| CARVIEW |
Hello!
I'm Neil Sharma
PhD Student in Computer Science
I am a first year PhD student at the University of Oregon (UO) advised by Prof. Suyash Gupta. I am studying Federated Learning and privacy-preserving techniques in federated learning, especially using Differential Privacy. Before joining UO, I worked at Cisco as a Production Engineer on the Webex team, and completed a research fellowship at ISRO (Indian Space Research Organisation).
About Me
I am a PhD student in Computer Science at the University of Oregon, working in the Distopia Lab. My research focuses on privacy-preserving federated learning and machine unlearning, with emphasis on developing computationally efficient methods for federated unlearning.
My academic journey has taken me through Manipal University (Bachelor's) and Stevens Institute of Technology (Master's) before joining University of Oregon for my PhD. I bring a unique combination of industry experience from Cisco and Chubb Insurance, along with research experience at ISRO and MNIT.
Research Interests
Research
Caffeine: Computationally Efficient Federated Unlearning
Machine unlearning—the ability to remove the influence of specific data from a trained model—is crucial for privacy compliance (GDPR's "right to be forgotten"). However, in federated learning settings, unlearning becomes particularly challenging due to the distributed nature of data and computation.
Caffeine addresses this challenge by developing computationally efficient methods for federated unlearning that avoid costly complete model retraining while maintaining model performance and providing formal privacy guarantees.
Past Research Projects
ISRO ChatBot
Developed a multimodal RAG-based chatbot using large language models for ISRO technical documentation.
Video Surveillance Anomaly Detection
Built real-time anomaly detection models for video surveillance systems using computer vision.
Sentiment Analysis
Developed NLP models for analyzing customer sentiment in cell phone reviews.
Employee Attrition Prediction
Created ML models to predict employee turnover with interpretable insights for HR.
Publications
Caffeine: Computationally Efficient Federated Unlearning
Neil Sharma
Middleware 2025 - Doctoral Symposium
This work presents Caffeine, a novel approach to federated unlearning that addresses the computational challenges of removing data influence from distributed machine learning models without complete retraining.
TheoremView: A Framework for Extracting Theorem-Like Environments from Raw PDFs
Shrey Mishra, Neil Sharma, Antoine Gauquier, Pierre Senellart
ECIR 2025 - European Conference on Information Retrieval
TheoremView introduces a novel approach to mathematical document search and retrieval, combining traditional information retrieval with mathematical formula understanding.
MERIT: Multimodal Enhanced Retrieval and Integration of Text and Images
Neil Sharma, Namita Mittal, Munish Singh
ISAI 2025 - International Symposium on Artificial Intelligence
MERIT presents a unified framework for learning multimodal embeddings that excel at both retrieval and downstream information tasks across text, image, and structured data modalities.
DIVERGEMENT: Domain-Tailored, Small-Model Natural Language to SQL Pipeline for Space Research Domain
Garvit Tibrewal, Neil Sharma, Naman Mittal
AusDM 2025 - Australasian Data Mining Conference Submitted
DIVERGEMENT introduces innovative methods for analyzing divergent data patterns in complex datasets with applications in anomaly detection and distribution shift analysis.
Experience
Graduate Research Assistant
University of Oregon - Distopia Lab
Working on privacy-preserving federated learning and machine unlearning. Published paper at Middleware 2025 Doctoral Symposium on the Caffeine project.
Graduate Teaching Assistant
University of Oregon - CS Department
Teaching Introduction to Software Engineering. Topics: Git/GitHub, Docker, CI/CD, Agile methodologies, and software testing.
Research Fellow
Indian Space Research Organisation (ISRO)
Developed multimodal RAG-based chatbot using large language models for technical documentation and knowledge management.
Production Engineer
Cisco Systems - Webex Team
Maintained production infrastructure for Webex services. Implemented monitoring systems, optimized microservices, and improved system reliability.
Data Analyst / ML Engineer
Chubb Insurance
Built machine learning models for employee attrition prediction and insurance risk assessment. Conducted data analytics for business insights.
Teaching
Introduction to Software Engineering
University of Oregon | Fall 2024 - Present | Graduate Teaching Assistant
Conducting lab sessions and mentoring students on modern software development practices. Topics include version control, containerization, CI/CD pipelines, and Agile methodologies.
Topics Covered
Office Hours
By appointment
Email to schedule
Teaching Philosophy
Hands-on learning with real-world projects and industry-relevant skills.
Get In Touch
Location
Department of Computer Science
University of Oregon
Eugene, OR 97403
Interested in collaboration?
I'm always open to discussing research collaborations, industry projects, or speaking opportunities related to privacy-preserving machine learning and federated learning.
Send Email