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Remy O. AI, Cloud and Modern Frameworks

My name is Remy O. and I have over 5 years of experience in the tech industry. I specialize in the following technologies: Python, TensorFlow, PyTorch, Artificial Intelligence, Django, etc.. I hold a degree in , , Bachelor of Science (B.S.). Some of the notable projects I’ve worked on include: Chegg Tutoring, Center For Speech And Language Processing - Johns Hopkins University. I am based in Dallas, United States. I've successfully completed 2 projects while developing at Softaims.

I value a collaborative environment where shared knowledge leads to superior outcomes. I actively mentor junior team members, conduct thorough quality reviews, and champion engineering best practices across the team. I believe that the quality of the final product is a direct reflection of the team's cohesion and skill.

My experience at Softaims has refined my ability to effectively communicate complex technical concepts to non-technical stakeholders, ensuring project alignment from the outset. I am a strong believer in transparent processes and iterative delivery.

My main objective is to foster a culture of quality and accountability. I am motivated to contribute my expertise to projects that require not just technical skill, but also strong organizational and leadership abilities to succeed.

Main technologies

  • AI, Cloud and Modern Frameworks

    5 years

  • Python

    2 Years

  • TensorFlow

    3 Years

  • PyTorch

    4 Years

Additional skills

Direct hire

Potentially possible

Previous Company

Google

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Experience Highlights

Chegg Tutoring

I worked with a variety of students in STEM, with programming as a main focus.

Center For Speech And Language Processing - Johns Hopkins University

> The 8-week workshop provided an intense intellectual environment. Undergraduates worked closely alongside more senior researchers as part of a multi-university research team, which had been assembled for the summer to attack some problem of current interest. > The classical performance measure of a speech recognizer, a.k.a. speech transcriber, has been word-error rate (WER). This measure dates back to the days when ASR was regarded as a task in its own right, and the goal was simply nothing more than to aim for the same perfect transcriptions that humans can (in principle) generate from listening to speech. We know better now: it seems, in fact, very unlikely that anyone would be interested in reading as much as a single-page transcript of colloquial, spontaneous speech, even if it were transcribed perfectly. What people want is to search through speech, summarize speech, translate speech, etc. And our computers’ memory capacity is now capable of storing large amounts of digitized audio to make these derivative tasks more direct. > Underneath the hood of any one of these “real” tasks, however, is a speech recognizer, or at least some components of one, which generates word hypotheses that are numerically scored. Even very flawed transcripts are nevertheless a very valuable source of features on which to train spoken language processing applications, even if we would be too embarrassed to show them to anyone. How do we evaluate the issue of these components? According to recent HCI research, WER simply does not work. Dumouchel showed that manually correcting a transcript with more than 20% WER is actually harder than starting over from scratch, and yet Munteanu et al. showed that transcripts with WERs of as much as 25% are statistically significantly better than not having a transcript on a common lecture-browsing task for university students. Transcripts with WERs as bad as 46% have proven to be a useful source of features for speech summarization systems, at least according to the very flawed standards of current summarization evaluations, but it is also clear that those same standards often label poor summaries as very good because of the lack of a higher-level organization or goal orientation that people expect from summaries, and it remains unclear the extent to which WER affects this. University of Toronto’s computational linguistics lab, which specializes in HCI-style experimental design for spoken language interfaces, is currently conducting a large human-subject study of speech summarizers, in order to evaluate summary quality in a more ecologically valid fashion. > With this experience at hand, this workshop focused on measures of transcript quality that are complementary to WER in the ASR- and summarization-related tasks of: (1) rapid message assessment, in which an accurate gist of a small spoken message must be formed in order to make a rapid decision, such as in military and emergency rescue contexts; and (2) decision-support for meetings, in which very interactive spoken negotiations between multiple participants must be distilled into a set of promises, deliverables and dates. Our intention was to bring our experience with human-subject experimentation on ASR applications together with recent advances in semantic distance measures as well as statistical parsing to formulate complementary objective functions to WER that can be computed without human-subject trials and employed to turn around better message-assessment and decision-support systems through periodic testing on development data.

Education

  • Harvard University

    in Computer science

    2008-01-01-2008-01-01

  • Baylor College of Medicine

    in

  • Duke University

    Bachelor of Science (B.S.) in Computer science, Neuroscience

Languages

  • English
  • Spanish
  • Japanese

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