Sanjay Ranka

Gainesville, Florida, United States Contact Info
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Sanjay Ranka is a Distinguished Professor in the Department of Computer Information…

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Experience & Education

  • American Society for AI

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Publications

  • An Efficient Computational Framework for Labeling Large Scale Spatiotemporal Remote Sensing Datasets

    2014 Seventh International Conference on Contemporary Computing (IC3)

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  • Optimal Alignment of Three Sequences On A GPU

    2014 4th International Conference on Bioinformatics and Computational Biology (BICoB), Las Vegas, Nevada, USA,

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  • Multicore and GPU Algorithms for Nussinov RNA Folding

    2013 IEEE 3rd International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), New Orleans, USA

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  • A global local modeling of internet usage in large mobile societies

    Proceedings of the 7th ACM MSWiM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks (PM2HW2N '12)

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  • Energy- and performance-aware scheduling of tasks on parallel and distributed systems

    ACM Journal on Emerging Technologies in Computing Systems

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  • Parallel Syntenic Alignment on GPUs

    2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM-BCB), Orlando, USA

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  • Dynamic Reconfiguration in Real-Time Systems: Energy, Performance, and Thermal Perspectives

    Springer

    Given the widespread use of real-time multitasking systems, there are tremendous optimization opportunities if reconfigurable computing can be effectively incorporated while maintaining performance and other design constraints of typical applications. The focus of this book is to describe the dynamic reconfiguration techniques that can be safely used in real-time systems. This book provides comprehensive approaches by considering synergistic effects of computation, communication as well as…

    Given the widespread use of real-time multitasking systems, there are tremendous optimization opportunities if reconfigurable computing can be effectively incorporated while maintaining performance and other design constraints of typical applications. The focus of this book is to describe the dynamic reconfiguration techniques that can be safely used in real-time systems. This book provides comprehensive approaches by considering synergistic effects of computation, communication as well as storage together to significantly improve overall performance, power, energy and temperature. Provides a comprehensive introduction to optimization and dynamic reconfiguration techniques in real-time embedded systems; Covers state-of-the-art techniques and ongoing research in reconfigurable architectures;Focuses on algorithms tuned for dynamic reconfiguration techniques in real-time systems; Provides reference for anyone designing low-power systems, energy-/temperature-constrained devices, and power-performance efficient systems which execute tasks with timing constraints.

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  • CMRF: analyzing differential gene regulation in two group perturbation experiments

    BMC Genomics

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  • CMRF: analyzing differential gene regulation in two group perturbation experiments

    BMC Genomics

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  • Pairwise Sequence Alignment for Very Long Sequences on GPUs

    2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), Las Vegas, USA

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  • Strassen's Matrix Multiplication on GPUs

    2011 IEEE 17th International Conference on Parallel and Distributed Systems (ICPADS), Tainan, Taiwan

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  • Behavior-aware Traffic Modeling of Mobile Internet: An Information Theoretic Co-clustering Approach

    The 17th ACM International Conference on Mobile Computing and Networking (MobiCom'11)

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  • Behavior-aware Traffic Modeling of Mobile Internet: An Information Theoretic Co-clustering Approach

    The 17th ACM International Conference on Mobile Computing and Networking (MobiCom'11)

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  • Efficient GPU Implementation for Particle in Cell Algorithm

    IPDPS 2011:395-406

    Particle in cell method is widely used method in
    the plasma physics to study the trajectories of charged particles
    under electromagnetic fields. The PIC algorithm is computationally
    intensive and its time requirements are proportional to the
    number of charged particles involved in the simulation. The focus
    of the paper is to parallelize the PIC algorithm on Graphics
    Processing Unit (GPU). We present several performance tradeoffs
    related to the small shared memory and atomic…

    Particle in cell method is widely used method in
    the plasma physics to study the trajectories of charged particles
    under electromagnetic fields. The PIC algorithm is computationally
    intensive and its time requirements are proportional to the
    number of charged particles involved in the simulation. The focus
    of the paper is to parallelize the PIC algorithm on Graphics
    Processing Unit (GPU). We present several performance tradeoffs
    related to the small shared memory and atomic operations
    on the GPU to achieve high performance.

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  • Data-driven co-clustering model of internet usage in large mobile societies

    Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems

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  • Inferring progression models for CGH data

    Bioinformatics

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  • GPU Matrix Multiplication

    chapter in Handbook on Multicore Computing, Chapman Hill

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  • Multicore and GPU Algorithms for Nussinov RNA Folding (journal version, to appear)

    BMC Genomics/Bioinformatics Special Issue

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  • Pairwise Sequence Alignment for Very Long Sequences on GPUs (journal version, to appear)

    International Journal of Bioinformatics Research and Applications

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Patents

  • Method, Algorithm, and Computer Program for Optimizaing the Performance of Messages Including Advertisements in an Interactive Measurable Medium

    Issued US United States 8086485

    Method, procedure, algorithm, system, and computer program for improving and attempting to optimize the performance of messaging campaigns, particularly to marketing campaigns in which advertisements or other messages are distributed over an interactive measurable medium such as the Internet. A goal is to allocate the message alternatives to the customer population to optimize business objectives. The includes reading prior stage message state data pertaining to a prior stage including a…

    Method, procedure, algorithm, system, and computer program for improving and attempting to optimize the performance of messaging campaigns, particularly to marketing campaigns in which advertisements or other messages are distributed over an interactive measurable medium such as the Internet. A goal is to allocate the message alternatives to the customer population to optimize business objectives. The includes reading prior stage message state data pertaining to a prior stage including a cumulative number of trials and a cumulative number of successes for a particular message. Message performance results representing message trials and message successes from the previous stage based on the prior-stage state are then read, and a current message state is computed. A current message allocation is generated based on the current message state. Desirably the cumulative number of trials and the cumulative number of successes are discounted based on the age of the information. This inventiv...

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  • Method, algorithm, and computer program for targeting messages including advertisements in an interactive measurable medium

    Issued US US8260663 B1

    Method, procedure, algorithm, system, and computer program and computer program product for improving and optimizing or attempting to optimize performance of messaging campaigns, particularly to marketing campaigns in which advertisements or other messages are distributed over an interactive measurable medium such as the Internet. When message is an advertisement, campaign involves a list of ad alternatives and a target customer population. Goal of message manager or marketing manager is to…

    Method, procedure, algorithm, system, and computer program and computer program product for improving and optimizing or attempting to optimize performance of messaging campaigns, particularly to marketing campaigns in which advertisements or other messages are distributed over an interactive measurable medium such as the Internet. When message is an advertisement, campaign involves a list of ad alternatives and a target customer population. Goal of message manager or marketing manager is to allocate ad alternatives to customer population to optimize business objectives such as maximizing the number of positive responses received. This is achieved at least in part by segmenting customer population into segments and then finding best allocation of ad alternatives for each segment. The number of segments and grouping of a customer population changes at different stages of the campaign. When the message is other than an advertisement, goal is to allocate messages to optimize analogous business or campaign objectives, typically measured by the number of successes or successful responses.

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  • System and method for improving the performance of electronic media advertising campaigns through multi-attribute analysis and optimization

    Filed US US 2011/0238487 A1

    Automated system, methods, algorithms, procedures, and computer software programs and computer program products for improving and optimizing the performance of messaging campaigns, particularly for marketing campaigns in which advertisements or other messages are distributed over an interactive measurable medium such as the Internet. [...]

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Languages

  • English

    -

  • Hindi

    -

Organizations

  • AAAS

    Fellow

  • IEEE

    Fellow

Recommendations received

  • LinkedIn User

    LinkedIn User

    “Professor Sanjay Ranka was my Ph.D. co-adviser at the University of Florida, Gainesville. He is a highly talented and accomplished professor and I feel very lucky to have him as my research mentor. He is a profound scholar with a firm grip in parallel computing, algorithms, and data mining. He is very friendly and polite to his students and gives extra-time in his office hours to students who want to discuss with him about the subject issues and any intricacies they are encountering in the subject. He is an excellent research adviser who motivates students for innovative thinking and emphasizes on quality research. I learned immensely from his guidance during my Ph.D. I wish him all the best!”

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