About Me
$ cat summary.txt
Hello! I am an AI Engineer and Researcher with a genuine passion for AI. My work has centered on misinformation detection, probabilistic machine learning, and large language models. I have built solutions that merge academic insight with practical applications. I am seeking roles as a Data Scientist or Machine Learning Engineer. I also look for PhD opportunities that focus on building AI models, probabilistic ML, and foundational models. My journey blends deep research with hands-on industry experience. I value clear results and practical innovation.
Education
$ cat education.json
Queen Mary University of London
London, UK
MSc in Artificial Intelligence
Sept 2022 – Sept 2023
Grade: 2:1
BRAC University
Dhaka, Bangladesh
BSc in Computer Science
Jan 2017 – Oct 2021
CGPA: 3.45
Honors: Vice Chancellor's List (Summer 2020, Fall 2020), Dean's List (2017)
Professional Experience
$ cat work_experience.log
Machine Learning Fellow
Fatima Fellowship
Sept 2023 – Present
Remote, US
- Conducted advanced research on misinformation context generation using text and images, employing proxy agents and Large Vision Language Models (LVLMs) like CLIP and BLIP.
- Developed and fine-tuned multimodal transformer models with frameworks such as PyTorch and TensorFlow, enhancing the accuracy of context generation in preliminary trials.
- Collaborated with a multidisciplinary team to integrate LVLMs into misinformation detection systems, contributing to improved analytical capabilities and co-authoring a research paper submitted for publication.
Data Scientist (Contract)
Faculty AI
May 2023 – July 2023
London, UK
- Optimized supply chain operations by implementing predictive models for supplier reliability and ex-factory dates using 20TB of data in Azure SQL Server.
- Applied techniques such as XGBoost, LightGBM, Bayesian Ridge, and neural networks, achieving high accuracy with a 2% MAE after hyperparameter tuning.
- Improved operational efficiency, resulting in a monthly labor savings of 500 hours.
Software Developer
Miguns Technologies Ltd.
July 2021 – Aug 2022
Dhaka, Bangladesh
- Led development of machine learning solutions for fintech applications: fraud detection, payment analytics, credit scoring.
- Implemented ML techniques (Random Forest, Gradient Boosting, ARIMA, Prophet, neural networks, NLP models).
- Enhanced customer experience by automating 40% of inquiries, reducing response time from 15 to 2 minutes.
Research Experience
$ cat research_experience.md
Research Fellow
Fatima Fellowship
Sept 2023 – Present
Project: Generating Context for Misinformation using Texts and Images using Large Vision Language Model (LVLM)
- Developed a framework to generate accurate context for misinformation using Large Vision Language Models (LVLMs) and agentic workflows.
- Implemented chain-of-thought and tree-of-thought reasoning for context generation.
- Aiming to improve the ability of LVLM models to detect and contextualize misinformation more effectively, contributing to interpretable and reliable NLP systems in real-world scenarios.
Undergraduate Researcher
BRAC University
Jan 2021 – Nov 2021
Project: Demystifying Black Box Learning Models for Rumor Detection in Social Media
- Developed a hybrid model combining XGBoost, SVM, Decision Tree Classifier (DTC), Extra Trees Classifier (ETC), and Random Forest Classifier (RFC) for rumor detection on Twitter, achieving a 5% improvement over the base model and an accuracy of 93.22%.
- Investigated model interpretability using LIME, providing detailed insights into feature contributions for predictions.
- Published findings at the 12th IEEE UEMCON Conference.
Projects
$ ls -la ./projects/
AI-Powered Ecological Assistant with Agentic Workflow
- Developed a multi-agent AI system (Planner, Evaluator, Executor) to autonomously route ecological queries across tools like GPT-4o, BioTrove-CLIP for species classification, and Wikipedia/RAG pipelines.
- Engineered a context-aware workflow using Streamlit (frontend) and FastAPI (backend), integrating dynamic tool selection (image/PDF analysis, GPT synthesis, or Wikipedia verification) based on conversational history.
- Open-sourced the project with modular agent/tool abstractions, enabling reuse in conservation tech or AI agent frameworks.
Skills
$ cat skills.yaml
Programming Languages
Frameworks and Libraries
Machine Learning
MLOps
Large Language Models
Databases
Publications
$ cat publications.bib
Demystifying Black-box Learning Models of Rumor Detection from Social Media Posts
Proceedings of the 12th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA, pp. 0358–0364.
DOI: 10.1109/UEMCON53757.2021.9666567
2021
This research developed a hybrid model combining XGBoost, SVM, Decision Tree Classifier, Extra Trees Classifier, and Random Forest Classifier for rumor detection on Twitter, achieving 93.22% accuracy. The study also investigated model interpretability using LIME to provide insights into feature contributions for predictions.
Contact Me
$ echo "Start a conversation with me"
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