My PhD research in Computer Science at the University of Houston focuses on multimodal AI, vision-language models, and computer vision for video understanding.
Lately, I’ve been building multimodal systems that make sense of long lecture videos: fusing speech, slide text, and visual frames with VLMs to generate chapter-level summaries, evaluated with LLM-as-judge methods and deployed on the NSF-funded VideoPoints platform.
This GitHub is where I share projects and tools across multimodal summarization, vision-language systems, object detection, and practical ML workflows.
If any of this sounds interesting, feel free to reach out: dipayan1109033@gmail.com
Multimodal AI · Vision-Language Models · Computer Vision · Video Understanding · Multimodal Summarization · LLM Evaluation · Visual Grounding
Python · C++ · SQL · PyTorch · Transformers · OpenCV · vLLM · Ollama · FastAPI · Hydra · MLflow · DeepEval · LangChain · Git · AWS
- VLM-Inferences: unified VLM inference across Ollama, vLLM, Transformers, OpenAI, and Gemini
- edu-video-visual-detection: code for our IEEE MIPR 2025 paper on visual content detection in lecture videos
- LVVO_dataset: Lecture Video Visual Objects benchmark (4,000 frames)
- SOTA-Detection-Lab: modular PyTorch framework for YOLO, Faster R-CNN, and DETR