Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Hanghang Tong wants to help people as they go about their daily lives, do their jobs, interact with infrastructure and conduct research. Yet most of the people Tong's work benefits may never know it. Tong applies his expertise -- large-scale data mining and machine learning -- to research networks, including those involved in online social interactions, electrical power grids, infrastructure and transportation. On a smaller-scale example of networks, Tong focuses on graphs, which are people or other nodes that are linked together in varied and complex ways. Hanghang Tong, an assistant professor of computer science in Arizona State University's Ira A. Fulton Schools of Engineering, studies networks and how to improve them in a wide range of applications through the design of novel algorithms.

Hanghang Tong wants to help people as they go about their daily lives, do their jobs, interact with infrastructure and conduct research. Yet most of the people Tong's work benefits may never know it. Tong applies his expertise -- large-scale data mining and machine learning -- to research networks, including those involved in online social interactions, electrical power grids, infrastructure and transportation. On a smaller-scale example of networks, Tong focuses on graphs, which are people or other nodes that are linked together in varied and complex ways. Hanghang Tong, an assistant professor of computer science in Arizona State University's Ira A. Fulton Schools of Engineering, studies networks and how to improve them in a wide range of applications through the design of novel algorithms.

Scalable generative models that capture the rich and often nonlinear distribution of highdimensional data, (i.e., image, video, and audio), play a central role in various applications of machine learning, including transfer learning [14, 25], super-resolution [16, 21], image inpainting and completion [35], and image retrieval [7], among many others. The recent generative models, including Generative Adversarial Networks (GANs) [1, 2, 11, 30] and Variational Autoencoders (VAE) [5, 15, 24] enable an unsupervised and end-to-end modeling of the high-dimensional distribution of the training data. Learning such generative models boils down to minimizing a dissimilarity measure between the data distribution and the output distribution of the generative model. To this end, and following the work of Arjovsky et al. [1] and Bousquet et al. [5] we approach the problem of generative modeling from the optimal transport point of view. The optimal transport problem [18, 34] provides a way to measure the distances between probability distributions by transporting (i.e., morphing) one distribution into another.