Abstract: ------------- We discuss new hybrid strategies we have developed for managing synchrony and memory in parallel graph algorithms.  We first describe a new algorithmic paradigm k-level asynchronous (KLA) that enables the level of asynchrony in parallel graph algorithms to be parametrically varied from none (level-synchronous) to full (asynchronous).  Results of an implementation of KLA in the STAPL Graph Library show excellent scalability on up to 96K cores and improvements of 10x or more over level-synchronous and asynchronous versions for graph algorithms such as breadth first search, PageRank, k-core decomposition and others on certain classes of real-world graphs.  We next describe a novel RAM-Disk hybrid approach to graph processing that works by partitioning the graph into subgraphs that fit in RAM and uses a paging-like technique to load subgraphs.  An implementation of this strategy in STAPL shows that without modifying the algorithms, this approach can scale from small memory-constrained systems (such as tablets) to largescale distributed machines with 16,000+ cores. Bio: ------------- Nancy M. Amato is Unocal Professor of Computer Science and Engineering at Texas A&M University where she co-directs the Parasol Lab. Her main areas of research focus are motion planning and robotics, computational biology and geometry, and parallel and distributed computing.  Amato received undergraduate degrees in Mathematical Sciences and Economics from Stanford University, and M.S. and Ph.D.  degrees in Computer Science from UC Berkeley and the University of Illinois, respectively. She was an AT&T Bell Laboratories PhD Scholar, received an NSF CAREER Award, is an ACM Distinguished Speaker, and was an IEEE Robotics and Automation Society Distinguished Lecturer (2006-2007).  She is a AAAS Fellow and an IEEE Fellow.