Sonya Alexandrova

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Existing approaches to Robot Programming by Demonstration (PbD) require multiple demonstrations to capture task information that lets robots generalize to unseen situations. However, providing these demonstrations is cumbersome for endusers. In addition, users who are not familiar with the system often fail to demonstrate sufficiently varied demonstrations.(More)
General-purpose robots can perform a range of useful tasks in human environments; however, programming them to robustly function in all possible environments that they might encounter is unfeasible. Instead, our research aims to develop robots that can be programmed by its end-users in their context of use, so that the robot needs to robustly function in(More)
De novo sequencing of proteins and peptides is one of the most important problems in mass spectrometry-driven proteomics. A variety of methods have been developed to accomplish this task from a set of bottom-up tandem (MS/MS) mass spectra. However, a more recently emerged top-down technology, now gaining more and more popularity, opens new perspectives for(More)
General-purpose robots present the opportunity to be programmed for a specific purpose {em after} deployment. This requires tools for end-users to quickly and intuitively program robots to perform useful tasks in new environments. In this paper, we present a flow-based visual programming language (VPL) for mobile manipulation tasks, demonstrate the(More)
There are two approaches for de novo protein sequencing: Edman degradation and mass spectrometry (MS). Existing MS-based methods characterize a novel protein by assembling tandem mass spectra of overlapping peptides generated from multiple proteolytic digestions of the protein. Because each tandem mass spectrum covers only a short peptide of the target(More)
The main theme of this class is randomized algorithms. We start by comparing these to the deterministic algorithms to which we are so accustomed. In the deterministic model of computation (Turing machines and RAM), an algorithm has fixed behavior on every fixed input. In contrast, in the randomized model of computation, algorithms take additional input(More)
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