Youngmok Jung
tom418@kaist.ac.kr | Google Scholar | CV

I am an AI research scientist at Genome Insight, where I collaborate with geneticists to develop secondary and tertiary analysis solutions for genomics data, aiming to uncover novel biological insights to enhance human health. Also, I am a computer science PhD candidate at INA lab, KAIST co-advised by Dongsu Han and Young Seok Ju. Expected date of graduation is February, 2024. Before then, I received my BS degree in Electrical Engineering in KAIST, South Korea.
My interests are in crafting high-performance systems that incorporate AI/ML approaches. I concentrate on utilizing underlying assumptions of real-world systems to enhance the integration of AI/ML techniques. My works aims at 1) designing efficient system built on AI/ML approaches; 2) improving AI/ML approaches by exploiting system-specific assumptions. All my work involves several months of implementation followed by thorough testing in real-world data.
Recently, I am striving to develop secondary and tertiary analysis solutions for genomics data using AI/ML approaches. Concurrently, I worked on various topics in high-performance networked systems, including AI enhanced video delivery system and data-center networking.
Awards and Honors
Feb, 2023 | Samsung Electronics 29th Humantech Paper Award (Silver Prize, Communication & Networks) |
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Feb, 2022 |
Samsung Electronics PhD Scholarship ![]() |
Spring, 2021 | KAIST Breakthrough of the Year 2021, Spring (LiveNAS, NEMO) |
Selected Publications
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PreprintBioinformaticsSIGCOMM
Publications
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CoNEXTSIGCOMMEuroSysMobicom
Experiences
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AI & ML for SystemDesigning AI-enhanced high-performance networked systemsEnhancing genome analysis pipeline with AI and ML techniques
Opensource
Main Contributor
RUN-DVChttps://github.com/kaist-ina/RUN-DVC - Generalizing deep learning-based variant caller via domain adaptation and semi-supervised learning.
BWA-MEMEhttps://github.com/kaist-ina/BWA-MEME - Short-read alignment software, released for benefit of research community.
Collaborative Projects
NAS: Neural Adaptive Content-aware Internet Video DeliveryNEMO: Enabling Neural-enhanced Video Streaming on Commodity Mobile DevicesTLT: Towards Timeout-less Transport in Commodity Datacenter NetworksNeuroScaler: Neural Video Enhancement at ScaleSkills
Programming Languages: Python, C, C++, SQL, UNIX shell scriptingFrameworks: Tensorflow, Pytorch, Spark, Hadoop, Python Django, Cloudstack
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