Research Experience

Remote Research Assistant
Supervisor: Dr. Laith H. Baniata
Research Professor, Gachon University, South Korea
Email: laith@gachon.ac.kr
June 2024 - Present

• Conducted research on "Investigating the Predominance of Large Language Models in Low-Resource Bangla Language Over Transformer Models for Hate Speech Detection: A Comparative Analysis." This work was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT under the grant NRF-2022R1A2C1005316.
• Carried out research on "SentimentFormer: A Transformer-Based Multi-Modal Fusion Framework for Enhanced Sentiment Analysis of Memes in the Under-Resourced Bangla Language." This work was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT under the grant NRF-2022R1A2C1005316.
• Currently working on the research titled "Dissecting the Reasoning Capabilities of Vision-Language Models in Medical Visual Question Answering: An Zero-shot Chain-of-Thought Approach."

Professional Experience

Application Developer (AI/ML)
Dexian (Bangladesh) Limited.
May 2024 - Present

• Project 1: RFPMatcher
  • Developed a Retrieval-Augmented Generation (RAG) solution using Chain of Thought prompting to extract key information from Request for Proposal (RFP) documents.
  • Built a Past Experience Matcher system that uses Automatic Chain-of-Thought prompting alongside in-context learning and preset questions to extract requirements from RFPs, then matches them against a master database of prior proposal responses.
  • Enabled the system to generate Yes/No decisions with detailed explanations of how similar requirements were addressed in the past, aiding in the prediction of potential win/loss outcomes for new proposals.
  • Generated dynamic Tables of Contents (TOC) based on extracted key information and historical experience to streamline and structure the proposal writing process for new bids.
  • Technologies used: Python, LlamaIndex, Azure OpenAI, AlloyDB, CouchDB, React JS, FastAPI
• Project 2: Org Info
  • Implemented a multimodal agent for extracting organizational hierarchical information from organograms, utilizing in-context learning with tree-of-thought prompting to preserve the correct parent-child structure.
  • Applied multipath reasoning to resolve conflicts and ambiguities in relationships for accurate role placement and stored the extracted hierarchical information in a relational database.
  • Designed an LLM-based agent that converted natural language queries into SQL using few-shot learning and self-consistency with chain-of-thought prompting, which enabled contextual reasoning to accurately retrieve relevant organizational data and integrated the results into the OrgChart framework for hierarchical visualization.
  • Developed a dynamic Agentic RAG-guided chat interface that enabled users to interact with a specific organizational hierarchy by utilizing predefined query types, roles, and goals, and delivered context-aware, natural language responses.
  • Set up scheduled jobs to fetch organizational data from Bullhorn every 30 days and visualized organizational information in OrgChart .
  • Technologies used: Python, LangChain, LangGraph, Azure OpenAI, OpenCV, Azure SQL, React JS, FastAPI
• Project 3: CaseAligner
  • Built an LLM-based application using zero-shot prompting to generate PowerPoint presentations for case studies based on selected practice areas and industries.
  • Implemented an interactive chat interface allowing users to query specific slide content and receive instant contextual responses.
  • Developed comprehensive search functionality to locate information across all generated case studies.
  • Created export capabilities for downloading slides in company's official template.
  • Designed an admin panel for authorized users to download and edit the knowledge base of case studies.
  • Technologies used: Python, LlamaIndex, Azure OpenAI, React JS, FastAPI
• Project 4: KnowledgeEngine
  • Developed an LLM-based, multi-document RAG Q&A system for internal document information retrieval.
  • Implemented a chat conversation interface with document page references for information sources.
  • Maintained session-based dedicated knowledge bases to ensure data isolation and user-specific context management.
  • Created an admin panel with document upload functionality and comprehensive document management capabilities.
  • Technologies used: Python, LlamaIndex, Azure OpenAI, AlloyDB, React JS, FastAPI
• Project 5: AgentDexi
  • Designed an LLM-based multi-agent system to generate customer intelligence by analyzing job demand.
  • Developed an RAG solution to identify technological trends in job descriptions across external companies.
  • Created interactive graphical charts to help technical recruiters view insights and optimize hiring strategies.
  • Technologies used: Python, LangChain, CrewAI, Azure OpenAI, React JS, FastAPI