Md. Tahmid Islam Tomal

Computer Vision & Machine Learning

My research interests center on vision-language models and representation learning, with a focus on designing efficient, reliable, and secure architectures that adapt to new concepts and environments. I aim to understand how learned representations shape model behavior when evidence is limited, ambiguous, or differs from training data. My research has explored these questions through few-shot and incremental learning, while my engineering work has exposed practical challenges in retrieval, context management, and multimodal reasoning. I hold a B.Sc. in Computer Science and Engineering from BUET, where my undergraduate thesis explored attention mechanisms and YOLO models for road crack detection. I currently work as a Machine Learning Engineer at Panjeree Publications and seeking PhD opportunities in computer vision and machine learning.

Portrait of Md. Tahmid Islam Tomal
Address: Dhaka, Bangladesh

Recent Highlights

Publications

Diagnosing Prototype Estimation in Frozen-Feature Few-Shot Class-Incremental Learning: Training-Free Geometry Selection and Unlabeled-Query Prototype Rectification

Md Tanvir Hossain Saikat, Md Akib Haider*, Md. Tahmid Islam Tomal*, Md Hasanul Kabir

A study of prototype estimation across 16 encoder–dataset pairs. ShrinkSel selects prototype geometry using base data, while Query Prototype Rectification uses unlabeled queries to refine novel-class prototypes.

Accepted2027

Background

Education

B.Sc. in Computer Science and Engineering

Bangladesh University of Engineering and Technology

Feb 2020 - March 2025

Professional Experience

Machine Learning Engineer

Panjeree Publications Ltd.

Oct 2024 — Present

Build large-scale data processing pipelines and AI-powered educational systems for Praxix Academy, working across agentic ai, model integration, application development, infrastructure, and production deployment.

Praxix Academy (opens in new tab)

Software Engineer

ICMsoft

Jun 2024 — Sep 2024

Built document extraction systems using LayoutLMv3 and few-shot domain adaptation.