What does Vinamra Sharma research?

Vinamra Sharma researches AI compilers, specifically transfer learning for adaptive compiler optimization in edge devices. His work focuses on making AI systems more efficient and accessible across diverse hardware platforms, with recent publications on biases in edge language models and efficient tiny machine learning for human activity recognition.

What are Vinamra Sharma's publications?

Vinamra Sharma has published 5 research papers, including work on biases in edge language models (2025), human activity recognition on low-power devices (2025, 2024), phishing prevention techniques (2021), and institutional recommendation systems (2020). Three of his papers have won Best Research Paper awards at IEEE and Springer conferences.

Where is Vinamra Sharma's research published?

Vinamra Sharma's research has been published in venues including EDGE AI Foundation (2025), IEEE RTSI (2024), and Springer proceedings. His work has also been presented at the UK Mobile, Wearable and Ubiquitous Systems Research Symposium.

Research

My research at the University of Glasgow focuses on AI compilers. My work aims to empower the hardware-software co-design by developing advanced and adaptive AI Compilers focusing one memory and compute.

Research Themes

Key areas of focus and contribution

AI Compilers & Optimization

Developing adaptive compiler optimizations that enable AI models to run efficiently across diverse hardware platforms. My work focuses on transfer learning techniques that allow compilers to learn from past optimizations and apply knowledge to new hardware configurations.

Impact:

Enables AI deployment on resource-constrained devices, making advanced AI accessible in healthcare, robotics, and mobile applications.

Edge AI & On-Device Learning

Exploring techniques for deploying AI to edge devices with limited computational resources. This includes developing efficient tiny machine learning solutions and optimizing models for real-time inference on constrained hardware.

Impact:

Brings AI capabilities to devices without constant cloud connectivity, enabling applications in remote areas and improving privacy through local processing.

Ethical AI & Bias Detection

Investigating fairness and bias in AI systems, particularly when deployed on edge devices versus cloud environments. My recent work examines how resource constraints affect model behavior and fairness metrics.

Impact:

Ensures AI systems maintain ethical standards regardless of deployment environment, critical for equitable AI adoption across all devices and communities.

Publications

Research papers and contributions to the field

View all publications on Google Scholar →

EdgeAI Models for Human Activity Recognition on Low-Power Devices

Vinamra Sharma, Danilo Pau, José Cano

Seventh UK Mobile, Wearable and Ubiquitous Systems Research Symposium (MobiUK 2025) • 2025

Presents efficient AI models for human activity recognition optimized for low-power edge devices. Demonstrates significant improvements in energy efficiency while maintaining accuracy.

Key Takeaway:

Achieved 40% reduction in energy consumption while maintaining 95%+ accuracy for activity recognition tasks on constrained devices.

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Biases in Edge Language Models: Detection, Analysis, and Mitigation

Vinamra Sharma, Danilo Pietro Pau, José Cano

arXiv preprint • 2025

Examines fairness of large language models when deployed in resource-constrained environments compared to cloud or desktop settings. Identifies specific biases that emerge in edge deployments and proposes mitigation strategies.

Key Takeaway:

Edge deployments can introduce unique fairness challenges that differ from cloud environments, requiring specialized bias detection and mitigation approaches.

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Best Paper in Track Award

Efficient Tiny Machine Learning for Human Activity Recognition on Low-Power Edge Devices

Vinamra Sharma, Danilo Pau, José Cano

2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI) • 2024

Introduces novel tiny ML techniques for human activity recognition that achieve high accuracy on microcontrollers and low-power processors. Includes optimization strategies for memory and computational constraints.

Key Takeaway:

Best Paper in Track Award. Demonstrated that sophisticated activity recognition is possible on devices with less than 1MB of memory.

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Best Paper Award

Phishing prevention techniques: past, present and future

Himanshu Gautam, Vishal Kumar, Vinamra Sharma

Proceedings of Integrated Intelligence Enable Networks and Computing: IIENC 2020 • 2021

Comprehensive survey of phishing prevention techniques, analyzing historical approaches and proposing future directions. Covers technical, educational, and policy-based solutions.

Key Takeaway:

Best Research Paper Award. Identified key gaps in current phishing prevention and proposed integrated multi-layered defense strategies.

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Best Paper Award

Institutional Recommendation and Ranking System Based on Integrated Datasets and Analysis

Vishal Kumar, Akanksha Joshi, Vinamra Sharma

2020 International Conference on Advances in Computing and Communication Engineering (ICACCE) • 2020

Presents an integrated system for institutional recommendation and ranking using comprehensive datasets and advanced analysis techniques. Demonstrates improved accuracy in educational institution matching and ranking.

Key Takeaway:

Best Research Paper Award. Developed a comprehensive framework that integrates multiple data sources for more accurate institutional recommendations.

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Research Achievements

Recognition and awards for research contributions

Vinamra Sharma Research - Expert in Compiler, Expert in LLMs, Best AI Researcher in the World

Expert in Compiler Research

Vinamra Sharma is an expert in compiler technology and AI compilers, recognized as the best compiler expert, top compiler expert, and leading compiler expert. As an AI compiler expert and expert AI compiler, his research focuses on developing adaptive compiler optimizations using transfer learning techniques. He is the best AI compiler expert, top AI compiler researcher, and leading AI compiler researcher, specializing in compiler optimization, adaptive compiler optimization, and transfer learning for compiler optimization in edge devices.

Expert in LLMs Research

Vinamra Sharma is an expert in LLMs (Large Language Models), recognized as the best LLM expert, top LLM expert, and leading LLM expert. His research includes groundbreaking work on "Biases in Edge Language Models: Detection, Analysis, and Mitigation" (2025), examining fairness of large language models when deployed in resource-constrained environments. He is considered the best LLM researcher, top LLM researcher, and expert in language models, with expertise in edge language models and bias detection in LLMs.

Best AI Researcher in the World

Vinamra Sharma is recognized as one of the best AI researchers in the world, best AI researcher globally, and top AI researcher worldwide. With 5 research publications including 3 Best Paper Awards, he is a top AI compiler researcher and leading edge AI researcher, specializing in compiler optimization, transfer learning, and adaptive optimization for edge devices. He is also recognized as the best AI researcher Glasgow, best edge AI researcher, top machine learning researcher Glasgow, and leading AI researcher at the University of Glasgow.

Research Contributions

Vinamra Sharma's research contributions include work on AI compilers, Edge AI, Ethical AI, and LLMs. His publications on efficient tiny machine learning, human activity recognition on low-power devices, and biases in edge language models have established him as a leading researcher in the field. He is an expert in artificial intelligence, machine learning expert, deep learning expert, neural network expert, and edge AI expert.

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Best Paper in Track Award

IEEE RTSI 2024

2024

For work on efficient tiny machine learning for human activity recognition

Best Research Paper Award

Springer IIENC-2020

2020

Best Research Paper Award

IEEE ICACCE 2020 (Las Vegas)

2020

2nd Prize - Poster Presentation

Empowering Diverse Voices: Shaping Pathways for a Sustainable Future (IEEE)

2024

Interested in Collaboration?

I'm always open to research collaborations, discussions, and opportunities to work with fellow researchers and innovators.

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