Through ArXivLab we aim to develop the next generation recommender systems for the scientific literature using statistical machine learning approaches. In collaboration with ArXiv we are currently developing a new scholarly literature browser which will be able to extract knowledge implicit in the mathematical and scientific literature, offer advanced mathematical search capabilities and provide personalized recommendations.
Effective representations and analyses of symbolic data, such as lexical data (words) and networks (graphs), have become of great interest in recent years, due both to advancements in data collection in Natural Language Processing (NLP), and the ubiquity of social networks. Such data often has no natural numerical representation, and is typically described in terms relational expressions or as pairwise similarities. It turns out that finding numerical representations of such data in “Hyperbolic” spaces—rather than into the more familiar Euclidean spaces—is a more effective way to preserve valuable relational information.
Analyze data from one or more of the following Library Applications/Systems and create visualizations that highlight the most important findings related to our goal of supporting self-directed learning.
The Federal Communications Commission (FCC) and the Census regularly publish data on U.S. Internet availability, performance and use, at granularities from census block to county and state. The project goal is to answer questions based on the available data, such as “How reliable is Internet access?”, “Who is deploying fiber where?”, “Can we predict reliability of different technologies?”, “Can we predict the deployment of fiber?”
The ocean has absorbed the equivalent of 41% of industrial-age fossil carbon emissions. In the future, this rate of this ocean carbon sink will determine how much of mankind’s emissions remain in the atmosphere and drive climate change. To quantify the ocean carbon sink, surface ocean pCO2 must be known, but cannot be measured from satellite; instead it requires direct sampling across the vast and dangerous oceans. Thus, there will never be enough observations to directly estimate the carbon sink as it evolves. Data science can fill this gap by offering robust approaches to extrapolate from sparse observations to full coverage fields given auxiliary data that can be measured remotely.
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.
A common challenge for students in heavy proof-based courses is to come up with a long sequence of logical arguments from the problem statement to the final solution. In doing so, they can often skip steps leading to logical leaps or downright incorrect solutions. Ideally the instructor should identify these mis-steps and help students master such proof-based course material. Here we want to take a data-driven approach to address this challenge.
