Karthik Srinivasan

Assistant Professor

University of Kansas

United States

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Dr. Karthik Srinivasan is an Assistant Professor at the University of Kansas, USA. His research areas include sustainability reporting, predictive modeling with incomplete data, enterprise forecasting systems, and machine learning interpretability. He has contributed to recent publications addressing the impact of public sentiment on COVID-19, as well as methods for advanced text analysis and empirical modeling in wellness studies.

Articles (14)

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2025

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2025

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2025

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2024

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2023

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2023

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2023

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2023

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2023

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2022

Collaborators (6)

Jarron Saint Onge

Associate Professor

University of Kansas Medical Center

UNITED STATES

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Sudha Ram

UNITED STATES

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Norma Juma

Brenneman Professor of Business Strategy

Washburn University

UNITED STATES

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Eric Numfor

Associate Professor

Augusta University

UNITED STATES

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A. Townsend Peterson

University Distinguished Professor

University of Kansas

UNITED STATES

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Folashade Agusto

University of Kansas

UNITED STATES

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Karthik Srinivasan

Research Interests

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Articles (14)

Textual Analysis of Sustainability Reports: Topics, Firm Value, and the Moderating Role of Assurance

A Reduced Modeling Approach for Making Predictions with Incomplete Data Having Blockwise Missing Patterns

Dynamic model selection in enterprise forecasting systems using sequence modeling

TextRegress: A Python package for advanced regression analysis on long-form text data

Impact of the COVID-19 pandemic on the stock market and investor online word of mouth

A discrete Bayesian network for analysing hospital discharge data

A trans-national comparison of stock market movements and related social media chatter during the COVID-19 pandemic

A Human-in-the-Loop Segmented Mixed-Effects Modeling Method for Analyzing Wearables Data

Examining Disease Multimorbidity in U.S. Hospital Visits Before and During COVID-19 Pandemic: A Graph Analytics Approach

Discovery of associative patterns between workplace sound level and physiological wellbeing using wearable devices and empirical Bayes modeling

Impact of public sentiments on the transmission of COVID-19 across a geographical gradient

ROLEX: A Novel Method for Interpretable Machine Learning Using Robust Local Explanations

MoreThanSentiments: A text analysis package

ROLEX: A Novel Method for Interpretable Machine Learning Using Robust Local Explanations

Collaborators (6)

Jarron Saint Onge

Sudha Ram

Norma Juma

Eric Numfor

A. Townsend Peterson

Folashade Agusto

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