Justin_Yi_Resume.tex

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\name{Justin Yi}
\address{San Francisco, California \\ joostinyi00@gmail.com \\ 909-342-3421}

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    \raggedright University of California, Los Angeles 2022 & \raggedleft Computer Science B.S., Mathematics Minor GPA: 3.9
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\textbf{Relevant Coursework}: Algorithms, Data Mining, Probability/Statistical Theory, Machine Learning, Deep Learning, Reinforcement Learning, Optimization, Graph Neural Networks, Operating Systems

\begin{rSection}{Work Experience}
    \begin{rSubsection}{Software Engineer}{\href{https://www.baseten.co/}{Baseten}}{September 2022 - Present}
        \item Served as a founding engineer of the \textbf{Model Performance} team, implementing various inference optimization strategies like {\href{https://www.baseten.co/blog/how-we-built-production-ready-speculative-decoding-with-tensorrt-llm/}{\textbf{speculative decoding}}}, {\href{https://www.baseten.co/blog/33-faster-llm-inference-with-fp8-quantization/}{quantization}}, {\href{https://www.baseten.co/blog/driving-model-performance-optimization-2024-highlights/}{etc.}}, collaborating with stakeholders from core platform, infrastructure, and GTM teams. 
        \item Designed an {\href{https://www.baseten.co/blog/automatic-llm-optimization-with-tensorrt-llm-engine-builder/}{\textbf{LLM optimization and deployment pipeline}}} with \textbf{TensorRT-LLM} and in-house model weight distribution system for performant inference servers, resulting in {\href{https://www.baseten.co/customers/writer/}{\textbf{60\% greater throughput and 35\% cost \\ reduction}}} for customers serving production traffic.
        \item Maintained the ML containerization and serving framework {\href{https://github.com/basetenlabs/truss}{\textbf{Truss}}}, adding support for a CLI live reload experience with deployed services and more expressive containerization support.
    \end{rSubsection}
    \begin{rSubsection}{Data Science Intern}{\href{https://www.ai-camp.org/}{AI Camp (Edtech Startup)}}{June 2021 - August 2021}
        \item Project managed student developer NLP projects by defining success criteria and deliverables for machine learning web applications.
        \item Presented machine learning concepts to hundreds of students nationwide, developing the company's first ML fairness course offering.
    \end{rSubsection}
    \begin{rSubsection}{Research Assistant}{Pilon Group, UCLA}{April 2019 - June 2020}
        \item Trained and evaluated an \textbf{attentive generative adversarial network} for image restoration of rain streak distorted
        images for applications in autonomous driving systems using in house created datasets of 10,000+ samples.
        \item Performed reverse osmosis of fracking wastewater for $\text{CO}_2$ adsorption for a carbon negative concrete synthesis process.
    \end{rSubsection}
    \begin{rSubsection}{Research Assistant}{\small Bhandari Group, Cal Poly Pomona}{June 2017 - August 2017}
        \item Studied and implemented methods for autonomous drone navigation in GPS denied environments using OpenCV and Caffe frameworks for Hector SLAM mapping.
        \href{https://docs.google.com/presentation/d/1fp-MPZUgKS_PhMD90d4aL0Rh0-YfghnEYHb3sHKmnGE/edit?usp=sharing}{[poster]}
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\begin{rSection}{Selected Projects}
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        \item {\bf On the Complexity and Convergence of Approximate Policy Iteration Schemes}:
        Literature \href{https://github.com/joostinyi/ECE239AS/blob/master/RL_S20.pdf}{survey} of approximation methods of \textbf{Policy Iteration}
        for \textbf{Markov Decision Processes} to with considerations of algorithmic complexity bound analysis, convergence guarantees, and rates of convergence. 
        \href{https://github.com/joostinyi/ECE239AS/blob/master/Approximate-Policy-Iteration-Poster.pdf}{[poster]}
        \item {\bf Graph Neural Network Projects}:
        \href{https://github.com/yichousun/Winter2021_CS249_GNN/tree/main/Paper_Presentation/Graph_Synthesis/GCPN}{Presented} and demonstrated findings of a novel graph convolutional policy network for goal-directed molecular graph generation.
        Literature \href{https://github.com/Sripathm2/GNNSurvey}{survey} of GNN applications in the field of programming languages, namely in bug detection, similarity analysis, program synthesis, etc.
    \end{list}
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\begin{rSection}{Leadership Activities}
    \begin{rSubsection}{ACM AI President}{\href{https://www.uclaacm.com/}{ACM at UCLA}}{November 2019 - June 2022}
        \item Led 4 committees of 30 members through various workshop, guided project, event, and outreach offerings to the UCLA and surrounding communities.
        \item Developed and presented multiple 10 week \href{https://www.youtube.com/playlist?list=PLPO7_kXilXFYGa-3ZpOXa7Z01ZYAtUh1U}{workshops}
        to teach machine learning fundamentals to cohorts undergraduate students – topics included \textbf{Neural Networks}, \textbf{Deep Learning}, Convolutional NN, Recurrent NN, Fair ML.
        \item Authored and edited tech policy blogs exploring various relevant socially impactful tech topics: \href{https://medium.com/impact-labs/considerations-for-the-future-of-ai-governance-46d727012c5b}{\bf AI Governance}, Big Tech Regulation, \href{https://medium.com/acm-at-ucla/digital-tech-for-a-greener-future-7bc7f2e00bda}{Climate Tech}.
    \end{rSubsection}
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{\bf Skills:} Python, C++, bash, PyTorch, Numpy, Kubernetes, SQL, React, Typescript, LaTex, Ableton
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