Exploring the science of data and the art of intelligence
About me
About me
About me
I'mMohithaVelagapudi,aSoftwareEngineerandResearcherspecializinginDataScienceandAI.
I'mMohithaVelagapudi,aSoftwareEngineerandResearcherspecializinginDataScienceandAI.
I'mMohithaVelagapudi,aSoftwareEngineerandResearcherspecializinginDataScienceandAI.
Core Competencies
Research and Publications
Audio Driven Detection of Hate Speech in Telugu: Toward Ethical and Secure CPS
Low-resource multimodal hate speech detection leveraging acoustic and textual representations for robust moderation in Telugu.
Accepted for Publication in
Springer
Domain
Speech Processing
Audio Driven Detection of Hate Speech in Telugu: Toward Ethical and Secure CPS
Low-resource multimodal hate speech detection leveraging acoustic and textual representations for robust moderation in Telugu.
Accepted for Publication in
Springer
Domain
Speech Processing
Audio Driven Detection of Hate Speech in Telugu: Toward Ethical and Secure CPS
Low-resource multimodal hate speech detection leveraging acoustic and textual representations for robust moderation in Telugu.
Accepted for Publication in
Springer
Domain
Speech Processing
Exploring Kolmogorov Arnold Networks for Interpretable Mental Health Detection and Classification from Social Media Text
This paper introduces Kolmogorov Arnold Networks (KANs) for highly accurate and interpretable mental health detection from social media text, outperforming MLPs and SVMs with fewer parameters.
Published by
ACL Anthology
Domain
Natural Language Processing
Exploring Kolmogorov Arnold Networks for Interpretable Mental Health Detection and Classification from Social Media Text
This paper introduces Kolmogorov Arnold Networks (KANs) for highly accurate and interpretable mental health detection from social media text, outperforming MLPs and SVMs with fewer parameters.
Published by
ACL Anthology
Domain
Natural Language Processing
Exploring Kolmogorov Arnold Networks for Interpretable Mental Health Detection and Classification from Social Media Text
This paper introduces Kolmogorov Arnold Networks (KANs) for highly accurate and interpretable mental health detection from social media text, outperforming MLPs and SVMs with fewer parameters.
Published by
ACL Anthology
Domain
Natural Language Processing
YOLOv10 for Enhanced Trypanosome Detection
This research leverages the cutting-edge YOLOv10 model, fine-tuned on the largest Tryp dataset, to significantly advance early and accurate detection of trypanosome parasites in unstained blood smears.
Accepted for Publication in
River Publishers
Domain
Computer Vision
YOLOv10 for Enhanced Trypanosome Detection
This research leverages the cutting-edge YOLOv10 model, fine-tuned on the largest Tryp dataset, to significantly advance early and accurate detection of trypanosome parasites in unstained blood smears.
Accepted for Publication in
River Publishers
Domain
Computer Vision
YOLOv10 for Enhanced Trypanosome Detection
This research leverages the cutting-edge YOLOv10 model, fine-tuned on the largest Tryp dataset, to significantly advance early and accurate detection of trypanosome parasites in unstained blood smears
Accepted for Publication in
River Publishers
Domain
Computer Vision
Enhancing Power Quality Disturbance Classification through Ensemble Learning and Statistical Techniques
This paper presents a highly effective framework for power quality disturbance classification using a novel statistical feature extraction method combined with gradient boosting for feature selection and ensemble learning.
Published by
IEEE
Domain
Machine Learning
Enhancing Power Quality Disturbance Classification through Ensemble Learning and Statistical Techniques
This paper presents a highly effective framework for power quality disturbance classification using a novel statistical feature extraction method combined with gradient boosting for feature selection and ensemble learning.
Published by
IEEE
Domain
Machine Learning
Enhancing Power Quality Disturbance Classification Through Ensemble Learning and Statistical Techniques
This paper presents a highly effective framework for power quality disturbance classification using a novel statistical feature extraction method combined with gradient boosting for feature selection and ensemble learning.
Published by
IEEE
Domain
Machine Learning
My Journey
From building AI models in college to researching at UCSC, I now build reliable backend systems at Guidewire Software.
Amrita University
Earned a Bachelor’s degree in Computer Science with a specialization in Artificial Intelligence. (2021 - 2025)
CGPA: 8.89/10
First Class with distinction
AI Researcher
Software Engineer
Amrita University
Earned a Bachelor’s degree in Computer Science with a specialization in Artificial Intelligence. (2021 - 2025)
CGPA: 8.89/10
First Class with distinction
AI Researcher
Summer Research Intern – University of California, Santa Cruz (UCSC)
Social Computing
LLM
Software Engineer
Currently working as a Go Backend Developer at Guidewire Software.
Go
Microservices
Amrita University
Earned a Bachelor’s degree in Computer Science with a specialization in Artificial Intelligence. (2021 - 2025)
CGPA: 8.89/10
First Class with distinction
AI Researcher
Summer Research Intern – University of California, Santa Cruz (UCSC)
Social Computing
LLM
Software Engineer
Currently working as a Go Backend Developer at Guidewire Software.
Go
Microservices
