InCM-GCL, we can either takethe textfeaturexT orthe image featurexI asthe content feature, and consider the corresponding text encoderfT or image encoderfI as the content encoder. In this section, we discuss the settings of baseline models for imbalanced node classification over fourgraphs. G1: We convert the rich text content into the bag-of-words feature vectors, and further feed the feature vectors with different imbalance ratios to a two-layer MLP [7] classifier to get the classification results. For AMiner, YelpChi, and GitHub graph datasets, we implement CHI-Square [11]toselect useful feature words. G2: We implement three graph neural network based representation learning models including GCN [5], GAT [9], and GraphSAGE [2] to learn the node embeddings by leveraging both node feature (bag-of-words feature vector) andgraph structure information.

My life has always involved centering the voices of those historically marginalized in order to foster equitable communities. Growing up, I lived in a small suburb just outside of Cleveland, Ohio and I was fortunate enough to attend Laurel School, an all-girls school focused on encouraging young women to think critically and solve difficult world problems. But my lived experience at school was so different from kids who lived even on my same street. I was grappling with watching families around me contend with an economic recession, losing any financial security that they had and I wanted to do everything I could to change that. Even though my favorite courses at the time were engineering and African American literature, I was encouraged to pursue economics.

Waltz served as Association for the Advancement of Artificial Intelligence (AAAI) president from 1997 to 1999, was a Fellow of AAAI and the Association for Computing Machinery (ACM), a senior member of the Institute for Electrical and Electronics Engineers (IEEE), and former chair of the ACM Special Interest Group on Artificial Intelligence (SIGART). Prior to joining CCLS, he was president of the NEC Research Institute in Princeton, and from 1984-1993 was director of Advanced Information Systems at Thinking Machines Corporation and a professor of computer science at Brandeis University. A celebration of his life was held in the spring of 2012, and a symposium in his honor was held September 23, 2012, at Brandeis University in Waltham, Massachusetts. That dissertation created the field of constraint propagation by showing that constraints and a rich but simple descriptive system were sufficient to recover threedimensional information from a two-dimensional projection. Besides an education, Dave picked up a passion for the highenergy atmosphere that propelled the MIT AI Lab to prominence -- an atmosphere that he spent the rest of his life recreating. In 1973, Dave Waltz with Richard P. Gabriel in tow headed west from MIT to the University of Illinois at Urbana-Champaign (UIUC) with the goals of starting a first-rate AI program and creating a lab in the image of the MIT AI Lab. All they had were an enthusiastic home in the Coordinated Science Laboratory, some friendly faculty in the Electrical Engineering department, a PDP-10, a shaky connection to the Advanced Research Projects Agency Network (ARPANET), and a small but eager coterie of misfit graduate students.

Brandeis has a program in autonomous agents, focusing on multi--agent and multi--robot systems and machine learning, headed by Maja Mataric For details on research directions and a photo of the available robot herd see: http://www.cs.brandeis.edu/dept/faculty/mataric To get more information about the Volen Center for Complex Systems, about the Computer Science Department, and about other faculty, see: http://www.cs.brandeis.edu/dept. For more information about the cognitive science and cognitive neuroscience programs at Brandeis see: http://fechner.ccs.brandeis.edu/cogsci.html The Robotics Institute also offers a Robotics PhD and students from other programs (e.g. Research includes many aspects of mobile robots, computer integrated manufacturing, rapid prototyping, sensors, vision, navigation, learning and architectures.