School of Computer Science Colloquium

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Now showing 1 - 6 of 6
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    Old and New Challenges in Networking, Including the Computing Kind
    ( 2021-12-09) Zegura, Ellen W. ; Georgia Institute of Technology. College of Computing ; Georgia Institute of Technology. School of Computer Science
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    From Immigrant to Entrepreneur and NBA Owner
    ( 2018-11-01) Ranadivé, Vivek ; Georgia Institute of Technology. School of Computer Science ; Bow Capital
    Vivek Ranadivé describes himself as a boy from Bombay – a boy who made his fortune digitizing Wall Street and providing real-time computing to the world’s largest companies. Now, the chairman of venture fund Bow Capital and the owner and chairman of the Sacramento Kings is looking to the next wave of technological change, which he calls Civilization 3.0. Ranadivé will speak about the ways software will guide, serve, protect and entertain us, transforming our lives and our economy.
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    Software Testing: And the Challenges (and Opportunities) Keep Coming!
    ( 2019-11-08) Soffa, Mary Lou ; Georgia Institute of Technology. School of Computer Science ; University of Virginia. Dept. of Computer Science
    Disruptive shifts in software application types and software development environments create challenges to software testing that need to be addressed to ensure software quality and reduce the cost of software development time. Over the years, the size and complexity of software have grown as well as the need for fast-changing codebases, fault detection strategies, and test case generation and selection. To meet these challenges, techniques such as regression testing, selection/prioritization, and fault localization have been developed as well as specialized testing techniques for GUIs, object-oriented software, mobile computing, and continuous evolution of software to name a few. This talk presents an overview of these challenges and solutions and references Mary Jean Harrold’s achievements in these areas. The talk then explores current challenges and opportunities that bring problems that cannot be solved by state of art techniques, including applications that are machine learning applications or use machine learning as part of a system where components interact and evolve. Other challenges that need to be explored involve autonomous systems, cloud applications, and data churn. As software becomes more autonomous, its operations and outputs become less predictable at test writing time; hence, the traditional nature of assert (Actual, Expected) test oracles does not work and needs to be addressed.
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    Legion — Programming Heterogeneous, Distributed Parallel Machines
    (Georgia Institute of Technology, 2018-01-31) Aiken, Alex ; Georgia Institute of Technology. School of Computer Science ; Stanford University. Computer Science Department
    Programmers tend to think of parallel programming as a problem of dividing up computation, but often the most difficult part is the placement and movement of data. As machines become more complex and hierarchical, describing what to do with the data is increasingly a fist-class programming concern. Legion is a programming model and runtime system for describing hierarchical organizations of both data and computation at an abstract level. A separate mapping interface allows programmers to control how data and computation are placed onto the actual memories and processors of a specific machine. This talk will present the design of Legion, the novel issues that arise in both the design and implementation, and experience with applications.
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    Opportunities and Perils of Data Science
    ( 2021-10-15) Spector, Alfred Z. ; Georgia Institute of Technology. College of Computing ; Georgia Institute of Technology. School of Computer Science ; Two Sigma Investments
    Data science has provided unprecedented opportunities to learn new insights and to predict, recommend, cluster, classify, transform, and optimize. Catalyzed by large-scale, networked computer systems, vast availability of data, and machine learning algorithms, data science has been extraordinarily impactful to-date, and it holds great promise in all disciplines. However, no new technology arrives without complications, and we have recently seen both the press and various political circles illustrating real, potential, and fictional implications of the field. This presentation aims to balance the opportunities provided by data science against the many challenges that have ensued. At its core, the talk proposes a rubric that practitioners can apply to tease out data science’s complexities and also maps out seven categories of data sciences challenges, ranging from engineering to ethics. The talk is illustrated with examples from many applications, and it concludes with some suggested ways to address the downsides of the field.
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    Lessons From Designing, Building, and Operating a Hyper-Scale Global Wide Area Network
    (Georgia Institute of Technology, 2019-11-14) Khalidi, M. Yousef Amin ; Georgia Institute of Technology. School of Computer Science ; Microsoft Corporation
    Cloud computing has ushered in a new class of hyper-scale systems, characterized by large-scale distributed systems, global connectivity, and ubiquitous computing models that span the spectrum from centralized data centers to the edge. Powering these hyper-scales are global networks that connect end-users through various other networks and devices to the cloud. In this talk, we describe our journey in building the network of one of the biggest cloud systems, Microsoft Azure. This network is built out of 130K+ miles of terrestrial and subsea cables, processes an average of 30 billion packets/second, and interconnects with more than 20K peering points around the globe. We describe many of the technological and engineering building blocks that enable us to build and reliably operate this network, including innovations in fiber optics, software-defined networking, network design, switch software and global-scale monitoring, and simulation software.