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Jinhang Jiang on Muck Rack

Jinhang Jiang

Chicago, Tempe
As seen in: Medium, Taylor & Francis Online, Towards Data Science
Covers:  Network Analysis, Machine Learning, Natural Language Processing, Data Science in General
Pricing Data Scientist at Walmart Lab | ASU MSBA 21’ | KU BSBBA 20'

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Jinhang Jiang’s Biography

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Graduated from the W.P.Carey School of Business focusing on Business Analytics with a B.S. in Business Analytics from The University of Kansas.

Experienced in in text mining, manipulating large datasets, regression analyses, classification analyses, computer vision and deep learning.

Jinhang Jiang’s Journalist Portfolio

Jinhang Jiang - Medium

Jinhang Jiang - Medium

Blogs

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Visualize High-Dimensional Network Data with 3D 360-Degree-Animated Scatter Plot

Visualize High-Dimensional Network Data with 3D 360-Degree-Animated Scatter Plot

We frequently encounter extremely complicated data that are unreadable or uninterpretable in the context of n…

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Articles

Gradients in articular cartilage and cartilage tissue engineering

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By Wei Zhou, Jinhang Jiang, Yong-chang Yao
| Taylor & Francis Online
Abstract Articular cartilage (AC) possesses complex depth-dependent gradients in cell phenotype, matrix composition, metabolism, mechanics, and growth factors. These natural gradients are essential for cartilage function. Because of lack of blood vessels, the repair and treatment of AC defects have been a grand challenge for surgeons, the natural gradients inspired researchers to find solutioninthe gradient construction for AC repair in tissue engineering.
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From Scratch to Deep Quantile Forecasting Original

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By Jinhang Jiang
| Towards Data Science
Short Introduction Data Build a Toy Version of Quantile Recurrent Forecaster Quantile Forecasting with the State-of-Art Models Zero-shot Quantile Forecast with LLMs Conclusion Quantile forecasting is a statistical technique used to predict different quantiles (e.g., the median or the 90th percentile) of a response variable's distribution, providing a more comprehensive view of potential future outcomes.
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Graph Analytics in Identifying Enduring Effects of Pandemics

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By Jinhang Jiang
| Towards Data Science
Graph analytics framework designed for identifying enduring effects of COVID-19 on Healthcare Systems using Exponential Random Graph Models (ERGMs) Introduction The Graph Analytics Framework Explanatory Modeling with Exponential Random Graph Models Implementation of ERGMs Conclusion One of our recent studies published in the ACM Transactions on Management Information Systems (ACM TMIS) has shown the potential of graph analytics in identifying the enduring effects of the COVID-19 pandemic.
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