Hi, I am Shiti Chowdhury.

I am an AI researcher with research interests in Multimodal Artificial Intelligence, Computer Vision, Video Understanding, Vision-Language Models, Generative AI, and Trustworthy AI.

I completed my B.Sc. in Computer Science and Engineering from Chittagong University of Engineering and Technology (CUET), Bangladesh, with a CGPA of 3.70/4.00.

My undergraduate thesis focused on multimodal abusive video detection, integrating textual, visual, and audio information through cross-modal attention and multi-task learning, with explainable AI techniques for interpreting model predictions. This work strengthened my interest in multimodal content understanding.

I am seeking fully funded M.Sc. or Ph.D. opportunities to pursue advanced research in Multimodal AI, particularly in Computer Vision, Video Understanding, Vision-Language Models, Generative AI, and Trustworthy AI.

RESEARCH INTERESTS

Multimodal Artificial Intelligence

Cross-modal learning and reasoning from text, images, video, and audio.

Computer Vision & Video Understanding

Visual recognition, temporal analysis, and complex video understanding.

Vision-Language Models

Visual-language alignment, grounded understanding, and multimodal reasoning.

NLP & Multilingual AI

Language representation and understanding for multilingual and low-resource settings.

Generative AI & Large Language Models

Generative modeling, intelligent reasoning, and AI-generated content understanding.

Trustworthy & Explainable AI

Explainability, robustness, safety, fairness, and responsible AI.

UNDERGRADUATE THESIS
Multimodal abusive content detection framework
2025 — 2026

A Multimodal Multiclass Framework for Automated Detection of Abusive Content in Online Videos

Explainable multimodal learning for abusive content detection in online videos.

Developed a multimodal framework integrating text, visual, and audio features to identify abusive content in Bangla-English online videos.

The framework combines XLM-RoBERTa, CLIP, and Wav2Vec2 with cross-modal attention, multi-task learning, and explainable AI techniques.

SUPERVISOR

Saadman Sakib, Assistant Professor, CSE, CUET

RESEARCH

Multimodal AI · Video Understanding · Explainable AI

Multimodal AI Computer Vision NLP Cross-Modal Attention Explainable AI
EDUCATION

Chittagong University of Engineering and Technology (CUET)

Bachelor of Science in Computer Science and Engineering (CSE)

Chattogram, Bangladesh

Graduated July 2026

CGPA: 3.70 / 4.00

Academic Excellence: Achieved an average CGPA of 3.85+ over the final five semesters.

Merit-Based Scholarship: Received a technical scholarship for all 8 semesters of the undergraduate program.

Chattogram College

Higher Secondary Certificate (HSC) — Science

Chattogram, Bangladesh

2020

GPA: 5.00 / 5.00

Chittagong Government Girls' High School

Secondary School Certificate (SSC) — Science

Chattogram, Bangladesh

2018

GPA: 5.00 / 5.00

NEWS
SEP 2026

Completed the 2026 Aspire Leaders Program by Aspire Institute.

JUL 2026

Published four research papers as a research author.

JUL 2026

Published two research papers at SemEval 2026, co-located with ACL 2026: “Invariance-Oriented Representation Learning for Robust AI-Generated Code Detection” and “Disentangling Logical Validity from Semantic Plausibility through Canonical Abstraction.”

JUN 2026

Successfully completed my undergraduate thesis on multimodal abusive video content detection with A+ (4.00/4.00).

MAR 2026

Published “A LangChain-Based Framework for Investigative Question Generation Using Large Language Models” in the TREC 2025 DRAGUN track proceedings.

CONTACT

I am open to fully funded graduate research opportunities and academic collaborations. I welcome inquiries regarding prospective research opportunities, scholarly discussions, and professional engagements.

shitichowdhury21@gmail.com