Research Assistant · AI in Digital Health

Building intelligent vision systems for real-world healthcare.

I design lightweight computer vision solutions that translate from lab prototypes to impactful clinical workflows.

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About Me

Hi, I am Anshul, a Research Assistant at the Koita Centre for Digital Health, IIT Bombay. My research interests include computer vision, artificial intelligence, and their applications in digital health.

My research journey began with a focus on energy management and optimization, where I explored topics like energy management in microgrids, battery state-of-charge estimation using deep learning and TinyML, and optimal sizing of battery energy storage. I have also worked on developing novel optimization algorithms. Additionally, I worked as a Junior Research Fellow at CSIR-CEERI, Pilani, gaining valuable industrial research experience.

Before that, I held a Bachelor of Technology in Electrical Engineering with distinction from Swami Keshvanand Institute of Technology, Management, and Gramothan, Jaipur (2023).

Core Skills

Computer Vision & Deep Learning
TinyML & Edge AI
Energy Management Systems
Optimization Algorithms

Experience

Project Technical Assistant

Computer Science & Engineering, IIT Bombay

April 2025 – Present

Implemented lightweight classification models using TinyViT and MobileViT architectures, ensuring efficient inference suitable for deployment in low-resource healthcare settings.
Explored semi-supervised learning techniques to reduce the annotation burden.

Junior Research Fellow

CSIR-Central Electronics Engineering Research Institute, Govt. of India

August 2023 – March 2025

Revitalizing Ancient Murals Using Deep-Learning Techniques

Performed data acquisition at Mandawa Kothi and image preprocessing to study realistic mural damage.
Developed a damage segmentation framework employing GANs and an ensemble network to identify varying levels of damage in Shekhawati murals.
Studied and implemented inpainting models including CNN-, GAN-, and Transformer-based networks to identify research gaps.
Developed an algorithm integrating a damage segmentation model, visual feature-based clustering, and modular inpainting for refined results.

Battery Management System for Redox Flow Battery (RFB)

Studied the operation and energy management of an industrial-grade 5kWh RFB battery.
Developed a regression model for state-of-charge prediction, including feature selection and dataset augmentation.
Deployed TinyML models to Arduino IoT boards using TensorFlow Lite.
Integrated node-edge-cloud computing paradigms for enhanced SoC estimation.

Additive Manufacturing Anomaly Detection

Designed an image- and sensor-based anomaly detection framework with hardware integration.
Collected vibration data using MPU 6050 sensors on two distinct 3D printing setups.
Processed vibration signals to distinguish failure types and developed a computationally efficient classifier.

X-ray AI: Baggage Security System

Built an AI-enhanced baggage scanner using KritiScan 6040 with YOLOv11 for real-time threat detection.
Identified prohibited items like weapons, tools, and sharp objects in X-ray imagery.

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Building intelligent vision systems for real-world healthcare.

About Me

Core Skills

Experience

Project Technical Assistant

Junior Research Fellow

Revitalizing Ancient Murals Using Deep-Learning Techniques

Battery Management System for Redox Flow Battery (RFB)

Additive Manufacturing Anomaly Detection

X-ray AI: Baggage Security System

Research Intern

Smart Home and Microgrid Energy Management

Battery State-of-Health Estimation

Projects

Publications

Contact

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