York State – to refine the current model based on additional intraregional and energy source data. The project has the possibility of extending through Summer 2020, subject to fundraising efforts and the success of the Spring 2020 project.

Continue reading