Deep Learning and the Analysis of Time Series Data

Dallas AI
Dallas AI
Public group
Location image of event venue


Agenda:[masked]pm: Networking & food[masked]pm: Main talk (Speaker: Dr. Bivin Sadler, Associate professor SMU MSDS Program)

We are all familiar with the 4 Vs of Big Data: Velocity, Veracity, Volume and Variety. With respect to the fourth ‘V’, ‘Variety’, various unstructured data types such as text, image and video data have gained quite a bit of attention lately and continue to gain momentum. However, there is a type of structured data that has maintained its intrigue and importance in both business and academia … Time Series Data. While it is not hard to find applications of Deep Learning methods to text, image and video data, they have also shown to have great promise in the analysis of data collected over time. In this session, we will investigate the theory, implementation and performance of recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) in the context of forecasting and predictive analytics. In addition, we will compare these deep learning methods with the more traditional but widely used ARMA and ARIMA type models.

Join us for a fun and informative discussion on a topic that is showing great promise in an area critical to science, business and industry: Deep Learning and the Analysis of Time Series Data!

About the Speaker:
Originally from Dallas Texas, Dr. Bivin Sadler finished a BS in mathematics magna cum laude from Texas Tech University before beginning his professional career in Scottsdale, Arizona, at Motorola. He worked as a statistician and software engineer for 2.5 years, working primarily on a companywide tool to predict when software projects could be released with optimal statistical properties (Six Sigma). Upon completion of the project, he moved to San Diego, and while playing professional beach volleyball for two years, finished a master’s degree in applied math at San Diego State University. He then moved back to Dallas to earn a PhD in statistics from SMU and finished his degree in 2014 after winning the Walsh Award for the top score on the qualifying exam taken after the third year of coursework.

Dr. Sadler was hired as part of the faculty at SMU after graduation and began a dual appointment teaching both undergraduate and graduate classes in the statistics department and online with the recently formed Master of Science in Data Science (MSDS) program. Academically, he has presented his work in item response theory at various conferences and is currently working on several domestic and international consulting projects. He became a full-time member of the MSDS faculty in August 2018 and, in addition to consulting projects and teaching, actively contributes towards developing new courses and enhancing existing ones at the SMU MSDS program.