Madan Baduwal

Starkville, MS

Hi there! Thank you for visiting my personal webpage. I am currently pursuing a PhD in Computer Science at Mississippi State University (MSU).

My academic foundation lies in Physics, Mathematics, and Computer Science.

My research interests lie at the intersection of Signal Processing, Multimodal Machine Learning, Quantum Machine Learning, and Robotics. I explore how humans perceive, process, and integrate information from multiple senses to make informed decisions. My ultimate goal is to reverse-engineer aspects of human cognition to develop robots capable of seeing, listening, speaking, learning, and interacting with human-like perception, reasoning, and social intelligence.

I was a Graduate Research Assistant at the University of Texas Permian Basin under Prof.Quan Yuan, while pursuing my master’s degree in Computer Science. I earned a BE in Computer Engineering at Tribhuvan University, under the direction of Sanjeev Prasad Panday. I am lucky to have opportunities to work with Mahabir Pun, and Rabin Dhakal. If you are interested in discussing research, feel free to reach out via LinkedIn, or Twitter. You can also send me an email: madanbaduwal100@gmail.com.

news

Jan 15, 2025	Started PhD in Computer Science at the MSU. 🎓
Jan 01, 2025	Started working as a Graduate research assistant at MSU. 💁
Mar 15, 2023	Began working as an Orientation Leader at UTPB.
Mar 10, 2023	Joined as an Undergraduate Student Assistant Editor at UTPB.
Jan 16, 2023	Started working as a Graduate research assistant at UTPB. 💁

latest posts

Dec 22, 2020	Covid-19 Detection
Dec 10, 2020	Python functions

selected publications

SigV

Signature Verification using Convolutional Neural Network and Autoencoder.

Prakash Ratna Prajapati , Samiksha Poudel , Madan Baduwal , and 2 more authors

Journal of the Institute of Engineering, 2021

Abs PDF

Signature has been one of the widely used verification biometrics out there. Handwritten signatures are used in cheques, forms, letters, applications, minutes, etc. The Signature of every individual is unique in nature, that is why it is essential that a person’s handwritten signature be uniquely identified. Signature Verification is a widely used method for authenticating any individual during absence. Human verification is prone to inaccuracy and sometimes indecisiveness. This paper presents an investigation of using Convolutional Neural Network (CNN) for Writer-Dependent models in signature verification. Random distortions were generated in genuine images using an autoencoder to get forged signatures, which were passed to the classifier during training. The paper details all the pre-processing steps carried out on the image and shows various test results for changing the number of training sets of images. The average test accuracy for Persian dataset is 83% when the system was trained with 22 genuine images. There was a decrease of 9.4% in accuracy when the model was trained with 9 genuine images.