Byung-Hak (Hak) Kim Email & Phone Number

Seoul, Kr

Jung-Gu, Seoul, Kr

Overseeing AI strategies for all business divisions and core AI R&D at a $30B revenue South Korean conglomerate alongside the group's CDO (Chief Digital Officer) and CAIO (Chief AI Officer), with a current focus on LLM and GenAI efforts in the entertainment and media domains :)▎(12/23) We made the top-10 list of "NeurIPS LLM Efficiency Challenge" (NVIDIA A100, aka industry track)! ► Invited Talks• "Addressing the Cost Disease with GenAI" at Seoul National Univ. and CJ Logistics (11/23)

https://mysc.imweb.me/ENGLISH

Palo Alto, California, Us

https://themiilk.com

South San Francisco, Ca, Us

South San Francisco, Ca, Us

► Company News• 03/22 - With Series-C funding, AKASA has become a 🦄 startup• 12/21 - AKASA recognized by #CBInsights on their #DigitalHealth 150 list for the second year in a row!• 03/21 - Thrilled to announce $60M Series B led by BOND and new brand identity, AKASA! • 08/20 - Alpha Health named to the 2020 CB Insights Digital Health 150 List - one of the most promising private digital health companies in the world!• 06/20 - Official company launch and $20M Series A led by a16z!► Publications• 08/21 - "Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines" paper presented at #MLHC2021 (and covered by BusinessInsider, EnterpriseAI, and AIThority) • 07/20 - "Deep Claim: Payer Response Prediction from Claims Data with Deep Learning" paper spotlighted at ICML2020 HSYS workshop (covered in Medical AI Times, Datanami, and Synced)► Invited Talks• "Autonomous Medical Coding: A Step toward the Future of Drug Development" at Stanford K-BioX (07/21) • "Addressing Baumol's Cost Disease with Machine Learning Startups!" at Upstage (07/21) and Samsung (05/21)• "Addressing Baumol's Cost Disease in Healthcare with Machine Learning" at US-Korea Conference (UKC 2021, 12/21) and Healthcare NLP Summit 2021 (04/21, featured on AI-TechPark)• "Imagining the Future with Machine Learning Startups!" at Lotte Ventures (2/22), Yonsei Univ (11/21), Kookmin Univ (08/21), Korea Univ (11/20), K-Tech@Silicon Valley 2020 (10/20), Stanford K-BioX (09/20), and Koreans in Silicon Valley 2020 (09/20)• "Navigating Korean Communication Barriers in Silicon Valley" at Samsung (11/21)• "LumièreNet and After!" at Kookmin Univ (10/19)► Community• Program committees for NAACL2022 "Clinical NLP", ML4H2021, ICCV2021 "Computer Vision for Automated Medical Diagnosis", ICML2021 "Self-Supervised Learning for Reasoning and Perception", and NeurIPS2019 "Fair ML for Health" workshops• Reviewers for NeurIPS, ICLR, ICML, ACL, NAACL, MLHC, ML4H, and CHIL

Mountain View, California, Us

▎Led the AI research to shape the future of education and make its direct impact on the world (at scale) through Udacity!► Publications• 07/19 - "LumièreNet: Lecture Video Synthesis from Audio" (https://arxiv.org/abs/1907.02253) featured in the press (VentureBeat, ElearningInside, FanaticalFuturist, etc) and on podcasts (SydneyBusinessInsights, BildungAltEntfernen, etc)• 05/19 - "Deep Learning to Predict Student Outcomes" paper accepted as a contributed talk to the AI for Social Good Workshop at ICLR 2019! (http://bit.ly/iclr2019) • 09/18 - "Domain Adaptation for Real-Time Student Performance Prediction" (https://arxiv.org/abs/1809.06686)• 07/18 - "GritNet: Student Performance Prediction with Deep Learning" paper presented at EDM 2018 (http://bit.ly/GritNet, accepted for oral)► Invited Talks• 04/19 - Invited talk on "Deep Learning to Predict Student Outcomes" at Texas A&M Univ. Workshop on AI in Education (AIEdu2019)• 02/19 - Our team's invited talk on "Predicting and Improving Student Performance with Machine Learning" at BayLAN conf., Stanford Univ. (http://bit.ly/BayLAN2019)• 10/18 - Invited talks on "Deep Learning to help student’s Deep Learning" at Naver DeView conf. (https://deview.kr/2018/schedule/265) and Sungkyunkwan Univ.► Community• 08/19 - Organized “2019 KDD Deep Learning Day Workshop on Deep Learning for Education (DL4Ed)” at Alaska (http://ml4ed.cc/2019-kdd-workshop)

▎We achieved the best (conversational) speech recognition accuracy on par with humans, reaching the closest performance so far to "Human Parity"! ► Publication• 05/17 - Our team paper "Deep Learning-Based Telephony Speech Recognition in the Wild" accepted to Interspeech 2017 as an oral presentation (http://bit.ly/interspeech2017)

Santa Clara, Ca, Us

▎Marvell is the world leader in ICs for mass storage - as a signal processing algorithms architect of Marvell's “Best and Brightest” data storage coding and signal processing group, led developments of state-of-the-art signal processing, machine learning, and communication algorithms for HDD (aka baseband MIMO communication systems) and SSD channels over multiple generations' architectures that have shipped in millions of ASIC chips.

College Station, Tx, Us

► Research Assistant• Probabilistic Graphical Models and Message-Passing Algorithms for Recommender Systems+ Introduced a novel clustering-based message-passing framework for the recovery of a data matrix from incomplete observations associated with recommender systems.+ Modeled the problem using a generative factor graph and proposed a new algorithm, termed IMP, which outperforms other algorithms on real collaborative filtering (e.g., Netflix) data matrices when the fraction of observed entries is very small (i.e., improves the cold-start problem for collaborative filtering systems in practice).+ IMP algorithm was adopted to a restaurant recommendation engine named allTomato of startup NextVerb. allTomato pipes Facebook user's activities (photos, check-ins, and status), leverages the friends' activities as a trust filter, and makes the fraud-resilient personalized recommendations.• LDPC Codes and Iterative Decoding for Channels with Memory+ Solved the joint iterative decoding problem for finite-state channels (FSCs) and low-density parity-check (LDPC) codes by generalizing the linear programming (LP) decoder for binary linear codes to joint decoding of binary-input FSCs.+ Developed a novel convergent iterative solver for the joint LP decoding problem which provides the error floor predictability of LP decoding by pseudo-codeword analysis and also significant gains over BCJR and SOVA based turbo equalization (TE) with a computational complexity of TE.+ The expected application is coding for magnetic storage where the required block-error rate is extremely low and system performance is difficult to verify by simulation.► Teaching Assistant• Taught and prepared for undergraduate digital communications/computer networks labs and graduate channel coding/digital communications recitation sections; graded papers, exams and homework, kept office hours.

San Diego, Ca, Us

Phd, Electrical & Computer Enginnering

Ms/Bs, Electrical Engineering