Jongkuk Lim

Pattern Recognition Researcher

250 Changnyong dae-ro, Suwon, Gyeonggi-do, South Korea, 16229


Keywords: Deep Learning, Machine Learning, Data Science

Frameworks: Tensorflow, Android, OpenCV

Languages: Python, Kotlin, Java, Matlab, C

Work  Experiences

Postdoctoral Researcher @ Dankook University

Mar 2020 - Now, Yongin

  • Light-weight Deep Learning for Edge Computing
  • Computer vision / Inertial sensor

Product Director @ Funiot 

Aug 2015 - Jul 2019, Yongin

Data Scientist @ Neofect 

Oct 2014 - Dec 2016, Yongin

  • Deployed ELK stack (Elasticsearch, Logstash, and Kibana) on AWS and local servers to embrace data growth, and developed python API for elasticsearch
  • Researched on deep learning-based activity recognition of daily livings with wearable sensors for stroke survivors and non-disabled controls


Mar 2014  - Feb 2020, Yongin

Ph.D. Degree in Computer Engineering  


Dankook University

   Thesis: Training-free Anaerobic Workout Tracking via a Wrist-worn Inertial Sensor

Mar 2012 - Feb 2014, Yongin

Master’s Degree in Computer Engineering  


Dankook University

   Thesis: User Independent Gesture Recognition using Accelerometer Sensor

Mar 2010 - Feb 2012, Yongin

Bachelor Degree in Computer Engineering


Dankook University


Aug 2019, ITRC Global Makerthon, IITP

The best idea award

  • One of 28 teams in 43 graduate schools
  • All-in-one solution for the gym owner. Ready Fitness ONE 

Nov 2017, ITRC Global Makerthon, IITP

1st place Minister's award by the Ministry of Science and ICT of Korea

  •  One of 35 teams in 29 graduate schools
  •  Smart IoT group workout solution

Oct 2013, Smart Robot Application Competition, SK Telecom

1st place, Korea Institute for Robot Industry Advancement Director Award

  •  One of 45 teams including professional companies
  •  Early childhood English education with a smart robot


iOS GPU accelerated Object Detection via YOLO v3 TensorFlow Lite


  • Training a model from scratch for a GPU accelerated mobile application

RepNet python command-line interface


  • Command-line interface implementation of RepNet

Deep Learning Start Guide


  • GitHub repository designed to  show the idea of how Deep Learning works for those who never have had experience in Deep Learning

Anonymizing Videos using Dual Shot Face Detector


  • Publishing an open video dataset raises a privacy issue. This project was to solve the privacy issue by anonymizing faces in videos with Deep Learning


Certificate in TensorFlow Development

Google, 2020

  • First acquisition in South Korea

Certified Bodybuilding Instructor

Korean Sports Promotion Foundation, 2019

  • First promotion model of body transformation in a local gym


Deep Learning Summer Course Instructor for Intensive Major

2018- 2020

  • Leading undergraduate students to understand Deep Learning

Design Thinking Course by Stanford and Berkeley Innovation Group

 in the event of  global entrepreneurship of Korea camp

 2016 and


  • Leading teams to solve business problems with creative solutions


  • Jongkuk, L., & Younggeun, C. (2020) uLift: Training-free Workout State, Type, Repetition, And Form Quality Recognition using a Single Wrist-worn Accelerometer Sensor, IEEE Access. (In preparation)
  • Jongkuk, L., Jeonghoon, S., Changho, K., & Younggeun, C. (2018). Efficiency Optimization of Deep Workout Recognition with Accelerometer Sensor for a Mobile Environment. IEEE TENCON 2018
  • Jongkuk, L., Seungyeon, J., & Younggeun, C. (2018). Optimal Low-power Workout State Classification for Multiple Configurations. IEEE 9th International Conference on Information and Communication Technology Convergence
  • Jiyoung, K., & Jongkuk, L. (2018). Storytelling-Based Hand Gesture Interaction in a Virtual Reality Environment. In Advances in Human Factors in Wearable Technologies and Game Design
  • Jiyoung, K., & Jongkuk, L. (2018). Study on Augmented Context Interaction System for Virtual Reality Animation Using Wearable Technology. 7th International Conference on Information Technology Convergence and Services