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18 followers · 74 following

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Achievement: Arctic Code Vault ContributorAchievement: Pull Shark

Achievements

Achievement: Arctic Code Vault ContributorAchievement: Pull Shark

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greninja/README.md

Hi there 👋

I have a strong foundation in research 🧪, with some experience building software 💻 as well under my belt. I recently earned my Master’s degree in Computer Science from UBC (Vancouver), where my work focused on trustworthy machine learning, leading to a Spotlight acceptance at a top-tier Machine Learning Conference, NeurIPS'24 (Link). For more details on my background and projects, visit my webpage (link on the left).

As I move forward in my career, I’m excited to transition into an industry-focused role as a software or machine learning engineer. I’m particularly drawn to early to mid-stage startups where I can contribute to meaningful growth. I highly value a strong, dynamic founding team and a culture that promotes both personal and professional development. I’m open to exploring different domains, as long as the product and roadmap are compelling, the team is solid, and there’s significant opportunity for learning and growth. If you think I’d be a great fit, feel free to reach out!

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  1. adaptive-randomized-smoothing adaptive-randomized-smoothing Public

    Forked from ubc-systopia/adaptive-randomized-smoothing

    Python

  2. NPLM NPLM Public

    Neural Network for word embeddings and Language Model

    Python 5 5

  3. PoU-Manifold PoU-Manifold Public

    Code to smoothly patch together local functions (linear/ polynomial) defined on charts of a manifold to give a global approximation using Partitions of Unity (PoU)

    Python 1 1

  4. CNF-Gen CNF-Gen Public

    Generate 2-CNF boolean functions via SAT solving

    Python 1

  5. javaminining javaminining Public

    Code to scrap git repositories and fetch commits (wherein new function arguments are added) using PyDriller

    Python

  6. ml_privacy_meter ml_privacy_meter Public

    Forked from privacytrustlab/ml_privacy_meter

    Machine Learning Privacy Meter: A tool to quantify the privacy risks of machine learning models with respect to inference attacks

    Python