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Theoretical and Applied Data Science (TADS) Lunch-n-Learn
Midwest Big Data Summer School: Regular Registration Open
Midwest Big Data Summer School: Late Registration Open
Midwest Big Data Summer School
Theoretical and Applied Data Science (TADS) Lunch-n-Learn
Presenter: Dr. Wallapak Tavanapong
Topic: Dealing with class imbalance and a limited labeled dataset with active deep learning
Theoretical and Applied Data Science (TADS) Lunch-n-Learn
Theoretical and Applied Data Science (TADS) Lunch-n-Learn
Advances in Graph Learning and Inference
Graph-based data processing algorithms impact a variety of application domains ranging from transportation networks, artificial intelligence systems, cellphone networks, social networks, and the Web. Nevertheless, the emergent big-data era poses key conceptual challenges: several existing graph-based methods used in practice exhibit unreasonably high running time; several other methods operate in the absence of correctness guarantees. These challenges severely imperil the safety and reliability of higher-level decision-making systems of which they are a part.
A Physics-Aware Learning Framework for Microstructure Design
A key problem in computational material science deals with understanding the effect of material distribution (i.e., microstructure) on material performance. The challenge we consider here is to synthesize microstructures with desired physical and chemical properties, given a finite number of microstructure images, evaluated based on the physical invariances that the microstructure exhibits.