Computational Sustainability aims to develop computational methods to help solve some of the key challenges concerning environmental, economic, and societal issues in order to help put us on a path towards a sustainable future. Recently, Gomes has become deeply immersed in research on scientific discovery for a sustainable future and more generally in research in the new field of Computational Sustainability. Her research area is Artificial Intelligence with a focus on large-scale constraint reasoning, optimization, and machine learning. in computer science in the area of artificial intelligence from the University of Edinburgh. Nielsen Professor of Computing and Information Science and the director of the Institute for Computational Sustainability at Cornell University. We show how such a strategy can outperform specialized solvers for Sokoban, a prototypical AI planning problem.īio: Carla Gomes is the Ronald C. Finally, I will also talk about the effectiveness of a novel curriculum learning with restarts strategy to boost a reinforcement learning framework. DRNets is part of a SARA, the Scientific Reasoning Agent for materials discovery. The article DRNets can solve Sudoku, speed scientific discovery, provides a perspective for a general audience about DRNets. For an intuitive demonstration of our approach, using a simpler domain, we also solve variants of the Sudoku problem. DRNets provide a general framework for integrating deep learning and reasoning for tackling challenging problems. DRNets reach super-human performance for crystal-structure phase mapping, a core, long-standing challenge in materials science, enabling the discovery of solar-fuels materials. DRNets requires only modest amounts of (unlabeled) data, in sharp contrast to standard deep learning approaches. In this work, we propose an approach called Deep Reasoning Networks (DRNets), which seamlessly integrates deep learning and reasoning via an interpretable latent space for incorporating prior knowledge. I will talk about our work on AI for accelerating the discovery for new solar fuels materials, which has been featured in Nature Machine Intelligence, in a cover article entitled, Automating crystal-structure phase mapping by combining deep learning with constraint reasoning. We amplify a few data examples with human intuitions and detailed reasoning from first principles and prior knowledge for discovery. However, often we only have access to small datasets and incomplete data. The tremendous AI progress that we have witnessed in the last decade has been largely driven by deep learning advances and heavily hinges on the availability of large, annotated datasets to supervise model training. AI systems are now performing at human and even superhuman levels on various tasks, such as image identification and face and speech recognition. Andre currently serves as an associate editor for the Network Optimization area at INFORMS Journal on Computing and in several senior roles in conferences such as AAAI, CP, and CPAIOR.Ībstract: Artificial Intelligence (AI) is a rapidly advancing field inspired by human intelligence. Thompson Doctoral Dissertation Award at Carnegie Mellon University. from Carnegie Mellon University in Operations Research in 2014, and has received the Research Excellence Award at the University of Toronto Scarborough, the INFORMS Computing Society Best Student Paper Award, and the Gerald L. His research focuses on both methodology and practice of optimization, specifically in mathematical programming and dynamic programming for scheduling, healthcare, and supply chain problems. The talk will highlight examples in routing, scheduling, and planning, while also emphasizing new applications and future research in the area.īio: Andre Augusto Cire is an Associate Professor in Operations Management and Analytics at the University of Toronto, cross-appointed between the Department of Management at the Scarborough campus and the Rotman School of Management. We will then leverage links with mathematical programming and polyhedral theory to propose stronger formulations, cutting-plane methods, and new decomposition approaches for difficult combinatorial and stochastic discrete problems. We will investigate the principles of DD modeling for combinatorial problems and develop the intrinsic connections between DDs and (approximate) dynamic programming. A DD, in our context, is a graph-based extended formulation of an optimization problem that exposes network structure, leading to novel bounding and branching mechanisms that complement classical model-based approaches. Abstract: In this talk we will discuss alternative solution techniques for discrete and stochastic optimization based on decision diagrams (DDs).
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |