All times are in US EDT
June 15th 12:30
Topic: Computational Microscopy of SARS-CoV-2
Speaker: Rommie Amaro, UCSD, USA
Abstract: I will discuss our lab’s efforts, together with collaborators, to use computational microscopy to understand the SARS-CoV-2 virus in atomic detail, with the goals to better understand molecular recognition of the virus and host cell receptors, antibody binding and design, and the search for novel therapeutics. I will focus on our studies of the spike protein, its glycan shield, its interactions with the human ACE2 receptor, our ACM Gordon Bell Special Prize winning efforts to model the SARS-CoV-2 virion, and escape variants.
Bio: Rommie E. Amaro holds the Distinguished Professorship in Theoretical and Computational Chemistry at the Department of Chemistry and Biochemistry at the University of California, San Diego. She received her B.S. in Chemical Engineering (1999) and her Ph.D. in Chemistry (2005) from the University of Illinois at Urbana-Champaign. Rommie was a NIH postdoctoral fellow with Prof. J. Andrew McCammon at UC San Diego from 2005-2009, and started her independent lab in 2009. She is the recipient of an NIH New Innovator Award, the Presidential Early Career Award for Scientists and Engineers, the ACS COMP OpenEye Outstanding Junior Faculty Award, the ACS Kavli Foundation Emerging Leader in Chemistry, the Corwin Hansch Award, and the ACM Gordon Bell Special Prize for COVID19. Rommie’s scientific interests lie at the intersection of computer-aided drug discovery and biophysical simulation. Her scientific vision revolves around expanding the range and complexity of molecular constituents represented in such simulations, the development of novel multiscale methods for elucidating their time dependent dynamics, and the discovery of novel chemical matter controlling biological function.
June 16th 10:00
Topic: Processing in Memory: Past, Present, and Future
Speaker: Kevin Skadron, , University of Virginia, USA
Abstract: Applications are increasingly data-intensive and bound by the performance of the memory and/or storage system. This “memory wall” arises from several factors: the volume of data is increasing exponentially, outstripping cache capacities; many applications involve streaming data with little or no temporal reuse; as algorithms become more sophisticated, access patterns are often unfriendly to effective caching; and the computation intensity of many of these algorithms is low--we often spend more time and energy moving data to the processor than we spend computing on the data. All these factors motivate breaking down the classic von Neumann architecture that separates processing and memory, and computing as close to the data as possible, with processing elements either tightly coupled with memory or storage, or possibly even embedded directly in the memory chips. The memory wall has been a concern for decades, with numerous proposals over the years for processing-in-memory and near-data architectures. This talk will review the motivation for processing in memory and some prior proposals, then provide an overview of the current landscape, and then conclude with some suggestions for promising applications and directions for in-memory/near-data design, together with some necessary operating-system and middleware capabilities.
Bio: Kevin Skadron is the Harry Douglas Forsyth Professor and chair of Computer Science at the University of Virginia, where he has been on the faculty since 1999, after receiving his PhD at Princeton. He is also director of the Center for Research on Intelligent Storage and Processing in Memory, part of the SRC JUMP program, as well as the Center for Automata Processing. He received his Ph.D. in Computer Science from Princeton University, is a Fellow of the IEEE and the ACM, and a recipient of the 2011 ACM SIGARCH Maurice Wilkes Award. Skadron’s research interests include design and application of accelerators and heterogeneous architectures, their memory hierarchies, and associated power, thermal, reliability, and programming challenges. He and his colleagues and students have developed a number of tools to support research on these topics, such as MNCaRT, HotSpot and Rodinia.
June 17th 10:00
Topic: Quantum Computing with Atoms
Speaker: Christopher Monroe, Duke University and IonQ, Inc., USA
Abstract: Quantum Computers are a radical departure from conventional computation, where bits are replaced by quantum bits or qubits, that can exist in a superposition of both 0 and 1. The entanglement of many qubits allows the storage and processing of huge amounts of information. A leading physical platform for quantum computers is a collection of individual atoms, suspended from electrodes crafted from a nearby chip, in a vacuum chamber. Trapped atomic ion qubits are perfectly identical and have essentially infinite idle coherence times, and therefore have the ingredients to scale. Quantum gate operations are performed are controlled with laser beams, allowing densely-connected and reconfigurable universal gate sets. Unlike all other physical platforms for quantum computing, the path to scale involves concrete architectural paths, from shuttling ions between QPU cores to modular photonic interconnects between multiple QPUs. Full-stack ion trap quantum computers have thus moved away from the physics of qubits and gates and toward the engineering of optical control signals, quantum gate compilation for algorithms, and high level system design considerations. I will summarize the state-of-the-art in these quantum computers and speculate on how they might be used.
Bio: Christopher Monroe is an experimental atomic and quantum physicist, with interests in developing and building full-stack quantum computers. He is Director of the Duke Quantum Center and a Professor of Electrical and Computer Engineering and Physics at Duke University. Monroe is also co-founder and Chief Scientist at IonQ, a company that is building full-stack quantum computers based on trapped atomic ions. He is an architect of the U.S. National Quantum Initiative and a member of the National Academy of Sciences.