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プロフィール詳細
Dr. Dan L.に依頼
United States
Data Scientist | Ph.D. in informatics | Visualization | Analysis | AI | 7+ years
プロフィール概要
専門分野
サービス
Writing
Technical Writing
Research
Scientific and Technical Research,
Systematic Literature Review
Consulting
Scientific and Technical Consulting
Data & AI
Predictive Modeling,
Statistical Analysis,
Data Visualization,
Text Mining & Analytics,
Data Insights
職務経験
Data Scientist
Milo Workshop LLC
1月 2025 - 現在
Assistant Professor
Baruch College
8月 2023 - 現在 
ASSISTANT PROFESSOR
City University of New York
8月 2023 - 1月 2025
Data Analyst (Consultant)
Indiana University Bloomington
8月 2021 - 8月 2023
学歴
Ph.D. (Informatics)
Indiana University Bloomington
8月 2020 - 6月 2023
Ph.D. (History and Philosophy of Science and Medicine)
Indiana University Bloomington
8月 2018 - 6月 2023
PhD in Informatics
Indiana University Bloomington
8月 2018 - 6月 2023
認定資格
-
Momentum Bootcamp in Climate Data Science Momentum Bootcamp in Climate Data Science
NSF Science Technology Center Learning the Earth with Artificial Intelligence and Physics
https://www.linkedin.com/in/dan-li-ph-d-24947528/overlay/1738855507659/single-media-viewer/?profileId=ACoAAAXDihkBM6R1E9MRp28_Rt5qNdRhM2v6Cso1月 2025 - 現在
出版物
JOURNAL ARTICLE
Ryan J. O'Loughlin, Dan Li, Richard Neale, Travis A. O'Brien (2025). Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling . Geoscientific Model Development.
Dan Li (2023). Machines Learn Better with Better Data Ontology: Lessons from Philosophy of Induction and Machine Learning Practice . Minds and Machines.
If a tree grows no ring and no one is around: how scientists deal with missing tree rings <head>
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</head> . Climatic Change.
Ryan O’Loughlin, Dan Li (2022). Model robustness in economics: the admissibility and evaluation of tractability assumptions . Synthese.
Ryan O'Loughlin and Dan Li(2022). Model robustness in economics: the admissibility and evaluation of tractability assumptions . Synthese. 200. (1). Springer Science and Business Media {LLC}
PREPRINT
Ryan O'Loughlin, Dan Li, Travis O'Brien (2024). Moving beyond post-hoc XAI: Lessons learned from dynamical climate modeling .
Ryan O'Loughlin, Dan Li, Travis O'Brien (2024). Supplementary material to "Moving beyond post-hoc XAI: Lessons learned from dynamical climate modeling" .
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