Sungsu Lim


I am currently working as a Senior Data Scientist at Tonal .

I have research experience in different areas of machine learning, including reinforcement learning and computer vision. I am interested in solving everyday challenges to make products and services more efficient. I believe technical skills to apply abstract knowledge to real problems are becoming more crucial.

My life so far has been a serendipitous journey. Born and brought up in Korea, I was fortunate to have lived in various countries including China, India, Hong Kong, U.S., and now Canada. I am excited to discover what my remaining journey looks like and live a fulfilling life helping others.

'사람이 마음으로 자기의 길을 계획할지라도 그의 걸음을 인도하시는 이는 여호와시니라.'
(In their hearts humans plan their course, but the Lord establishes their steps) - Proverbs 16:9

Contact : ss.sungsulim_at_gmail_dot_com

[Last updated: Mar 21, 2023]

Experience

Senior Data Scientist

Tonal [link]
  • Working as a full-stack data scientist from data collection and research to building machine learning models in production.
  • Building intelligent features such as personalized weight suggestions (patented), form feedback, and content recommendations.
  • Working within a tight-knit cross-functional team for rapid feature development.
10.2020 - Present
Toronto, Canada

Research Assistant

University of Alberta
  • Research focus on control methods in reinforcement learning.
05.2018 - 09.2020
Edmonton, Canada

Associate Researcher

Noah's Ark Lab, Huawei [link]
  • Worked on an applied project to increase automation efficiency using reinforcement learning (subject to NDA).
  • Developed deep continuous control models using PyTorch.
05.2019 - 08.2019 (3 mo.)
Edmonton, Canada

Machine Learning Intern

Hive [link]
  • Developed face and logo detection/recognition models for providing comprehensive media analytics on celebrity/brand exposure across TV channels.
  • Worked on customizing deep learning models (Single-Shot Detector, faster R-CNN, R-FCN) and improving data preprocessing procedure using TensorFlow and Caffe.
05.2017 - 08.2017 (3 mo.)
San Francisco, U.S.

Computer Vision Research Intern

SenseTime [link]
  • Worked for the research team at a unicorn startup developing core computer vision algorithms for various clients around the world.
  • Worked on fast depth perception, computing 3D depth and its confidence map simultaneously from stereo images by extending traditional BRIEF feature descriptor to multi-scale.
06.2016 - 12.2016 (7 mo.)
Shatin, Hong Kong

Education

University of Alberta

PhD in Computing Science
Advisor : Prof. Martha White and Prof. Adam White
Voluntary withdrawal to work more closely in the industry

09.2019 - 05.2020
Edmonton, Canada

University of Alberta

MSc (Research) in Computing Science
Advisor : Prof. Martha White
  • Research focus on control methods in reinforcement learning.
  • Worked as Research/Teaching Assistant (Fall 2017 - Summer 2019)
  • Developed assignments for Reinforcement Learning Specialization in Coursera
09.2017 - 08.2019
Edmonton, Canada

The Hong Kong University of Science and Technology

BEng in Computer Science
Final Thesis Advisor : Prof. James Kwok
  • Graduated with First Class Honors
  • Leave for mandatory military service in Korea (05.2013 - 02.2015, 21 mo.)
  • School of Engineering Student Ambassador (2012-2013)
  • Overseas Exchange Program (Northwestern University, Fall 2012)
09.2011 - 12.2016
Kowloon, Hong Kong

American Embassy School

IB Diploma
09.2009 - 05.2011
New Delhi, India

Publications and Preprints

  • Neumann S., Lim S. , Joseph A., Pan Y., White A., White M., Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement . Accepted to ICLR, 2023
    [paper]
  • Chan A., Silva H., Lim S. , Kozuno T., Mahmood A. R., White M., Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences . Accepted to JMLR, 2022
    [paper]
  • Satsangi Y., Lim S. , Whiteson S., Oliehoek F., White M., Maximizing Information Gain in Partially Observable Environments via Prediction Rewards . Accepted to AAMAS, 2020
    [paper]
  • Yasui N., Lim S. , Linke C., White A., White M., An Empirical and Conceptual Categorization of Value-based Exploration Methods . Accepted to Exploration in Reinforcement Learning Workshop. ICML, 2019
    [paper]
  • Lim S. , Joseph A., Le L., Pan Y., White M., Actor-Expert: A Framework for using Action-Value Methods in Continuous Action Spaces . Accepted to Deep Reinforcement Learning Workshop. NeurIPS, 2018
    [paper] [poster]
  • Lim S. , Tai Y., Ren J., Yan S., Liang Y., BRIEF Stereo: Fast Stereo Matching using Multiscale Binary Feature . Work done at SenseTime, 2016
    [paper]

Projects

RLControl: Collection of Reinforcement Learning Algorithms in Continuous Action Space

[code]

  • This repository contains various reinforcement learning algorithms, including value-based and policy gradient methods for continuous action spaces. This code base was also used for my Masters Thesis.

ArmStrong: Smart Coaching Mat with Real-time Feedback

[link]

  • Interdisciplinary Global Product Development project in collaboration with students from different universities in Hong Kong, Korea, and China.
  • The coaching mat keeps track of the number of push-ups and also gives feedback if the forces on the hands are uneven. The feedback is given through a mobile app connected via bluetooth.
  • Used Arduino with ultrasonic sensors, pressure sensors, and bluetooth module.

SmartWalk: Fall-detection Shoe for the Elderly

[link]

  • Summer Design Thinking project in collaboration with China Academy of Arts.
  • Based on the theme of 'the elderly', we developed a prototype shoe that detects if the wearer falls down and alerts family members.
  • Used Arduino with accelerometer and gyroscope.