Area Exam

The Applications of Machine Learning Techniques in Networking

The growing complexity and scale of Internet systems coupled with the improved capabilities of Machine Learning (ML) methods in recent years have motivated researchers and practitioners to increasingly rely on these methods for data-driven design and analysis of wide range of problems in network systems such as detecting network attacks, performing resource management, or improving quality of service (QoS). 

Verification Techniques for Low-Level Programs

We explore the application of highly expressive logical and automated reasoning techniques to the analysis of computer programs. We begin with an introduction to formal methods by describing different approaches and the strength of the properties they can guarantee. These range from static analyzers, SMT solvers, deductive program provers, and proof assistants. We then explore applications of formal methods to the analysis of intermediate representations, verification of floating point arithmetic, and fine-grained parallelism such as vectorization.

Methods for Accelerating Machine Learning in High Performance Computing

Driven by massive dataset corpuses and advances and programmability in accelerator architectures, such as GPUs and FPGAs, machine learning (ML) has delivered remarkable, human-like accuracy in tasks such as image recognition, machine translation and speech processing.  Although ML has improved accuracy in selected human tasks, the time to train models can range from hours to weeks.  Thus, accelerating model training is an important research challenge facing the ML field.

Program Performance Modeling Techniques for High Performance Computing

The performance model of an application can provide insight about its runtime behavior on particular hardware. Such information can be analyzed by both developers and researchers for performance tuning. In this talk, we explore different performance modeling techniques and categorize them into three groups: Analytical modeling; empirical modeling and simulation-based modeling.

Automated Statistical Methods for Parallel Performance Analysis

This paper explores the use of process automation to guide a parallel performance analyst through the knowledge discovery process, while providing the ability to customize the analysis process. For example, the input data can be evaluated to determine the distribution of the data, the standard deviation and/or the prevalence of outliers. Different analytical methods assume or require particular distributions or other data characteristics, and process automation would help prevent the misapplication of inappropriate analytical methods.

Personalized Pedestrian Navigation Assistant: User Profile Assessment in PC-RE Framework

Personalization has been applied and researched in the field of adaptive user interface, e-commerce, and requirements engineering. Many personalized systems don?t work well because of difficulty in inferring users? characteristics, particularly in the early stage of application usage. I propose that the alternative is to pay attention to users? abilities, goals, and preferences at the very early stage of requirements engineering. I will use the Personal and Contextual Requirements Engineering Framework to approach personalization.

Sensing Puns is its Own Reword: Automatic Detection of Paronomasia

Since the early stages of human language, puns have been an important component of it. The proof for this is the fact that Sumerian cuneiform and Egyptian hieroglyphs, which are two of the earliest human writing systems were based on punning systems. In contemporaneity applications, puns are abundant in literature, advertising and social media. However, despite the importance of puns in human language understanding and its long history, computational detection of puns is scarce.

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