An Introduction to Adobe InDesign

There is so much you can do with InDesign. In this introductory workshop, you will learn more about the program's interface and tools in order to prepare you for future utilizations, whether you are planning on making flyers, digital books, or other graphic design elements such as business cards and more. Follow along in this interactive setting that allows you to ask live questions to an experienced instructor.

Power Lunch: Ben Catt

**This event is for current Duke students only**

Duke students are invited to join a Virtual Power Lunch featuring Ben Catt, CEO of Pine Gate Renewables. Event capacity will be limited so that all attendees have the chance to introduce themselves, ask questions, and exchange ideas, just like at an in-person Power Lunch. Advance registration required. Attendees will be able to access the meeting information in the "Digital Links" section of Eventbrite after registration is complete.

Register now: https://bit.ly/jan28lunch

Data Dialogue with Christopher Ratto and Marisa Hughes (JHU-APL): System Integration with Multiscale Networks (SIMoN): A Modular Framework for Resource Management Models

Although the scientific community has proposed numerous models of Earth and human systems, there are few tools available that support the model coupling that is necessary to capture their complex interrelationships and promote further research cooperation. To address this challenge, we propose System Integration with Multiscale Networks (SIMoN), an open source modeling framework with a novel methodology for supporting heterogeneous geospatial regions.

China’s Evolving Innovation Trajectory: From Tech Laggard to Tech Powerhouse

Over the last few years, impressions regarding China's technology capabilities have shifted in a dramatic fashion. As recently as 3-4 years ago, China was seen as a technological laggard, far behind the West in most major categories. The Chinese S&T system faced numerous problems regarding inefficient funding allocation, ineffective use of talent, and mismanagement of intellectual property. There seemed to be an ample cushion between China and the West insofar as the dynamics of global competition were concerned.

AI Health

Introduction: Michael Pencina, Vice Dean for Data Science and Information Technology

Minimax Pareto Fairness and Subgroup Robustness
Speaker: Guillermo Sapiro, James B. Duke Distinguished Professor of Electrical and Computer Engineering, Professor of Computer Science and Mathematics, and Microsoft Fellow

Inter/Intra Subject Brain to Brain Signal Transfer from Robust Deep Networks
Speaker: Vahid Tarokh, Rhodes Family Distinguished Professor of Electrical and Computer Engineering, Professor of Mathematics, and Microsoft Fellow

Decoding and Programming the Human Genome with CRISPR Technologies

Introduction: Gregory E. Crawford, Professor of Pediatrics and Associate Professor of Molecular Genetics and Microbiology

Speaker: Charles A. Gersbach, Rooney Family Associate Professor of Biomedical Engineering and Associate Professor of Surgery, Orthopaedic Surgery, and Cell Biology

This webinar is one of the many events during Duke Research Week 2021. Register to join at dukeresearchweek.vfairs.com.

Please contact the Office of Research at research-office@duke.edu with any questions or concerns.

The Duke Quantum Center

Quantum Computers for Research
Speaker: Christopher R. Monroe, Professor of Electrical & Computer Engineering and Physics

Building Next Generation Quantum Computers
Speaker: Jungsang Kim, Professor of Electrical & Computer Engineering

This webinar is one of the many events during Duke Research Week 2021. Register to join at dukeresearchweek.vfairs.com.

Two $100 vouchers will be awarded to participating attendees of this lunchtime keynote. Recipients will be chosen at random.

"Assessing the reliability of machine learning models in predicting properties of solid-state materials "

Advances in machine learning (ML) are making a large impact in many disciplines, including materials and computational chemistry. A particularly exciting application of ML is the prediction of quantum mechanical (QM) properties (e.g., formation energy, bandgap, etc.) using only the composition or structure as input. Assuming sufficient accuracies in the ML models, these methods enable screening of a considerably large chemical space at orders of magnitude lower computational cost than QM methods.