Serena Wang, a doctoral student at the University of California, Berkeley, will deliver a talk as part of CSRAI's Young Achievers Symposium.
Christine Herlihy, a doctoral student at the University of Maryland, College Park, will deliver a talk as part of CSRAI's Young Achievers Symposium.
Alfonso Morales, Vilas Distinguished Achievement Professor and Chair of the Department of Planning and Landscape Architecture, and State Food Systems/Marketplaces Specialist, UW-Extension, will deliver a talk as part of CSRAI's AI for Social Impact Seminar Series.
Lily Xu, a doctoral candidate at Harvard University, will deliver a talk as part of CSRAI's Young Achievers Symposium.
Chinasa T. Okolo, a doctoral candidate in the Department of Computer Science at Cornell University, will deliver a talk as part of CSRAI's Young Achievers Symposium.
Chun Kai Ling, a doctoral student at Carnegie Mellon University, will deliver a talk as part of CSRAI's Young Achievers Symposium.
Varun Chandola, Program Manager at National Science Foundation, will present a Penn State AI Distinguished Talk, "Towards a Robust Artificial Intelligence Innovation Ecosystem." This event is part of the Penn State AI Hub Talks series, organized by the Penn State AI Hub.
Join Harsh Nisar, Lead Data Scientist with the Government of India's Ministry of Rural Development for an upcoming talk in the AI for Social Impact Seminar Series. This lecture is free and open to the Penn State community.
Umang Bhatt, a doctoral candidate in the Machine Learning Group at the University of Cambridge, will deliver a talk as part of CSRAI's Young Achievers Symposium.
Su Lin Blodgett, a postdoctoral researcher in the Fairness, Accountability, Transparency, and Ethics (FATE) group at Microsoft Research Montréal, will deliver a talk as part of CSRAI's Young Achievers Symposium.
Join Maria De-Arteaga, assistant professor in the McCombs School of Business at the University of Texas at Austin for an upcoming talk in the AI for Social Impact Seminar Series. This lecture is free and open to the Penn State community.
This event is hosted as part of the Penn State Center for Socially Responsible Artificial Intelligence's Distinguished Lecture Series in partnership with the College of Information Sciences and Technology. The series highlights world-renowned scholars of repute who have made fundamental contributions to the advancement of socially responsible artificial intelligence. The series aims to provoke attendees and participants to have thoughtful conversations and to facilitate discussion among students, faculty, and industry affiliates of the Center.
Ryan Shi, a doctoral candidate of societal computing in the School of Computer Science at Carnegie Mellon University and founder of 98Connect, will deliver “From a Bag of Bagels to Bandit Data-Driven Optimization” as part of CSRAI's Young Achievers Symposium.
Join Srijan Kumar, assistant professor at the College of Computing at Georgia Institute of Technology for an upcoming talk in the AI for Social Impact Seminar Series. This lecture is free and open to the Penn State community.
Praneeth Vepakomma, a doctoral student at MIT, will deliver a talk as part of CSRAI's Young Achievers Symposium.
Kai Wang, a doctoral candidate at Harvard University, will deliver a talk as part of CSRAI's Young Achievers Symposium.
Ana-Andreea Stoica, a doctoral candidate at Columbia University, will deliver “Diversity and Inequality in Social Networks” as part of CSRAI's Young Achievers Symposium.
Join Dashun Wang, Professor of Management and Organizations, Kellogg School of Management and McCormick School of Engineering at Northwestern University for an upcoming talk in the AI for Social Impact Seminar Series. This lecture is free and open to the Penn State community.
Sherry Tongshuang Wu, a final year doctoral candidate in computer science and engineering at the University of Washington, will deliver "Interactive AI Model Debugging and Correction" as part of CSRAI's Young Achievers Symposium.
CSRAI is currently inviting short 2-3 page proposals for its annual seed funding program. Applications will be accepted through Dec. 1, 2021, with projects expected to start in spring 2022 and last for up to two years.
“4 Reasons Why Social Media Make Us Vulnerable to Manipulation”
As social media become major channels for the diffusion of news and information, it becomes critical to understand how the complex interplay between cognitive, social, and algorithmic biases triggered by our reliance on online social networks makes us vulnerable to manipulation and disinformation. This talk overviews ongoing network analytics, modeling, and machine learning efforts to study the viral spread of misinformation and to develop tools for countering the online manipulation of opinions.
Join David G. Rand, Erwin H. Schell Professor and Professor of Management Science and Brain and Cognitive Sciences, MIT for an upcoming discussion in the Talk Series on Frauds and Fakes. This lecture is free and open to the Penn State community.
“Exploring the Power of Taxonomy and Embedding in Text Mining”
The real-world big data are largely dynamic, interconnected and unstructured text. It is highly desirable to transform such massive unstructured data into structured knowledge. Many researchers rely on labor-intensive labeling and curation to extract knowledge from text data. Such approaches, unfortunately, may not be scalable, especially when such texts are domain-specific and nonstandard (such as social media). We envision that massive text data itself may disclose a large body of hidden structures and knowledge. Equipped with domain-independent and domain-dependent knowledge-bases, we can explore the power of massive data to transform unstructured data into structured knowledge. In this talk, we introduce a set of methods developed recently in our group for such an exploration, including joint spherical text embedding, discriminative topic mining, taxonomy construction, and taxonomy-guided knowledge mining. We show that data-driven approach could be promising at transforming massive text data into structured knowledge.
“Network Heterogeneity on Graph Neural Networks”
Graph neural network (GNN), as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. Basically, the current GNNs follow the message-passing framework which receives messages from neighbors and applies neural network to learn node representations. However, previous GNNs mainly focus on homogeneous graph, while in reality, the real-world graphs usually are far from homogeneity. Here we first examine the various types of network heterogeneity, including node and link type heterogeneity, neighborhood heterogeneity, fragment heterogeneity, temporal heterogeneity, and structure heterogeneity. We then discuss the implications and methods to overcome these heterogeneities.
More than one year into the pandemic, the Penn State Center for Socially Responsible AI hosts world-class researchers, industry professionals, and non-profit organizations for a single day event in which we introspect and discuss on how AI research has responded in the face of COVID-19, how has it changed, and how is it likely to change in the coming future (and the role that AI researchers can expect to play in that change).