NYU Data Science newsletter – June 26, 2015

GROUP CURATION: N/A

The course provides a deep excursion into cutting-edge research in deep learning applied to NLP. The final project will involve training a complex recurrent neural network and applying it to a large scale NLP problem. On the model side we will cover word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some very novel models involving a memory component. Through lectures and programming assignments students will learn the necessary engineering tricks for making neural networks work on practical problems. Taught by Richard Socher.

One of the fastest growing types of biological data is the As, Cs, Gs, and Ts of DNA sequencing — already in the tens of petabytes and on track to reach exabytes. With today’s high-throughput sequencing technology, it’s much easier to generate genomic data than to transform it into information or knowledge that can improve human health. That’s why Google Genomics and Broad Institute of MIT and Harvard are teaming up to combine the power, security, and scale of Google Cloud Platform with Broad Institute’s expertise in scientific analysis.

Videos of conference presentations now available.

The Spark Summit is an event to bring the Apache Spark community together. Spark Summit 2015 ran from on Monday, June 15 through Wednesday, June 17, 2015 at The Hilton Union Square in San Francisco. Attendees heard from leading production users of Spark, SparkSQL, Spark Streaming and related projects; they were able to find out where project development is headed; and learn how to use the Spark stack in a variety of applications.

Recommind announced on Wednesday that it has introduced a new tool, to be managed by Deloitte, that can organize and extract data from complex over-the-counter derivative legal agreements.

The new product, called the Perceptiv Derivatives Contract Analysis, utilizes the company’s machine learning technology to sift through the OTC agreements, identify key data points, such as termination points, bankruptcy contingencies and various others, and classify each agreement based on the language used.

Transparency, openness, and reproducibility are readily recognized as vital features of science. When asked, most scientists embrace these features as disciplinary norms and values. Therefore, one might expect that these valued features would be routine in daily practice. Yet, a growing body of evidence suggests that this is not the case.

A likely culprit for this disconnect is an academic reward system that does not sufficiently incentivize open practices. In the present reward system, emphasis on innovation may undermine practices that support verification

The NYCDH Steering Group are pleased to announce our second annual cross-institutional NYCDH digital humanities graduate student project award. We invite all graduate students in the city of New York to apply by August 1, 2015. First prize winner(s) will receive a cash prize of $1000. Two runner up positions will receive $500 each. All three winning proposals will have the opportunity to receive support from one or more of the many centers affiliated with NYCDH. Winners will also receive exposure on our site and through our social media outlets.

For more information about this award and how to apply: http://nycdh.org/nycdh-sgpa-2015/

Application Deadline: Saturday, August 1