Aerin Kim

The truth will set you free.

<h1>Welcome to <br> Aerin Kim's website! </h1>
<marquee>enjoy developing user-pleasing products using data.</marquee><br><br>

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 &nbsp;&nbsp; I am a passionate, entrepreneurial engineering lead with extensive hands-on execution experience at both small and large firms. I have a common sense approach to ML/engineering problems and an aptitude for improving user experiences. I thrive on generating results.<br><br><br></div>

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<a href="https://automata88.medium.com/" target="_blank">Blog</a><br>
<a href="https://github.com/aerinkim" target="_blank">GitHub</a><br>
<a href="https://www.youtube.com/@devjourney101" target="_blank">YouTube</a><br>
<a href="https://twitter.com/aerinykim" target="_blank">Twitter</a><br>
<a href="https://www.linkedin.com/in/aerinykim/" target="_blank">LinkedIn</a><br>
<a href="https://stackoverflow.com/users/3907250/aerin" target="_blank">StackOverflow</a>
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&nbsp;&nbsp; My professional work has been centered around building and optimizing large-scale production-grade machine learning models. Areas that I have sunk substantial time into are:
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• ML Applications Development: Various families of semantic parsing (text-to-SQL, question-answering, machine translation, etc.), deepfake detection and generation, visual QA, multimodal summarization, cross-modal retrieval, chatbot, object detection, semantic segmentation as well as targeted ad delivery. 
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• Search Accuracy and Database Integrity: Improve search result fidelity through advanced deduplication and indexing, leading to operational efficiency and cost savings.
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• Stochastic Modeling and Simulation: Use tools from probability theory including Markov chains, queueing theory, and reliability analysis, to simulate real-life phenomena. 
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• High-Volume Data Processing: Real-time streaming data ingestion, delimiting, deduplication, and recalibration to guarantee stunningly accurate ML results.
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• Training Dataset Synthesis: Datasets for academia and industry using both synthetic and human-in-the-loop methods.
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• Autoscaling for ML Workloads: Handling of large-scale batch jobs, job queues, and unexpected demand surges by scaling compute resources.
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• Hypothesis A/B Testing: Design and execute experiments. Make decisions about variable selection, control groups, and segmentation.
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<div style="text-align: center;"> . . . </div>
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&nbsp;&nbsp;  I believe in everyone's innate potential as they each have a vast cosmos within them.  Working with exceptional people and solving hard problems gives me the greatest joy.
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