Nov. 7, 2017
By Hana Maeda
Working in teams can bring many unexpected challenges, but one professor is looking into making that process a little easier. As part of an ongoing research project, School of Information Studies (iSchool) Associate Professor Jeffrey Saltz is continuing experiments on how teams can best work together on data science projects. Saltz is testing these experiments in his data science course, Applied Data Science, where he has teams work on different process methodologies each semester. This semester, Saltz is working on applying the Kanban methodology, a type of agile technique, to analyze how well teams work in a data science environment. And Saltz’s initial experiments suggests that Kanban “is one of the better project methodologies for data science teams.”
Saltz was inspired to take on this project from his own experiences working in data science teams for 20 years. He said that teams “would always struggle with getting all the different stakeholders on the same page and communicating and coordinating a large, diverse group of people.” For Saltz, coming to Syracuse University was the opportunity to address this challenge.
This project, Saltz said, is important because up until recently, most data scientists have worked individually. Being a ...
Dec. 17, 2016
The Center for Computational and Data Sciences in the ISchool hosted the following talk:
Title: Deep Learning for NLP
Speaker: Dr. Nancy McCracken
Date: Wednesday, December 14th
Time: 3:00 PM - 4:00 PM
Location: Katzer Room (Hinds Hall)
Recorded presentation: Click here
Presentation slides: Click here
Description: In this talk, I introduce Deep Learning as a technique in machine learning that automatically learns classification features. Deep learning tasks will be surveyed, including those for NLP. The main focus of the talk will be on the NLP task of learning dense word similarity vectors, such as those popularized by the word2vec software. We will show examples with both word2vec (the gensim implementation) and Deep Tensor software for this task in python.
Bio: Dr. McCracken is a Research Associate Professor in the iSchool. Her general research interests are in applying the principles and tools of computational linguistics to making information accessible and understandable for people. Recent research projects include an NSF funded project for using natural language processing and machine learning to assist social scientists in content analysis of text, an NSF funded project for building a qualitative data repository and working with the campaign research project group in using social ...
Sept. 17, 2016
By Jerry Robinson
As one of the iSchool's newest data science hires, Daniel Acuna has an affiliation with the Center for Computational and Data Science (CCDS). Acuna is an Assistant Professor at the Syracuse University School of Information Studies. He earned his Ph.D. in Computer Science from the University of Minnesota, Twin Cities, and completed a postdoc at the Rehabilitation Institute of Chicago and Northwestern University before joining the iSchool this fall.
Acuna's work crosses multiple disciplines including cognitive science, computer science, machine learning, and neuroscience. Currently, he most identifies with the relatively new but growing field of Science of Science.
Acuna research and tools help scientists and funding agencies to make high-quality decisions around hiring, promotion, literature search, and expertise matching. He believes his work is possible because of data science: "Data science is a fundamental area that allows me to enter a number of fields and use data to answer critical questions in those fields."
Acuna regularly relies on his background knowledge to design useful computational tools that help scientists and funders make sense of data and answer important questions in their specific domain. For instance, cognitive science helps him to understand how uncertainty factors ...
June 13, 2016
By Amanda Quick
Jeff Hemsley is an Assistant Professor at the School of Information Studies at Syracuse University. He believes that the Center for Computational and Data Sciences (CCDS) is a key resource for students who need research experience as well as for the public who wants detailed insights about the upcoming presidential election.
The CCDS isn’t your average research lab – from PhD candidates who specialize in natural language processing to masters students in Library and Information Science, the CCDS brings together students, faculty and industry experts to collect, interpret and analyze data as well as solve social problems with interactional datasets.
Over the past few years, one of the largest projects Center for Computational and Data Sciences has been “Illuminating 2016,” a project that collects social media data from all of the 2016 Presidential candidates using a complex algorithm. With the Presidential Election coming up in November, Hemsley expects the CCDS to obtain more public exposure from Illuminating 2016.
“When it [Illuminating 2016] comes online, it’s a platform we can make available to the public and will give us a higher profile,” said Hemsley.
In addition to national recognition, Hemsley hopes that the CCDS continues to grow ...
June 13, 2016
By Amanda Quick
Jeff Saltz is an Associate Professor at the School of Information Studies (iSchool) at Syracuse University. With over 20 years of experience in the data science and analytics field, Saltz reflects on the future of the industry, how students can benefit from the Center for Computational and Data Sciences (CCDS) and the growth of the data science program at the iSchool.
Jeff Saltz has seen hundreds of students come and go from the iSchool since he first became a professor in 2014. While not all of his students chose to become data scientists, he believes that all everyone should obtain the skills to become “data literate.”
Along with a variety of data science courses at the iSchool, Saltz believes the CCDS gives students – undergraduate, master's, and Ph.D. – the opportunity to work with large datasets, get involved with data science projects and learn how to create detailed analyses based on insights and results.
“They [students] will be able to think more strategically about analyzing and using data,” said Saltz about getting involved with the CCDS or in the classroom. “Even if someone is not going to be a data scientist, they need to know how to ...
June 6, 2016
Hedge is a research staff member at the Center for Computational and Data Science (CCDS). He reflects on how his education and experience as a software engineer at Alcatel Lucent have given him the chance to better understand, engage and analyze different datasets and projects.
By Amanda Quick
Yatish Hedge is no stranger to working with large datasets. Since his first day on the job four years ago, Hegde has had the opportunity to work on various data-related projects while engaging with faculty from both the NLP and data science departments in the iSchool.
“One of the main opportunities is working with data,” said Hegde of his experience so far at the CCDS. “Students learn how to correct, analyze and collect data. The data is already here and they can work with all real datasets.”
Hegde graduated from the iSchool at Syracuse University with a master’s degree in Information Management in 2010. He also holds an undergraduate degree in Computer Science from R.V College of Engineering, according to his bio. Hegde’s education and work experience as a software engineer have prepared him to become a key research member of the CCDS.
“My role in the Center is ...