The project has collected a large set of data (>200GB) from a cryptocurrency block chain. It is developing methods for detecting anomalies in transactions based on newer Social Networks, Graph Analysis and Machine Learning methods. The work involves data cleaning/wrangling and creation and implementation of various algorithms and analyzing the transactions for identifying different set of anomalies and manipulations.
Last year one of my graduate students developed a novel algorithm for detecting “weird” signals in photometric time series, such as those taken by NASA’s Kepler Mission and now TESS. An undergraduate students will work in my team to run the algorithm on TESS data, which is just starting to be released publicly (https://heasarc.gsfc.nasa.gov/docs/tess/status.html). We hope to detect strange signatures, possibly including analogs to Tabby’s Star, interacting binaries and perhaps even technosignatures.
This project works with a novel corpus of text-based school data to develop a multi-dimensional measure of the degree to which American colleges and universities offer a liberal arts education. We seek a data scientist for various tasks on a project that uses analysis of multiple text corpora to better understand the liberal arts. This is an ongoing three-year project with opportunities for future collaborations, academic publications, and developing and improving existing data science and machine learning skills.
In a globalized world we live in today consequences of catastrophic events easily transgress national borders. Whether it’s a natural disaster, a war or an economic crisis it’s likely to spread out and affect all of us. We propose a framework to model global risks that is not bound to any specific model and is a hybrid of human and machine intelligence. The core of this approach is in using Bayesian Nets of causalities constructed by an analyst equipped with text mining and a map of economic, political and business interconnections.
Despite the promise of predictive analytics in healthcare, the lack of continuous internal sensing devices has impeded its realization. With the exception of CGMs, no current commercially available wearable devices yield information intimate to the body. To overcome this deficiency, our research group has developed a minimally invasive wearable device capable of continuous monitoring of glucose and electrolytes in the superficial layer of the skin in an extremely minimally invasive manner.
The Data Science Institute is pleased to call for Faculty Participation in the 2nd cohort of Data Science Institute (DSI) Scholars Program for Spring and/or Summer 2019. The goal of the DSI Scholars Program is to engage undergraduate and master students to work with Columbia faculty through a data science research internship. The program’s unique enrichment activities will foster a learning and collaborative community in data science at Columbia.