UNIVERSITY PARK, Pa. — The College of Information Sciences and Technology was well-represented at the 2020 ACM Knowledge Discovery and Data Mining (KDD) Conference, held virtually Aug. 23-27.
The conference accepted 11 papers including Penn State researchers, 10 of which involve experts from the College of IST.
“Mining information from big data using data mining and machine learning is revolutionizing technology and society,” said Prasenjit Mitra, associate dean for research at the College of Information Sciences and Technology. “The variety of applications that College of IST researchers address, ranging from homeless youth, social media applications such as Snapchat, electronic health records to computer security, shows that our work impacts all aspects of our lives involving technology and beyond.”
Mitra added, “In the last five years, only about 18% of the papers submitted to this competitive premiere data mining conference on average were accepted, and having 10 papers accepted from Penn State with nine of them from researchers in the College of IST demonstrates that the University and the college are among the premier research organizations in the world in this area.”
Penn State research represented at the 2020 KDD Conference includes:
— “AdvMind: Inferring Adversary Intent of Black-Box Attacks,” by informatics doctoral students Ren Pang and Xinyang Zhang; and Ting Wang, assistant professor of information sciences and technology; along with collaborators from Zhejiang University and Hong Kong Polytechnic University.
— “Attackability Characterization of Adversarial Evasion Attack on Discrete Data,” by Fenglong Ma, assistant professor of information sciences and technology, along with collaborators from King Abdullah University of Science and Technology, NortonLifelock Research Group and Guangzhou University.
— “DETERRENT: Knowledge Guided Graph Attention for Detecting Healthcare Misinformation," by Limeng Cui, doctoral student in information sciences and technology; Hae Seung Seo and Maryam Tabar, doctoral students in informatics; Fenglong Ma, assistant professor of information sciences and technology; Suhang Wang, assistant professor of information sciences and technology; and Dongwon Lee, associate professor of information sciences and technology.
— “GRACE: Generating Concise and Informative Constrastive Sample to Explain Neural Network Model,” by Thai Le, doctoral student in information sciences and technology; Suhang Wang, assistant professor of information sciences and technology; and Dongwon Lee, associate professor of information sciences and technology.
— “Graph Structure Learning for Robust Graph Neural Networks,” by Xianfeng Tang, doctoral student in information sciences and technology, and Suhang Wang, assistant professor of information sciences and technology; with collaborators from Michigan State University.
— “HiTANeT: Hierarchical Time-Aware Attention Networks for Risk Prediction on Electronic Health Records,” by Junyu Luo and Muchao Ye, doctoral students in informatics; and Fenglong Ma, assistant professor of information sciences and technology; in collaboration with IQVIA.
— “Local Community Detection in Multiple Networks,” by Dongsheng Luo, Yaowei Yan and Xiao Liu, doctoral students in information sciences and technology; and Xiang Zhang, associate professor of information sciences and technology; along with collaborators from Baidu Research USA.
— “Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks,“ by Weilin Cong, graduate student in computer science; Mahmut Kandemir, professor of computer science and engineering; and Mehrdad Mahdavi, assistant professor of computer science and engineering; in collaboration with Microsoft Bing.
— “Targeted Data-driven Regularization for Out-of-Distribution Generalization,” by Mohammad Mahdi Kamani, doctoral student in informatics; Sadegh Farhang, doctoral student in information sciences and technology; Mehrdad Mahdavi, assistant professor of computer science and engineering; and James Wang, professor of information sciences and technology.
— "Identifying Homeless Youth At-Risk of Substance Use Disorder: Data-Driven Insights for Policymakers,” by Maryam Tabar, doctoral student in informatics; Stephanie Winkler, doctoral student in information sciences and technology; Dongwon Lee, associate professor of information sciences and technology; and Amulya Yadav, assistant professor of information sciences and technology.
— “Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps,” by Xianfeng Tang, doctoral student in information sciences and technology; Prasenjit Mitra, associate dean for research; and Suhang Wang, assistant professor of information sciences and technology.
The annual KDD conference is the premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics and big data.