Md Moniruzzaman, Ph.D.

- My current research focuses on AI (e.g., Large Language Models (LLMs)) and Blockchain, with a particular emphasis on their applications in the Cybersecurity of Autonomous Vehicles. - I am actively involved in developing a decentralized intelligent and secured framework using Quantum Machine Learning (QML) and Quantum-safe protocols. - I teach a broad range of courses related to Computer Science and Cybersecurity.

Teach various computer science courses.

- Developing an intelligent peer-to-peer transaction system using reinforcement learning (RL), Transfer learning. - Creating a Blockchain consensus protocol for fast transactions using Game theory and optimization. - Implementing a peer-to-peer trading model based on the leading blockchain platforms such as Ethereum, Avalanche. - Modeling federation of decentralized learners considering Reinforcement Learning as their base model.

Exploring novel solution approaches for Peer-to-peer trading strategies by using Quantum Machine Learning and Quantum-Safe Blockchain

- Taught a range of computer programming and data science courses.

Exploring novel solution approaches for Electric Vehicle (EV) energy trading with diverse energy consumers/prosumers by utilizing AI and Blockchain.

• Develop, design, and automate new reports for customers using IBM Cognos analysis systems. • Develop ETL process for the data warehouse for storing company data collected from customers all over the Asia Pacific region. • Design for SQL data ingestion by using Microsoft SSIS data package to build data cubes created from the warehouse for further data analysis. • Perform research on data to prepare actionable insights for improving business productivity. • Hands-on working experience on the Microsoft Azure Platform and its data tools such as Synapse, and Power BI.

• Analyze subscribers’ geographic location and visualize traffic trends to improve the number of active customers for the telecom operator. • Develop a Web-based Tool for area-based customer registration in different parts of the network to find a pattern for normal and busy hours. Recommend traffic routing for busy hours. • Develop a data warehouse for gathering Call Details Record (CDR) data from all the subscribers in the telecom operator. Then, this is used for network fault detection. • Research industry trends based on data collection methods and analytical strategies and used the information for improved Key Performance Indicators (KPI). • Dashboard automation for daily key performance index such as the number of calls, and registration customers, for the telecom core network. • Technical expertise in telecom core network protocols and topologies such as, Next Generation Networks (NGN), IP Multimedia Subsystem (IMS), and Voice-Over-Long-Term Evolution (VoLTE).