Want to hire this expert for a project? Request a quote for free.
Profile Details
Create Project
USD 68 /hr
Hire Dr. Dan L.
United States
USD 68 /hr
Data Scientist | Ph.D. in informatics | Visualization | Analysis | AI | 7+ years
Profile Summary
Subject Matter Expertise
Services
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
Work Experience
Data Scientist
Milo Workshop LLC
January 2025 - Present
Assistant Professor
Baruch College
August 2023 - Present 
ASSISTANT PROFESSOR
City University of New York
August 2023 - January 2025
Data Analyst (Consultant)
Indiana University Bloomington
August 2021 - August 2023
Education
Ph.D. (Informatics)
Indiana University Bloomington
August 2020 - June 2023
Ph.D. (History and Philosophy of Science and Medicine)
Indiana University Bloomington
August 2018 - June 2023
PhD in Informatics
Indiana University Bloomington
August 2018 - June 2023
Certifications
-
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_Rt5qNdRhM2v6CsoJanuary 2025 - Present
Publications
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>
<META HTTP-EQUIV="Refresh" CONTENT="0;URL=/servlet/useragent">
</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" .
Ratings and Comments
Total Projects Awarded:
Comments: