Debaditya Chakraborty

Assistant Professor

University of Texas at San Antonio

United States

Research Interests

Explore related searches

Professors in United States

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Debaditya Chakraborty is an Assistant Professor at the University of Texas at San Antonio, USA. His research focuses on the application of Explainable Artificial Intelligence in various domains, including climate change mitigation strategies, cancer therapeutics, and environmental sustainability. Recent articles authored by him explore glacier mass changes, aquifer sustainability, and the impact of institutional quality on CO2 emissions.

Articles (20)

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Year:

2020

Collaborators (7)

Cristian Rodríguez-Aguayo

The University of Texas MD Anderson Cancer Center

UNITED STATES

View details

Marcio Giacomoni

Full Professor

University of Texas at San Antonio

UNITED STATES

View details

Sandeep Langar

Assistant Professor

University of Texas at San Antonio

UNITED STATES

View details

Yidong Bai

UT Health San Antonio

UNITED STATES

View details

Ali Mirchi

Oklahoma State University

UNITED STATES

View details

Cristina Ivan

Assistant Professor RFA

The University of Texas MD Anderson Cancer Center

UNITED STATES

View details

Hazem Elzarka

Professor

University of Cincinnati

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Debaditya Chakraborty

Research Interests

Explore related searches

Contact this professor

About

Articles (20)

Interpretation of glacier mass change within the Upper Yukon Watershed from GRACE using Explainable Automated Machine Learning Algorithms

Efficacy of mitigation strategies for aquifer sustainability under climate change

Discovering novel prognostic biomarkers of hepatocellular carcinoma using eXplainable Artificial Intelligence

Discovering genetic biomarkers for targeted cancer therapeutics with eXplainable Artificial Intelligence

Discovering genetic biomarkers for targeted cancer therapeutics with eXplainable Artificial Intelligence

Discovering Novel Prognostic Biomarkers of Hepatocellular Carcinoma using eXplainable Artificial Intelligence

Does institutional quality affect CO2 emissions? Evidence from explainable artificial intelligence models

Does institutional quality affect CO2 emissions? Evidence from explainable artificial intelligence models

Heuristic data-inspired scheme to characterize meteorological and groundwater droughts in a semi-arid karstic region under a warming climate

Heuristic data-inspired scheme to characterize meteorological and groundwater droughts in a semi-arid karstic region under a warming climate

A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications

Using Artificial Neural Network (ANN) for Short-Range Prediction of Cotton Yield in Data-Scarce Regions

Artificial Intelligence for Developing Accurate Preliminary Cost Estimates for Composite Flooring Systems of Multi-Storey Buildings

Explainable AI reveals new hydroclimatic insights for ecosystem-centric groundwater management

An explainable machine learning model to predict and elucidate the compressive behavior of high-performance concrete

Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer

Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence

Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling

Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework

A novel construction cost prediction model using hybrid natural and light gradient boosting

Collaborators (7)

Cristian Rodríguez-Aguayo

Marcio Giacomoni

Sandeep Langar

Yidong Bai

Ali Mirchi

Cristina Ivan

Hazem Elzarka

Make your next move.