Hongsup Shin
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All (13)
EDA (1)
Interoperability (1)
ML (10)
MLOps (1)
MLflow (1)
causality (2)
collaboration (1)
conference (6)
criminal justice (1)
data preprocessing (1)
education (1)
ethics (1)
explainability (3)
fairness (3)
governance (1)
journalism (3)
language models (1)
measurement (1)
responsible AI (4)
robustness (1)
verification (1)
visualization (1)
volunteering (2)

Interoperability testing for hyperparameter tuning: MLflow, LightGBM, sklearn, and dask-ml

ML
MLOps
MLflow
Interoperability

MLflow autologging allows monitoring LightGBM training loss during model training. This behavior is not always expected when we use scikit-learn and dask to tune LightGBM models. This notebook describes how the unexpected behavior manifests and explains some gotchas when using these tools together.

Mar 17, 2023

Comparing type inference methods for mixed data arrays

ML
data preprocessing

Pandas have two type inference methods. Let’s compare the methods by inferring data types for mixed data type arrays.

Mar 2, 2023

Tech volunteering tips for nonprofits

volunteering
journalism
ML

Lessons I’ve learned from my own experience by working with various nonprofit organizations such as DataKind and Texas Justice Initiative

May 25, 2021

Police shooting in Texas 2016-2019

criminal justice
visualization
EDA
volunteering
journalism

Jupyter Notebook on police shooting analysis in Texas from 2016 to 2019 (done in collaboration with Texas Justice Initiative)

May 24, 2021

FAccT 2021. Journalism, data leverage, education, and language models

conference
journalism
measurement
education
language models
responsible AI
ML

Summary of Day 3 at FAccT 2021. Julian Anguin’s Markup, language models, measurements, and data average

Mar 10, 2021

FAccT 2021. Automated decision-making, causal accountability, and robustness

conference
explainability
causality
robustness
responsible AI
ML

Summary of Day 2 at FAccT 2021. Automated decision-making, accountability and recourse, and model robustness

Mar 9, 2021

FAccT 2021. AI audit, governance, and trustworthiness

conference
governance
explainability
responsible AI
ML

Summary of Day 1 at FAccT 2021. Algorithm audit, impact assessment, data governance, trust in AI, and explainable AI

Mar 8, 2021

Tutorials at FAccT 2021

conference
causality
fairness
explainability
responsible AI
ML

FAccT 2021 (virtual) tutorial summary. Causal analysis, XAI, and algorithmic impact

Mar 4, 2021

Markdown and GitHub for scientific writing

collaboration

How to use GitHub to publish and review academic manuscripts for better tracking, communication, and transparency

Nov 24, 2020

Efficient bug discovery with ML for hardware verification

ML
verification

My Arm Research blog post about using ML in hardware engineering to make verification more compute-efficient

Sep 22, 2020

Critiquing and rethinking at ACM FAT 2020

conference
fairness
ML

Summary of FAT 2020 in Barcelona, Spain. “Critiquing and rethinking” was their new attempt to open up a discussion between multidisciplinary stakeholders

Mar 1, 2020

Reflection on USF tech policy and data ethics workshop

ethics

A reflection piece about the USF tech ethics and policy workshop focusing on data ethics as a tech worker myself

Jan 30, 2020

Fairness in ML at ACM FAT 2019

conference
fairness
ML

Several key moments from the conference and my thoughts at the FAT (Fairness, Accountability, and Transparency in ML) conference

Feb 1, 2019
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