Juyoung Wang

Data Scientist

acrossB

Biography

Juyoung Wang (왕주영, 王主榮) is a data scientist at acrossB. Academically, he is a Ph.D. dropout (after one year of study) and Master of Applied Science degree (supervisors: Merve Bodur and Mucahit Cevik) holder from Industrial and Systems Engineering at University of Toronto . He obtained his Bachelor’s degree from Institute for Mathematical and Computational Engineering of Pontifical Catholic University of Chile.

His research interests include both mathematical optimization and statistics, and their intersections such as statistical learning (both non-deep and deep learning) and explainable AI. Juyoung also has worked on a variety of applied topics such as healthcare, sales time series forecasting applied to revenue optimization, transportation and logistics.

Download my resumé.

Download my short portfolio.

Interests

Data-driven Stochastic Optimization.
Operations Research.
Statistical Learning.

Education

Ph.D. student in Industrial and Systems Engineering (Dropout), 2022
University of Toronto
M.A.Sc. in Industrial and Systems Engineering, 2021
University of Toronto
B.Sc. in Mathematical and Computational Engineering, 2017
Pontifical Catholic University of Chile

Skills

Python

Scikit-learn, Tensorflow, Pytorch, among others

Statistical analysis

Julia

JuMP, SDDP.jl

Mathematical optimization

Commercial solvers (CPLEX, Gurobi, CP optimizer), Non-commercial sovlers (SCIP, GLPK), Google OR Tools, (Deterministic/Stochastic) (Non)linear optimization, Dynamic programming, among others.

Statistics

Statistical learning, Sampling, Simulation, Time-series forecasting, among others.

Language

Korean, English (TOEFL iBT 110/120) and Spanish.

Experience

Data Scientist

acrossB

Sep 2022 – Present Seoul, Korea

Alternative military service.

Data Scientist

The Bank of Nova Scotia (Scotiabank)

Jul 2018 – Nov 2018 Santiago, Chile

Responsibilities include:

Campaign management
Data analysis and statistics
Modelling

Consultant

Groupe SII

Apr 2018 – Jun 2018 Santiago, Chile

Worked as a data science consultant, together with digital banking team and business intelligence team of Scotiabank.

Accomplishments

Practical Time Series Analysis

Coursera Oct 2022

Practical Time Series Analysis course completion certificate.

See certificate

Natural language processing with classification and vector spaces

Coursera Jul 2022

Natural Language Processing specialization course completion certificate.

See certificate

Fundamentals of Reinforcement Learning

Coursera May 2021

Reinforcement Learning specialization course completion certificate.

See certificate

Build Basic Generative Adversarial Networks (GANs)

Coursera Nov 2020

Generative Adversarial Networks (GANs) specialization course completion certificate.

See certificate

Deep Learning

Coursera Dec 2019

Deep Learning specialization completion certificate. The certificate is awared for those who completed the following five courses: Neural Networks and Deep Learning, Improving Deep Neural Networks - Hyperparameter Tuning, Regularization and Optimization, Structuring Machine Learning Projects, Convolutional Neural Networks, Sequence Models.

See certificate

Publications

Quickly discover relevant content by filtering publications.

Cheng Chi, Amine Mohamed Aboussalah, Elias B Khalil, Juyoung Wang, Zoha Sherkat-Masoumi (2022). A Deep Reinforcement Learning Framework For Column Generation. Proceedings of the 36th Annual Conference on Advances in Neural Information Processing Systems (NeurIPS 2022).

Cite Link

Ozan Ozyegen, Juyoung Wang, Mucahit Cevik (2022). DANLIP: Deep Autoregressive Networks for Locally Interpretable Probabilistic Forecasting. To be submitted.*.

Cite

Merve Bodur, Mucahit Cevik, Andre Cire, Mark Ruschin, Juyoung Wang (surname-based ordering of the authorship) (2022). Multistage Stochastic Fractionated Intensity Modulated Radiation Therapy Planning. Major revision in Computers and Operations Research.

Cite Link

Juyoung Wang, Mucahit Cevik, Saman Hassanzadeh Amin, Amir Ali Parsaee (2021). Mixed-integer linear programming models for the paint waste management problem. Transportation Research Part E: Logistics and Transportation Review.

Cite Link

Juyoung Wang (2021). Multistage stochastic programming combined with deep learning-based time series forecasting: new methodologies and applications.

Cite Link

Juyoung Wang, Mucahit Cevik, Merve Bodur (2021). On the impact of deep learning-based time-series forecasts on multistage stochastic programming policies. INFOR: Information Systems and Operational Research.

Cite Link