Research Computing Presentation Program

  • 10:30 Qualtrics, Surveys are Conversations
  • 11:00 Mathworks, Deep Learning for Image Processing & Computer Vision
  • 11:30 Mathworks, Signal Processing and Machine Learning Techniques for Data Analytics
  • 12:00 RStudio, An amuse-bouche overview of R and RStudio
  • 12:30 SAS, An Overview of Data Mining with SAS Enterprise Miner
  • 1:00 JMP, JMP, Statistical Discovery Software from SAS
  • 1:30 Qualtrics, Surveys are Conversations


Surveys are Conversations

Mathworks MATLAB

Deep Learning for Image Processing & Computer Vision

Deep learning is a machine learning technique that can learn useful representations or features directly from data, images, text and sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance in object classification. In this session, we will discuss typical object detection and object recognition workflows using MATLAB & deep learning techniques.


  • Accessing and managing large sets of images
  • Leveraging the use of pre-trained networks for feature extraction
  • Using standard computer vision techniques to augment the use of deep learning
  • Speeding up the training process using GPUs and Parallel Computing Toolbox

Signal Processing and Machine Learning Techniques for Data Analytics

An increasing number of data processing applications require the joint use of signal processing and machine learning to extract meaning. MATLAB can accelerate the development of these systems by providing a full range of modeling and design capabilities. In this session we will introduce common signal processing methods (including digital filtering and frequency-domain analysis) that help extract descriptive features from raw waveforms. We will then then discuss how to explore and test different classification algorithms (such as decision trees, support vector machines, or neural networks) to model the system performance. Finally, we will show how to scale the modeling to large datasets.

Speaker Bio

Grant Martin has a B.S. and M.S. in Electrical Engineering from The University of New Mexico (UNM) specializing in image/signal processing and algorithm design. While pursuing his advanced degrees, Grant worked at Sandia National Laboratories developing real-time Synthetic Aperture Radar (SAR) imaging algorithms. Grant has also performed research in the area of medical imaging and taught signal processing with MATLAB at UNM. He joined the MathWorks in November 2005 and is currently a manager in the Application Engineering group. Grant and his team are responsible for the computer vision and image processing application areas as well as signal processing code generation applications with both C and HDL.


An amuse-bouche overview of R and RStudio

Whether you are a seasoned user of R and RStudio, a hobbyist, or someone who is just curious to hear about how RStudio stays in business, we will give you a quick overview and hopefully point you to where you can go to learn more.

Speaker Bio

Tareef Kawaf, the President of RStudio, is a software startup executive and a member of teams that built up ATG’s eCommerce offering and Brightcove’s Online Video Platform, helping both companies grow from early startups to publicly traded companies.  He joined RStudio in early 2013 to help define its commercial product strategy and build the team.  He is a software engineer by training, and an aspiring student of advanced analytics and R.


An Overview of Data Mining with SAS Enterprise Miner

This presentation introduces the powerful data mining tool, SAS Enterprise Miner. This advanced tool streamlines the data mining process and creates highly accurate predictive and descriptive models that are based on analysis of vast amounts of data. During this brief presentation, we will navigate through the point and click interface and introduce the skills required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and survival analysis).

Speaker Bio

Jaymie Shanahan obtained her Master of Science degree in Biostatistics, from the University of South Carolina. She earned her BA in Mathematics (Statistics Minor) from Elon University. Shanahan’s interests include public health, data mining, survival analysis, and SAS programming. She began her career at SAS Institute in 2012 as an Analytical Software Tester for the SAS/STAT product. She moved to an Analytical Consulting role where she was responsible for delivering solutions for various SAS customers interested in forecasting and optimizing revenue management. Now, as part of the Global Academic Program at SAS, Shanahan provides consulting around the country to universities, community colleges, and high schools by teaching faculty and students how to use SAS Software.


JMP, Statistical Discovery Software from SAS

What is JMP?  Desktop statistical software that is freely available to all BU faculty, students and researchers.  JMP includes comprehensive capabilities for statistical and graphical analysis of data for every academic field and most research needs. JMP is visual and interactive, and runs natively on Windows and Macintosh operating systems.  It is also an easy, point-and-click interface to SAS®, R, MATLAB and Excel.  Come and see what JMP can do for you!

Speaker Bio 

Mia L. Stephens is an Academic Ambassador with JMP (a division of SAS), responsible for providing technical support for JMP academic users.  Prior to joining SAS, Stephens was an adjunct professor of statistics at University of New Hampshire, a founding member of the North Haven Group and a trainer and consultant with the George Group. A co-author of four books and several papers, she has developed training materials, taught and consulted within a variety of fields and industries.