2019 MATLAB Academic Tour


During this event, you will learn about the new capabilities available in the MATLAB environment to address some of the major technical challenges in teaching and research.

In the first part of the presentation, you will discover several cloud services available to MATLAB users to enrich their teaching and learning anywhere and anytime, such as

  • how to gain instant access to the latest version of MATLAB on the cloud through your web browser
  • how to use interactive online trainings to foster your students’ MATLAB and Simulink skills
  • how to create your own auto-graded assignments to supplement students’ learning in engineering and science
  • how to share code with students and coworkers and data with your students
  • how to integrate mobile devices into your learning environment and exercises

The second part of the presentation will focus on practical aspects of the domain of deep learning and demonstrate new MATLAB features that simplify these tasks and eliminate the low-level programming. From prototype to production, we’ll build and train neural networks, and discuss automatically converting a model to CUDA to run natively on GPUs.

Some of the key highlights will include:

  • Performing pixel-level semantic segmentation on images
  • Importing and use pre-trained models from TensorFlow and Caffe
  • Speeding up network training with parallel computing on a cluster
  • Using data augmentation to increase the accuracy of a deep learning model
  • Automatically converting a model to CUDA to run on GPUs

About the Presenter

Stefano Olivieri received a Master’s Degree in Electrical Engineering at University of Bologna, Italy, in July 1995, and got a Post Graduate Advanced Degree in Information Technology at CEFRIEL, Polytechnic of Milan the same year.

He’s been with MathWorks since 2005. After spending eight years as a Senior Application Engineer in the field of Signal Processing and Communication Systems, supporting companies in the Communications, Electronics, Semiconductors and Aerospace and Defense industry segments, Stefano is currently working as a Customer Success Engineer to help the top universities with the adoption of MathWorks tools for effective teaching and research.

Before that, he worked with R&D labs in STMicroelectronics and Philips Research, were he dealt with the design and development of wireless communication and video processing systems.

Stefano has also been Contract Professor with the University of Milano for three years, where he was teaching Transmission Theory for the Telecommunication Software Engineering Bachelor’s Degree.

Loren Shure has worked at MathWorks for over 28 years. She has co-authored several MathWorks products in addition to adding core functionality to MATLAB, including major contributions to the design of the MATLAB language. She graduated from MIT with a B.Sc. in physics and has a Ph.D. in marine geophysics from the University of California, San Diego, Scripps Institution of Oceanography. Loren writes about MATLAB on her blog, The Art of MATLAB


Time Title
15 Minutes


  • Campus wide license
  • Portal
  • Certification
30 Minutes

Teaching and learning on the Cloud

  • MATLAB Online, Drive, Mobile, MAOTS, ThinkSpeak
  • MATLAB Grader
30 Minutes

Robotic assessment of muscular fatigue in children with neuromuscular diseases with MATLAB and Simulink - Francesca Marini, MathWorks

  • Project description and objectives 
  • Real time control of a rehabilitation robot
  • Data analysis and results
2 Hours

Demystifying Deep Learning with MATLAB

  • Manage large data sets (images, signals, text, etc.)
  • Create, analyze, and visualize networks, and gain insight into the black box nature of deep learning models
  • Automatically label ground truth or generate synthetic data
  • Build or edit deep learning models with a drag-and-drop interface
  • Perform classification, regression, and semantic segmentation with images or signals
  • Apply reinforcement learning with deep Q networks (DQN)
  • Leverage pre-trained models (e.g. GoogLeNet and ResNet) for transfer learning
  • Import models from Keras-TensorFlow, Caffe, and the ONNX Model format
  • Speed up network training with parallel computing on a cluster

Registration closed