Research direction
Primary theme
Document understanding in low-resource settings: layout analysis, printed/handwritten OCR, and robust extraction from noisy real-world PDFs/images.
Efficient multimodal adaptation
Resource-aware fine-tuning and quantization of vision-language models for document and scene understanding; interest in parameter-efficient multi-task methods for deployable multimodal AI.
Secondary theme
Efficient multimodal systems (LLM/VLM + speech pipelines): quantization, retrieval augmentation, and reliability-aware evaluation.
Style
Baseline-first experimentation, careful metric definitions, error analysis, and reproducibility (CI + API-first prototypes) to keep results verifiable.
Research interests
- Efficient multimodal adaptation: parameter-efficient fine-tuning, quantization, and sweep-guided model selection for VLMs/LLMs.
- Computer vision for document and scene understanding: detection, layout analysis, and comparative evaluation under low-resource constraints.
- Deployable foundation models: reproducible multi-GPU training, objective-aligned metrics, and edge-ready inference.
What I’m trying to contribute
Goal: bridge rigorous research and deployable systems for high-variability documents and low-resource scripts.
- Robustness: methods that hold up under layout variance, scan artifacts, and domain shift.
- Evaluation: reproducible benchmarks and ablations that make trade-offs explicit (accuracy vs cost/latency).
- Efficiency: studying accuracy–memory–latency trade-offs when adapting foundation models (quantization, sweep-guided selection, multi-GPU reproducible training).
- Systems: executable research artifacts (APIs, Docker, CI) to reduce “it works on my machine” gaps.
Education & credentials
University of Liberal Arts Bangladesh (ULAB)
B.Sc. Computer Science and Engineering (Minor: Business Administration)
- CGPA: 3.96 / 4.00 (145 credits earned)
- Student ID: 192014038
- Provisional certificate issued: 9 April 2023
- Relevant coursework: Artificial Intelligence (A), Digital Image Processing (A), Algorithms (A), Data Structures (A), Statistics and Probability (A), Software Engineering (A−), Discrete Mathematics (A), Operating Systems (A)