Imaging Science and NVIDIA Workshop: Artificial Intelligence/Fundamentals of Deep Learning Workshop

RIT Imaging Science & NVIDIA Workshop
Artificial Intelligence/Fundamentals of Deep Learning Workshop

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Event Details:

In this workshop, participants will learn the fundamentals of deep learning. Whether you're a practitioner or just starting your journey into the world of artificial intelligence, this workshop promises valuable insights and hands-on experiences that will deepen your understanding of deep learning principles.  The workshop is free for all participants from higher education institutions.

Lunch will be included.

Workshop Highlights (benefits to attending):

1. Gain a solid understanding of the foundational concepts of deep learning, including neural networks, activation functions, and gradient descent optimization.

2. Explore popular deep learning architectures such as convolutional neural networks (CNNs) for image processing, recurrent neural networks (RNNs) for sequential data analysis, and generative adversarial networks (GANs) for synthetic data generation.

3. Learn about key training and optimization techniques essential for training deep neural networks efficiently, including stochastic gradient descent, batch normalization, and regularization methods.

4. Get hands-on experience with cutting-edge NVIDIA GPUs and software tools, empowering you to accelerate your deep learning workflows and tackle complex tasks.

5. Discover best practices and tips for designing and training robust deep learning models, optimizing performance, and avoiding common pitfalls.

6. Engage with colleagues during interactive Q&A sessions and connect with fellow participants to share insights and experiences.

Speaker Bio:

Dr. Dimah Dera specializes in robust and trustworthy modern machine learning (ML) solutions for real-world applications, including healthcare, cybersecurity, remote sensing, and surveillance systems. In the rapidly evolving landscape of artificial intelligence (AI) and autonomous systems, the integration of ML techniques has paved the way for unprecedented advancements across various domains. The robustness, safety, and reliability of AI systems have emerged as pivotal requirements. The scope of her research includes developing innovative techniques to ensure the robustness, safety, and reliability of AI systems by integrating Bayesian theory and statistical signal processing foundations into modern ML frameworks. This research highlights the intricate connections between learning Bayesian uncertainty in ML models and their robustness and safety awareness to dynamically changing environments and systems failure. This research advances theoretical and algorithmic knowledge that will transcend traditional ML and AI systems toward safe and reliable deployment of AI models in high-risk real-world applications. Dimah received the National Science Foundation (NSF) Computer and Information Science and Engineering Research Initiation Initiative (CRII) in 2023 for her research focusing on robust machine learning and time-series analysis. She won multiple awards, such as the Best Paper Award at the 2019 IEEE International Workshop on Machine Learning for Signal Processing (MLSP’19) and the IEEE Philadelphia Sections Benjamin Franklin Key Award (2021). She publishes in the area of trustworthy, reliable, and expandable machine learning, signal and image processing and optimization.

Intended Audience:

RIT students, faculty, and staff. Anyone interested in the topic.