2015
SAS
®
Student
Ambassadors
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The SAS Student Ambassador program is a competitive
program that recognizes and supports students using
SAS technologies in innovative ways that benefit their
respective industries and fields of study.
The following SAS Student Ambassadors will present
their projects at SAS Global Forum 2015 in Dallas, TX,
April 26-29.
Ashley Collinsworth
Collinsworth is currently pursuing a doctorate of science in health systems research at Tulane University’s School of Public Health and Tropical Medicine. She earned a bachelor’s degree from Texas A&M University and a master’s of public health from the University of North Carolina. Collinsworth currently serves as the Director of Health Care Research for Baylor Scott and White Health’s Center for Clinical
Effectiveness. She has participated in several federally funded projects and multisite collaborations pertaining to health care quality improvement, patient safety, diabetes management, delirium prevention and use of health information technology. She will present her work on the use of electronic health records for health services research. This work is based on a study of delirium prevention and mitigation in intensive care units funded by the Agency for Healthcare Research and Quality, and it highlights the analyses she performed in SAS as part of her dissertation to examine the effectiveness and cost-effectiveness of the ABCDE (Awakening and Breathing Coordination, Delirium monitoring/management, and Early exercise/mobility) bundle.
Lauren Cook
Cook is currently a high school senior in the information technology academy at Phillip O. Berry Academy of Technology. She was first introduced to SAS during her sophomore year when she took a SAS Programming 1 class. Over the summer
(NCWIT). Cook will present her research project focused on the Charlotte Bobcats and how the dwindling fan base would be affected if the team changed names.
Mihaela Ene
Ene has a bachelor’s and a master’s degree in educational psychology and is currently pursuing a doctorate in educational psychology, research and measurement at University of South Carolina (USC). She is working as a Research Associate in the South Carolina Educational Policy Center at USC, focusing on projects related to school climate, summer learning loss and various magnet programs. Her research interests also include applications of multilevel modeling and latent variable modeling in various educational contexts. Ene is currently part of a research team interested in reframing models that are commonly conceptualized in other frameworks (e.g., Rasch). She will present, on behalf of the team, a paper on Multilevel Models for Categorical Data Using SAS PROC GLIMMIX: The Basics. It will cover the key elements in estimating two-level models with both dichotomous and polytomous outcomes via PROC GLIMMIX and will include step-by-step examples of modeling, estimation and interpretation.
Betty Johanna Garzon Rozo
Garzon Rozo is a PhD student in the Management Science group of the University of Edinburgh Business School.
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assess, in a multivariate way, the asymmetry and extreme dependence between severities via skew t-copula. It also seeks to provide a more efficient allocation of capital charges and to enhance the understanding of the new techniques for modeling operational value at risk (OpVaR). Garzon Rozo has a bachelor’s and master’s degree in industrial engineering from Distrital University in Colombia. She graduated as the top student of her class and received a full scholarship for her master’s study. She is also a two-time recipient of the Meritorious Thesis award. She will describe specifically the construction of severity models using the procedure PROC SEVERITY and PROC NLMIXED in SAS® 9.2. This construction implements several
parametric distributions and an alternative mix distribution via extreme value theory using SAS/STAT®.
Ila Gokarn
Gokarn is currently pursuing a bachelor’s degree in information systems with a specialization in business intelligence and analytics at Singapore Management University. She was named a SAS Scholar in 2013 and interned at SAS Singapore in 2014. During her internship, she worked on different fraud and security analytics projects, and a research project under the mentorship of Clifton Phua, former Fraud and Security Analytics Lead at SAS. Using fraud as a case study, she will be presenting her findings revolving around domain-agnostic experiments written in Base SAS 9.4. These experiments evaluate different ways of performing feature extraction from textual data using hash objects and information retrieval concepts. Her current research extends feature extraction to aspect-based sentiment mining.
Lisa Henley
Henley is a PhD candidate at the University of Canterbury in New Zealand, currently completing her research on the
quantification and visualization of human flourishing. She received a bachelor’s degree and postgraduate diploma with distinction in statistics. Henley was first introduced to SAS while working for a consulting firm in Sydney and since then has held a number of analytical and statistical roles involving SAS in New Zealand and the UK. She enjoys developing code to investigate data, particularly using new techniques and approaches in that context. She will share the part of her PhD research, which involves using genetic algorithms to trim large data sets.
Gibson Ikoro
Ikoro is a PhD student in the School of Electronic Engineering and Computer Science at Queen Mary University of London. After finishing a bachelor’s degree in mathematics, he received an MSc in computing science at the University of East Anglia. Ikoro was first introduced to SAS in his undergraduate courses, and during his master’s study he gained invaluable experience in data mining techniques and developing statistical models such as logistics regression and decision tree and support vector regression methods in SAS Enterprise Miner 12.1 and SAS/STAT®. Using a real database containing information (symptoms) that are less likely or unknown to have association with breast cancer, he
developed a model that can be used to predict breast cancer at an early stage and assist inexperienced oncologists to save time in disease diagnosis. Ikoro is a SAS Certified Clinical
Trials Programmer Using SAS® 9, SAS Certified Statistical Business Analyst Using SAS® 9: Regression and Modeling, and SAS Certified Base Programmer for SAS® 9.
Ramcharan Kakarla
Kakarla received his bachelor’s degree in electrical engineering from Sastra University. He graduated this past December with a master’s degree in management information systems from Oklahoma State University. He recently started his career as a senior data science analyst with Comcast. His research area is in fundraising analytics for nonprofit organizations. Kakarla was first introduced to SAS as part of his master’s course work and has used SAS extensively for class projects. He is an active member in the Analytics Conference Series. He has earned the following certifications: SAS Certified Predictive Modeler Using SAS Enterprise Miner 7, SAS Certified Statistical Business Analyst Using SAS® 9: Regression and Modeling, and SAS Certified Advanced Programmer for SAS® 9. He has also earned the SAS and OSU Data Mining Certificate.
Catherine LaChapelle
LaChapelle is a senior political science and women’s and gender studies double major with a minor in mathematical decision sciences at the University of North Carolina at Chapel Hill. She has spent three summers as a research and
incarceration using SAS Text Analytics to analyze the data. After she graduates from UNC Chapel Hill in May 2015, she will begin a graduate program at the Institute for Advanced Analytics at North Carolina State University.
Isabel Litton
Litton is currently a fourth-year student at California Polytechnic State
University, San Luis Obispo pursuing a bachelor’s degree in statistics and a minor in mathematics. She will pursue a master’s degree in analytics from the University of San Francisco in hopes of starting a career in data science. She first fell in love with SAS during an introductory class in her sophomore year at Cal Poly, and has used the program ever since for all her research projects. Her primary areas of interest are data mining, text mining and sentiment analysis. She will present an
automated approach to download JSON formatted tweets.
Juan Ma
Ma is currently a PhD candidate in the School of Industrial Engineering and Management at Oklahoma State University. She received her bachelor’s degree in logistics systems
engineering from Huazhong University of Science and Technology. Her PhD dissertation is on the problems of resilient network design under
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Miner™ 6.1, SAS Certified Base Programmer for SAS® 9 and
SAS Certified Advanced Programmer for SAS® 9. Ma will present the paper, Statistical Evaluation of the Doughnut Clustering Method for Product Affinity Segmentation, which is a joint work with Darius Baer and Goutam Chakraborty.
Balamurugan Mohan
Mohan’s passion toward analytics led him to join the operations team as an
analytical consultant for a Fortune Global 500® company where he was introduced to SAS. He later pursued a master’s degree in management information systems at Oklahoma State University. He has earned the following credentials: SAS Certified Predictive Modeler Using SAS Enterprise Miner 7, SAS Certified Statistical Business Analyst Using SAS® 9: Regression and Modeling, SAS Certified Advanced Programmer for SAS® 9, and SAS Certified Base Programmer for SAS® 9. His internship with OG&E Energy gave him hands-on experience in working with large data sets and deriving useful business insights from the same. He also received the SAS and OSU Predictive Analytics certificate in December 2014. His research is focused toward modeling the risk associated with transformers, which is a joint work with Dr. Goutam Chakraborty. He continues to believe that making sense of huge, unstructured data matters a lot.
Jun Neoh
Neoh is a PhD student in management science at the Centre for Risk Research in the Southampton Business School at Southampton University. Working alongside a local transportation planner, his PhD research aims to influence commuters to travel in environmentally friendly ways by using quantitative and operational research methods such as structural equation modeling, multidimensional scaling and system dynamics. He was first introduced to SAS by his supervisor, Maxwell Chipulu, and he has been using SAS to conduct his research ever since. He is also the lab tutor at his university for the Data Analysis and Modeling With SAS course for master’s students. Prior to his PhD, Neoh earned an MSc with distinction in risk management from Southampton University. He was also awarded the Vice-Chancellor and Faculty Scholarship for his PhD studies.
Narmada Panneerselvam
Panneerselvam is a master’s student in management information systems at Oklahoma State University. She received her bachelor’s in electrical and
electronics engineering from Anna University, India. She started her career as a mainframe developer in a multinational IT company. During her master’s degree, she has been using various SAS tools to solve different academic projects and research problems. She earned the SAS Certified Predictive Modeler Using SAS Enterprise Miner 7, SAS Certified Statistical Business Analyst Using SAS® 9: Regression and Modeling and SAS Certified Base Programmer for SAS® 9 credentials. She has also earned the SAS and OSU Data Mining certificate. During her final
semester, she has been working as a professional intern with Disney, and it serves as the next step to learn and experiment various statistical sophistications of SAS in real world
scenarios. She is passionate about playing with large amounts of data, finding meaningful insights, and making a difference in the way businesses resolve problems. She will present her work on one of the high-performance modeling techniques, random forest, by building an income level prediction using SAS Enterprise Miner.
Deanna Schreiber-Gregory
Schreiber-Gregory graduated from North Dakota State University with bachelor’s degrees in psychology and statistics. She is currently pursuing a Master of Science in the Health and Life Science Analytics Program at National University. Schreiber-Gregory will present her work and research on latent analyses. Her interest in this area started through her work as a student researcher at the Neuropsychiatric Research Institute. Through her work at the institute and collaboration with professionals in the eating disorder research field, Schreiber-Gregory discovered the power and insight that latent analyses lend to this very complex group of disorders. Schreiber-Gregory has gained an in-depth understanding of the degree to which latent analysis can help explain even the most complex questions. Schreiber-Gregory used Base SAS 9.3 for the paper that she will present.
SAS
®Student Ambassador Program
Present your work at SAS
®Global Forum
SAS® Student Ambassador Benefits
• Expenses paid to attend and present at SAS Global Forum.
• Receive recognition of your knowledge and skills before an international SAS audience.
• Network and interact with SAS users from every industry and sector.
• Become one of a select number of students around the world to earn the title of SAS Student Ambassador – a true résumé or CV differentiator!
SAS
®Student Ambassador
Application Process
This is a two-part application process:
• Submit an abstract and a working draft of your project to SAS Global Forum. (The call for papers generally opens in the summer, and the deadline to submit is usually in mid-September.)
• Complete the online SAS Student Ambassador Program Application.
After completing the application process, SAS Global Forum leaders select the papers that merit acceptance for presentation. The SAS Student Ambassador Program then
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®
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