About Me
After completing my master’s in computer science, I worked in industry across India and England before moving to Amsterdam. At the University of Amsterdam (UvA), I got involved in the research on EdgeAI, i.e., enabling Artificial Intelligence (AI) models to execute efficiently on resource-constrained edge devices. I pursued a PhD at UvA (completed 2022), focusing on hardware-aware AI models. I continued at UvA as a post-doctoral researcher before becoming an Assistant Professor in 2024. I have since built and extended an independent research line in EdgeAI, centred on context-aware and adaptive model deployment for dynamic execution environments.
I approach research with curiosity and creativity, focusing on designing adaptive neural architectures for resource-constrained edge devices. My work explores how AI models can dynamically adjust to changing environments while remaining efficient and sustainable over time.
Building on this foundation, I aim to make AI deployment more accessible and energy-aware across connected devices, bridging the gap between intelligence and efficiency. Through my research, I strive to publish impactful work, develop open-source tools, and create frameworks that strengthen Europe’s and the world’s AI capabilities.
Interests
Neural Architectures
Edge AI
Design Space Exploration
Adaptive AI
Education
PhD in Computer Science, 2022
University of Amsterdam
M.E. in Computer Science, 2007
BITS Pilani, India
B.Tech. in Computer Science, 2005
Bharati Vidyapeeth's College of Engineering
Awards and Recognition
Best Paper Award: Constrained evolutionary piecemeal training to design convolutional neural networks”. IEA/AIE 2020.
Best Paper Award Candidate: PELSI: Power-Efficient Layer-Switched Inference. RTCSA 2023.
Outstanding Reviewer Award : for outstanding service in CASES, ESWEEK 2024
Adaptive AI. Edge Deployment
Secure Inference. Early Exit NNs
Edge AI. Low-Power Design.
*Defended May 2025