Efficient solutions to large-scale problems.

Much of MERL’s research activity involves formulating scientific and engineering problems as optimizations, which can be solved in an efficient way. We have developed fundamental algorithms to better solve classic problems, such as quadratic programs and minimum-cost paths. Our work also involves developing theoretical bounds to understand performance limits.

  • Researchers

  • News & Events

    •  TALK   Advances in Accelerated Computing
      Date & Time: Friday, February 2, 2018; 12:00
      Speaker: Dr. David Kaeli, Northeastern University
      MERL Host: Abraham Goldsmith
      Research Areas: Control, Optimization, Machine Learning, Speech & Audio
      • GPU computing is alive and well! The GPU has allowed researchers to overcome a number of computational barriers in important problem domains. But still, there remain challenges to use a GPU to target more general purpose applications. GPUs achieve impressive speedups when compared to CPUs, since GPUs have a large number of compute cores and high memory bandwidth. Recent GPU performance is approaching 10 teraflops of single precision performance on a single device. In this talk we will discuss current trends with GPUs, including some advanced features that allow them exploit multi-context grains of parallelism. Further, we consider how GPUs can be treated as cloud-based resources, enabling a GPU-enabled server to deliver HPC cloud services by leveraging virtualization and collaborative filtering. Finally, we argue for for new heterogeneous workloads and discuss the role of the Heterogeneous Systems Architecture (HSA), a standard that further supports integration of the CPU and GPU into a common framework. We present a new class of benchmarks specifically tailored to evaluate the benefits of features supported in the new HSA programming model.
    •  NEWS   MERL invites applications for Visiting Faculty
      Date: February 15, 2018
      • University faculty members are invited to spend part or all of their sabbaticals at MERL, pursuing projects of their own choosing in collaboration with MERL researchers.

        To apply, a candidate should identify and contact one or more MERL researchers with whom they would like to collaborate. The applicant and a MERL researcher will jointly prepare a proposal that the researcher will champion internally. Please visit the visiting faculty web page for further details: http://www.merl.com/employment/visiting-faculty.php.

        The application deadline for positions starting in Summer/Fall 2018 is February 15, 2018.

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  • Internships

    • MP1249: Control of Systems with Hybrid Dynamics

      MERL is seeking an intern to develop optimal control strategies for mode-switching vapor compression systems characterized by discrete changes in dynamics. The ideal candidate has a strong background in hybrid systems, optimization, and event-based control. Proficiency in MATLAB, Python or Julia is required. Senior students enrolled in doctoral programs in engineering, applied mathematics or related fields are encouraged to apply. It is expected that the intern will assist in preparation of results for publication in scientific venues. The duration of the internship is approximately three months.

    • SP1267: Signal spectrum analysis

      MERL is looking for a self-motivated intern to work on signal spectrum analysis. The ideal candidate would be a senior Ph.D. student with strong background in signal processing, compressive sensing, and mathematics. Proficiency in MATLAB and Python programming is necessary. Experience in analyzing real experimental data or detecting weak signal in noisy environment is a great asset. The intern is expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. Start date is flexible.

    • CD1260: Model Predictive Control of Hybrid Systems

      The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to work on hybrid model predictive control. The scope of work includes the development of model predictive control algorithms for hybrid dynamical systems, switched systems, and quantized systems, analysis and property proving, and applications in automotive, space systems, and energy systems. PhD students with expertise in some among control, optimization, model predictive control and hybrid systems, and with working knowledge of Matlab implementation are welcome to apply. The expected duration of the internship is 3-6 months with flexible start date.

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  • Videos