Muhammad Waseem

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I am a final-year undergraduate student at Shiv Nadar University Chennai where I'm majoring in Artificial Intelligence and Data Science. I am specifically interested in applied CS Research with special interest in Computer Vision and Multi-modal models.

Currently I am a visiting researcher under Dr. Karthik Nandakumar at MBZUAI.

I spent my Summer'2024 as UGRIP intern at MBZUAI, Abu Dhabi where I worked on analysing the hallucination of LLM's responses to principled prompts. We also collected human and model preferences for each of the response pair for future study on preference based optimization.

Previously, I did a Research Internship under Dr Ravi Kiran Sarvadevabhatla to achieve precise text line segmentation for complex Indic and Southeast Asian historical palm leave. Our work got accepted to ICPR conference.

Please feel free to check out my resume. You can also find me on other spaces below.

~ 𝕏 (Twitter) | Github | LinkedIn ~

Sep '24	Ranked 45th out of several thousand teams participated in Amazon ML Challenge 2024. Eligible for PPI for the role of Applied Scientist Intern.
Jun '24	Our paper LineTR: Unified Text Line Segmentation for Challenging Palm Leaf Manuscripts got accepted at ICPR 2024 🥳!
Mar '24	Selected for UGRIP at MBZUAI with an acceptance rate of 4%.
Dec '23	Selected as Research intern to work in Computer Vision Center, Spain.
Nov '23	Awarded Merit Scholarship for academic excellence in the year 2022-2023.
Feb '23	Selected for Summer Research internship at CVIT Lab in IIIT Hyderabad.
Jan '23	Special mention award at NIT Trichy & DataNetiix hackathon for our Research digest prototype.
Dec '22	Awarded Merit Scholarship for academic excellence in the year 2021-2022.
Nov '22	Selected as the Student Coordinator for University's Annual Tech fest.
Sept '21	Joined Shiv Nadar University Chennai for B.Tech in Artificial Intelligence and Data Science

Shiv Nadar University, Chennai
Bachelor of Technology in Artificial Intelligence and Data Science
September '21 - May '25

Awards: 2 times Merit Scholarship Awardee

Student Societies:

Special Invitee | Students Grievance Redressal Committee (SGRC)
Student Coordinator | Invente - Annual Technical Fest
Technical Member | Chess Club

	Visiting Researcher \| SPriNT-AI Lab July '24 - August '24 Working in SPriNT-AI (Security, Privacy and Trustworthiness in Artificial Intelligence) lab, focussing on effective utilization of shapley values in federated learning for non-iid setting.
	UGRIP Intern \| ViLA Lab May '24 - June '24 [My Experience] Our project, conducted under Dr. Zhiqiang Shen (Jason), focused on "Optimizing Prompts for Foundation Models" to reduce hallucination. We curated a benchmark dataset of 25k questions across ~60 topics like law, philosophy, and history. Additionally, we developed a web application to collect human preferences and assess the correctness of responses before and after applying 26 guiding principles. This preference data is crucial for future preference-based optimization techniques, enhancing the accuracy and reliability of AI-generated responses
	Research Intern \| Computer Vision Center (CVC) Feb '24 - Present Working on document editing. Currently analysing the potential of LLMs to generate structured commands to edit documents.
	Research Intern \| Center for Visual Information Technology (CVIT) May '23 - Feb '24 Co-Developed on a novel method to achieve precise text line segmentation for complex Indic and Southeast Asian historical palm leaves.

LineTR: Unified Text Line Segmentation for Challenging Palm Leaf Manuscripts

Vaibhav Agrawal, Niharika Vadlamudi, Muhammad Waseem, Amal Joseph, Sreenya Chitluri, Ravi Kiran Sarvadevabhatla
ICPR 2024
[paper]

We present LineTR, a novel two-stage approach for precise line segmentation in diverse and challenging handwritten historical manuscripts. LineTR's first stage uses a DETR-style network and a hybrid CNN-transformer to process image patches and generate text scribbles and an energy map. A robust, dataset-agnostic post-processing step produces document-level scribbles. In the second stage, these scribbles and the text energy map are used to generate precise polygons around text lines. We introduce three new datasets of Indic and South-East Asian manuscripts and demonstrate LineTR's superior performance and effectiveness in zero-shot inference across various datasets.

Uncovering bias and uncertainty in model using Semi-Supervised VAEs

[code] [blog]

This project aims to investigate and quantify the biases present in face detection models. Identified biases include a preference for white faces over black faces, higher accuracy in detecting male faces compared to female faces, better detection of faces without glasses, and variations in accuracy based on different hair colors. The ultimate goal is to highlight these biases and suggest ways to mitigate them, promoting the development of fairer and more inclusive face detection systems.

Writer independent offline handwriting verification

[code] [blog]

Developed a model using PyTorch CRAFT and Vision Transformer to determine if two handwritten Hindi images are by the same writer. Achieved an AUC of 0.72 and 10th place in a NCVPRIPG workshop competition.

Nature Inspired Neural Networks

[code]

Optimizing neural network weights using nature-inspired algorithms instead of gradient descent and backpropagation. The algorithms include Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithm.

More projects can be found on Github

Last updated: September 22, 2024

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