The SAS Global Forum Student Symposium is an opportunity of teams of two to four post-secondary students and a faculty advisor (professor) to showcase their skills and compete with other teams in the application of SAS Analytics in big data.
Below is a list of the top eight teams who made the finals based on their submitted paper. These eight teams virtually presented their paper to a panel of judges, and the top three teams were recognized as winners. I want to personally thank all the students as they didn’t hesitate to submit their virtual presentations while still completing their coursework during this pandemic. They are truly amazing, and their papers and presentations provide a variety of interesting subjects and SAS topics.
1st Place Winner
Unsupervised Contextual Clustering of Abstracts: Jacob Noble & Himanshu Gamit from the University of St Thomas, St Paul delves into their study that utilizes publicly available data from the National Science Foundation (NSF) Web Application Programming Interface (API). In this paper, various machine learning techniques are demonstrated to explore, analyze and recommend similar proposal abstracts to aid the NSF or Awardee with the Merit Review Process. To perform text analysis, SAS® University Edition is used which supports SASPy, SAS® Studio and Python JupyterLab. Gensim Doc2vec is used to generate document vectors for proposal abstracts. Afterwards, document vectors were used to cluster similar abstracts using SAS® Studio KMeans Clustering Module. For visualization, the abstract embeddings were reduced to two dimensions using Principal Component Analysis (PCA) within SAS® Studio. This was then compared to a t-Distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction technique as part of the Scikit-learn machine learning toolkit for Python.
SAS Users Channel (video)
2nd Place Winner
Reducing Ecological Footprint: Examining Panel Data: Neil Belford, Jordan Humes, Manasi Murde, Bruce Rehburg of Oklahoma State University. In their paper, worldwide panel data is explored to develop a model that can be used to find characteristics that are associated with a nation’s ecological impact, as measured by Ecological Footprint Accounting. Through panel regression in SAS®, several factors are found that are associated with a reduction in Ecological Footprint among OECD countries after controlling for GDP and population size. These findings point to general policy objectives that can be used by industrialized and industrializing countries to reduce their ecological impact.
SAS Users Channel (video)
3rd Place Winner
The Plight of the Honeybee: Rachael Bishop, DaMarkus Green, and Karin Kolb of Kennesaw State University explores the effects of these pesticides on honeybees by comparing annual honey production yields and honeybee colony counts in the United States from 1995 through 2015. A decline in the honeybee population is raising concerns worldwide. Honeybees are an important part of agricultural industries. Is it possible that the neonicotinoid pesticides used to protect crops from damaging insects are also harming the insects necessary for pollinating the same crops?
SAS Users Channel (video)
Finalists
Analyzing the Factors Impacting Suicidal Behavior in American Youth: Ashlesha Sampathi, Mounica Mandapati, Nitesh Maruthi, and Venkat Ram Reddy from Oklahoma State University closely examines the 2017 Youth Risk Behavior Survey (YRBS), in order to understand characteristics of teenagers who attempted suicide. Latent Class Analysis using SAS® Enterprise Guide® found that among teens who attempted suicide, three distinct classes were evident which were characterized mainly by sexual assault, bullying, and depression respectively. Moreover, using PROC SURVEYLOGISTIC, it was seen that the odds of attempting suicide were four times higher for teens who were sexually assaulted and three times higher for teens who were bullied or abused drugs. The outcome of the paper highlights the importance of early intervention in preventing teenagers from slipping down a “rabbit hole” of risky behaviors that ultimately lead them to take their own lives.
SAS Users Channel (video)
Determining College Student Success: Carl Palombaro, Kayla Barrier, and Katherine Floyd, from UNC Wilmington discusses how student loan debt is an extremely hot topic in the media and is continuously on the rise, totaling over 1.5 trillion dollars in the United States alone. The overall goal of this project was to determine a student’s best financial option based on the degree they intend to seek, the university they attend, and the average salary per degree after graduation. In order to narrow the scope, this idea was applied to schools in North Carolina within the UNC public school system. The choice to exclude private universities in North Carolina was because many tuition rates at private institutions exceed the amount of funding typically available to a student through federal loans.
SAS Users Channel (video)
Economic Impacts of Sea Level Rise on Coastal Real Estate: Austin Willoughby, Emma Delehanty, and Victoria Mullin University of North Carolina at Wilmington. Their study models the potential loss of value coastal properties in New Hanover County, North Carolina will experience due to sea level rise. The methods used were linear regression to predict the future value of properties in addition to methods used in ArcGIS in order to find the parcels affected by different increments of sea level rise. This analysis could be used to inform coastal residents on the potential loss in value of their property and what to expect in future years.
SAS Users Channel (video)
Hate Speech Classification of social media posts using Text Analysis and Machine Learning: Venkateshwarlu Konduri, Sarada Padathula, Asish Pamu and Sravani Sigadam of Oklahoma State University focuses on how SAS® Enterprise Miner’s Text Analytics was used to develop a model that categorizes tweets based on their content, specifically hateful vs normal. After sampling and cleaning of the data and breaking the tweets down into quantifiable components, different models were built and compared. The best performing model was used to score unseen data, achieving reasonable accuracy in classification. Their paper touches upon how text analytics could be harnessed by organizations like Twitter for encouraging civic responsibility in its users. By providing a feature at the user-level which allows tweets to be labelled as a particular category as they are typed, the users might be given an opportunity to review and possibly modify any hateful tweets before posting them.
SAS Users Channel (video)
What Happens After Police Shootings? Aravind Dhanabal, Mason Kopasz, and Alex Lindsay of Oklahoma State University explores the possibility of underlying biases and prejudices that are leading to officers not being held accountable. To accomplish this, police shooting data and the results of the follow-on investigations were examined. The goal was to identify if there is a predictive model to evaluate if a police officer will face Grand Jury Indictment based on the attributes within the dataset. The police disposition (which specifies if an officer faced a Grand Jury Indictment or not) was used as the target variable.
SAS Users Channel (video)
