“As a School of Engineering and Applied Sciences, we take immense pride in the profound impact our faculty have on society, and we remain committed to fostering an environment that cultivates innovation, collaboration and societal responsibility.” “I am extremely proud of the research that our early career faculty are leading,” says Kemper Lewis, dean and professor of the School of Engineering and Applied Sciences. In total, they will receive nearly $2.9 million in funding for their respective projects, which include both research on pressing societal problems and outreach to diverse communities. Erdem Sariyuce and Ziming Zhao, assistant professors in the Department of Computer Science and Engineering Prathima Nalam, assistant professor in the Department Materials Design and Innovation and Sangwoo Shin, assistant professor in the Department of Mechanical and Aerospace Engineering, are recipients of one of the most prestigious honors for early-career scientists and engineers. She has published more than 30 papers in refereed journals and conferences.Mingchen Gao, A. Her thesis work was supported by IBM PhD fellowship and lead to a well-received tutorial at SDM’10 conference. She obtained her PhD degree in Computer Science from University of Illinois at Urbana-Champaign in 2011. She is broadly interested in data and information analysis with a focus on information integration, ensemble methods, transfer learning, anomaly detection and mining data streams. Jing Gao is an assistant professor in the Computer Science and Engineering Department at the University at Buffalo, The State University of New York. I will show the effectiveness of these general learning techniques with a few sample applications in social media, networking, cyber security and bioinformatics. Second, we developed approaches based on matrix factorization and spectral embedding to detect objects performing inconsistently across multiple sources as a new type of anomalies. First, for knowledge integration, we proposed a graph-based consensus maximization framework to combine multiple supervised and unsupervised models, which significantly improves classification accuracy. I will present two perspectives of learning from multiple sources, i.e., exploring their similarities (knowledge integration) or their differences (inconsistency detection). In this talk, I will discuss our research on exploring the power of multiple heterogeneous information sources in challenging learning scenarios. Meanwhile, users provide limited feedback, have growing privacy concerns, and ask for actionable knowledge. Although many algorithms have been developed to analyze multiple information sources, real applications continuously pose new challenges: Data can be gigantic, noisy, unreliable, dynamically evolving, highly imbalanced, and heterogeneous. Many interesting patterns cannot be extracted from a single data collection, but have to be discovered from the integrative analysis of all heterogeneous data sources available. Useful knowledge is usually buried in multiple genres of data, which are from different sources, in different formats, and with different types of representation. Nowadays, a vast ocean of data are collected from trillions of connected devices everyday. Exploring the Power of Heterogeneous Information Sources
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