$$ \newcommand{\floor}[1]{\left\lfloor #1 \right\rfloor} \newcommand{\ceil}[1]{\left\lceil #1 \right\rceil} \newcommand{\N}{\mathbb{N}} \newcommand{\R}{\mathbb{R}} \newcommand{\Z}{\mathbb{Z}} \newcommand{\Q}{\mathbb{Q}} \newcommand{\C}{\mathbb{C}} \renewcommand{\L}{\mathcal{L}} \newcommand{\x}{\times} \newcommand{\contra}{\scalebox{1.5}{$\lightning$}} \newcommand{\inner}[2]{\left\langle #1 , #2 \right\rangle} \newcommand{\st}{\text{ such that }} \newcommand{\for}{\text{ for }} \newcommand{\Setcond}[2]{ \left\{\, #1 \mid #2 \, \right\}} \newcommand{\setcond}[2]{\Setcond{#1}{#2}} \newcommand{\seq}[1]{ \left\langle #1 \right\rangle} \newcommand{\Set}[1]{ \left\{ #1 \right\}} \newcommand{\set}[1]{ \Set{#1} } \newcommand{\sgn}{\text{sign}} \newcommand{\halfline}{\vspace{0.5em}} \newcommand{\diag}{\text{diag}} \newcommand{\legn}[2]{\left(\frac{#1}{#2}\right)} \newcommand{\ord}{\text{ord}} \newcommand{\di}{\mathrel{|}} \newcommand{\gen}[1] \newcommand{\irr}{\mathrm{irr }} \renewcommand{\deg}{\mathrm{deg }} \newcommand{\nsgeq}{\trianglelefteq} \newcommand{\nsg}{\triangleleft} \newcommand{\argmin}{\mathrm{argmin}} \newcommand{\argmax}{\mathrm{argmax}} \newcommand{\minimize}{\mathrm{minimize}} \newcommand{\maximize}{\mathrm{maximize}} \newcommand{\subto}{\mathrm{subject\ to}} \newcommand{\DKL}[2]{D_{\mathrm{KL}}\left(#1 \di\di #2\right)} \newcommand{\ReLU}{\mathrm{ReLU}} \newcommand{\E}{\mathsf{E}} \newcommand{\V}{\mathsf{Var}} \newcommand{\Corr}{\mathsf{Corr}} \newcommand{\Cov}{\mathsf{Cov}} \newcommand{\covariance}[1]{\Cov\left(#1\right)} \newcommand{\variance}[1]{\V\left[#1\right]} \newcommand{\variancewith}[1]{\V\left[#1\right]} \newcommand{\expect}[1]{\E\left[#1\right]} \newcommand{\expectwith}[2]{\E_{#1}\left[#2\right]} \renewcommand{\P}{\mathsf{P}} \newcommand{\uniform}[2]{\mathrm{Uniform}\left(#1 \dots #2\right)} \newcommand{\gdist}[2]{\mathcal{N}\left(#1, #2\right)} \DeclarePairedDelimiter{\norm}{\lVert}{\rVert} $$ \everymath{\displaystyle}

Wonseok Shin

Scientist @ Standigm Inc.

I am currently working as a Scientist at Standigm, working on AI for drug discovery.

My research interests span methods for understanding and analyzing large-scale data, including machine learning, data mining and efficient algorithms. While I appreciate the rigor and beauty of theoretical approaches, I am also deeply passionate about applying these techniques to solve real-world problems.

Previously, I obtained my Master’s degree in Computer Science from Seoul National University (SNU), where I studied under the guidance of Prof. Kunsoo Park. My research during my Master’s program focused on efficient algorithms for large-scale graph data management and data mining, with a thesis centered on accurate and efficient estimation of subgraph counts.

Before that, I completed my Bachelor’s degree in Computer Science and Mathematical Sciences (double major) at SNU.

For further information about me, please refer to my Curriculum Vitae.

Recent Updates
Sep 2, 2024 I started as an AI researcher at Standigm Inc. Excited for this new chapter and the work ahead!
Aug 29, 2024 I received my Master’s in Computer Science with the Best Thesis Award. I’ll dearly miss my time at SNU and am forever grateful for the support of my labmates and my advisor, professor Kunsoo Park.
Jul 31, 2024 I will be giving a talk at 2024 Korea–Japan Joint Workshop on Algorithms and Computation (August 02, 2024) on Approximate Counting of Subgraph Isomorphisms.
May 20, 2024 I will be giving a talk at Top Conference Session of Korea Computer Congress 2024, on our VLDB24 paper.
Apr 15, 2024 Our paper “Cardinality Estimation of Subgraph Matching: A Filtering-Sampling Approach” is accepted to VLDB 2024
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