Vara Prasad Gudi
Graduate Researcher
Graduate Researcher
I am a driven software engineer and AI researcher passionate about leveraging technology to solve real-world problems. With hands-on experience in developing AI-driven solutions at Boehringer Ingelheim and Ecolab, I have automated processes, reduced manual workloads, and optimized system efficiency. Currently pursuing my Master's in Computer Science at UMass Amherst, I specialize in Machine Learning, Distributed Systems, and AI applications. My portfolio showcases cutting-edge projects that merge innovation with societal impact—explore how I transform ideas into scalable solutions for the greater good.
Developed AI-driven automation solutions using Python, Flask, and Databricks for process optimization, object detection and classification. Designed scalable web portals with NextJS, TypeScript, and PostgreSQL, and supported the implementation of local LLMs with LangChain and GPT-Turbo for RAG workflows. Designed REST APIs, integrated CI/CD pipelines with Jenkins, Docker, and SonarQube, enhancing operational efficiency and system reliability.
Developed and automated end-to-end data pipelines integrating Azure SQL DB, Snowflake, SharePoint, and SAP using Python, Azure Logic Apps, and APIs. Designed and deployed machine learning models using Azure ML to forecast supply-demand trends and optimize inventory. Performed data cleaning and transformation on large datasets with SQL and Jupyter Notebooks, automating workflows using Azure Function Apps and CI/CD pipelines. Created dynamic visualizations with Power BI and Microsoft Power Platform to support decision-making.
Developed and containerized microservices using PHP Laravel, Docker, and Kubernetes for a scalable video management system with YouTube API integration. Implemented real-time video communication leveraging React, WebRTC, and Agora API to enhance platform responsiveness and user experience.
Answered student queries over email. Interacted with students for any Academic help and report it to core committee. Occasionally interacted with core committee to give suggestions regarding Academics.
Handled Projects like Authentication Bypass, Scanning using OWASP ZAP, Scanning & Attacking for Open ports and worked on System Hacking and Exploiting Server Vulnerabilities. Also, into Hands-on Training on various Ethical hacking concepts and Kali Linux Tools.
Collaborated with the technical lead to deliver technology solutions and community support. Played a key role in organizing technical events such as WebHunt, Capture The Flag, and CyberPunk, fostering engagement in cybersecurity and software development. Contributed technical blogs and provided hands-on assistance in skill development, emphasizing web technologies and cybersecurity practices.
This research, under Prof. Mohit Iyyer, evaluates multiple strategies to enhance Out-of-Distribution (OOD) robustness using fine-tuned and pre-trained models such as BERT, RoBERTa, and Llama. By implementing zero-shot, k-shot, few-shot, chain-of-thought prompting, and explanation-based techniques, a 20-30% improvement in OOD accuracy over supervised fine-tuning baselines was achieved. Additionally, 4-bit quantization for memory optimization was integrated using Unsloth, maintaining performance with explanation-based prompting. The findings of the research have been submitted to the 14th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis.
Published a chapter in the book "Explainable Artificial Intelligence (XAI): Concepts, Enabling Tools, Technologies and Applications," published by The IET Digital Library, provides a comprehensive insight into the role of XAI in the medical field. With its Chapter DOI: 10.1049/PBPC062E_ch, it emphasizes the necessity for transparency in AI systems among healthcare professionals. The chapter thoroughly explores applications in areas such as medical image analysis with deep learning, clinical decision support, and broader healthcare. It also highlights XAI's role in enhancing trust in healthcare, with in-depth discussions on its integration in healthcare frameworks for pandemic prevention, and specific medical applications like allergy diagnosis. This research significantly contributes to the understanding of how XAI can be a transformative force in medical science, emphasizing the importance of explainability in the deployment of AI solutions for improved health outcomes.
This research, conducted under the guidance of Assistant Prof. Sheela Jayachandran at the School of Computer Science and Engineering (SCOPE) at VIT-AP, presents a groundbreaking Python-based machine learning algorithm for image analysis. The Fuzzy Image Clustering via Featured Interval Extraction algorithm excels with a 100% accuracy rate in overlap distance evaluation, significantly outperforming existing methods in datasets with high overlap. Its versatility and precision in multivariate statistical applications not only mark a significant advancement in the field but also earned recognition through a utility patent (IP India Application No. 202341029896). This work highlights the potential for enhancing image analysis techniques and contributes substantially to the research community.
This research, under Assistant Prof. Sheela Jayachandran at VIT-AP's School of Computer Science and Engineering, has significantly advanced waste management technology. By integrating the YOLOv4 algorithm with IoT, the team developed a system that not only surpasses previous models in accuracy but also improves efficiency. This innovation led to a notable 10% increase in detection precision and a 12% boost in processing speed, showcasing a scalable solution that melds deep learning with environmental sustainability. The impact of this groundbreaking work is further acknowledged through the publication of a design patent (IP India Application No. 202341070187), marking a milestone in sustainable technology development.
This research, led by Associate Prof. Somya Ranjan Sahoo at VIT-AP's SCOPE, innovatively applies fuzzy logic to traffic control systems, resulting in a 30% improvement in traffic flow and a 20% reduction in vehicle wait times. The study involved a Pygame simulation of a complex four-way intersection, showcasing its potential in urban traffic management. The team's findings, demonstrating a 45% efficiency increase over static systems, were recognized for their significance in urban planning and smart cities, culminating in a publication in an IEEE journal.