About
An enthusiastic AI researcher with a deep-seated passion for computer vision and deep learning.
My academic journey in artificial intelligence has equipped me with a strong foundation in developing and deploying intelligent
systems that address complex real-world challenges.
I thrive on exploring the vast possibilities within AI, focusing on creating innovative and impactful
solutions that can advance both academic knowledge and industry practices. My work is driven by a curiosity
to understand how AI can reshape the future, and I am particularly interested in the cutting-edge areas of
generative models, explainable AI, and the intersection of AI with other emerging technologies.
As a professional, I believe in the power of continuous learning and collaboration.
I strive to engage with the global AI community, contributing to advancements that push the boundaries of technology.
Whether through research, publications, or hands-on projects, my goal is to make a meaningful impact in the world of AI,
shaping technologies that can benefit society at large.
Research Domains
- Computer Vision
- Applied Machine Learning
- Machine Learning Optimization
- Geometric Deep Learning (3D Computer Vision)
- Generative AI for Vision
- Segmentation / Object Tracking / Medical Imaging / 3D Image Reconstruction
What I've Done
Total Projects
Total Publications
Awards
Collabrative Works (Research/Project)
Publication
To Know More: 
Projects
Generative AI Playground (Vision)
Building an interactive platform for experimenting with various GANs and VAEs, facilitating image tasks like data augmentation, image compression, image generation and reconstruction
Real-Time Depth Estimation & Image Reconstruction from Point Cloud
Developed a real-time depth estimation system that leverages the MiDaS model to generate accurate depth maps and YOLOv5 for object detection from webcam input.
Gaze Tracing for Mouse Pointer Movement
Implemented gaze-based mouse control, translating detected gaze direction into precise mouse pointer movements for improved user interaction and accessibility.
Medical Image Segmentation
Processes 3D medical images from various modalities, such as MRI and CT scans. Implemented segmentation in both centralized and decentralized setups with a novel loss function tailored for each approach.
City Space Segmentation
Developed semantic segmentation models for classifying and segmenting urban street scenes from the Cityspace dataset. And achieved 88-94% segmentation accuracy by optimizing model architectures and employing data augmentation techniques.
Low Light Night Vision Face Detection Using Thermal Imaging
Built a face detection module for low-light conditions using Haar-cascade, Dlib, and YOLOv8. And Enhanced detection accuracy by combining multiple models (e.g Dlib with YOLOv8) for improved performance in challenging lighting.
Palm Print Detection & Identification Using Mobile Cameras
Developed proprietary palm print dataset with manual annotation. Pre process through noise removal and kernel filtering. Applied transfer learning with YOLO (v5 and v8), with YOLOv8 outperforming YOLOv5 by 9-13%.
Predicting Solar Power Generation With Its Maintenance Activities
Leveraged deep learning, time series modelling, and various ML techniques & libraries (Sci-Kit Learn, Pandas, NumPy, TensorFlow, Keras, PyBats, matplotlib) to analyze data patterns, perform mathematical operations, and yield precise results.
Testimonials
Dr. Kshitij Jadhav
Assistant Professor, KCDH, IITB
Charul is a highly motivated innovative an enterprising individual who has worked closely with me on an important research project. He developed a novel Loss function for medical image segmentation. This helped in beating the SOTA results in low resource setting. Charul is a fantastic Python coder, works cohesively in a team and works deligently within deadlines. He is eager to learn and has the right attitude and a growth mindset which brings the best not only within himself but also his team.
Contact
Location:
Bharat (India)