Virtual Symposium on

Harnessing Data Revolution
for Autonomous Memory Materials

Thursday, July 15, 2021 

1:00 - 5:00 pm EDT

Registration: Click here

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Synopsis

With the end of Moore’s and Kryder’s Laws, there is an urgent need for new computational and data storage technologies to meet the unending demands for more powerful computers and electronic devices. In this regard, as the most sophisticated computational machine in the universe, the brain has been a source of marvel and inspiration. Over the past decade, we have seen large amount of brain data being generated to understand its myriad functions and powerful deep learning algorithms being created to address complex problems in many disciplines. The confluence of these developments means that we are now at a tipping point for harnessing the data revolution in neuroscience through data-intensive strategies that would enable the creation of next-generation computing and memory materials, such as spintronics, photonics, phononics, nanoparticles, and many others, that embed brain functional architectures. The symposium brings together speakers from neuroscience, data science, and materials science with the goal of advancing convergence research across the different disciplines. 

Invited Speakers

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Agenda

1:00 PM               Welcome and Opening Remarks

 

Session 1: Neurobiology 

1:15 PM              Eun Jung Hwang (Rosalind Franklin U)

                            The Neural Basis of Long-term Motor Learning

1:45 PM              Sarah Muldoon (U. Buffalo)

                            Brain Network Structure in Health and Disease 

2:15 PM               Break

 

Session 2: Neuroscience Models to Data Science Models 

2:30 PM               Jeffrey Moehlis (UC Santa Barbara)

                            A Data-Driven, Machine Learning-Based Approach to Adaptive Deep Brain Stimulation 

3:00 PM               Raghu Machiraju (Ohio State)

                             Challenges of Creating Data-Driven Learning Frameworks in Materials

3:30 PM               Break

 

Session 3: Data Science Models to Materials

3:45 PM               Mark Bathe (MIT)

                            DNA-based Exascale Data Storage and Retrieval

4:15 PM               Rigoberto Hernandez (John Hopkins)

                             Networked Engineered Nanoparticles for Autonomous Computing

4:45 PM               Concluding Remarks