Md Nur Al Ahad

Developed syllabi for AI in Business, Data Mining, and Data Warehousing to align with industry demands. Increased student engagement in Python programming and business statistics and mentored students through internships, research projects, and journal publishing.

Analyzed large pension datasets using advanced statistical methods to improve data reliability and depth of analysis. Engineered and refined machine learning models for pension maturity prediction.

Analyzed 10,000+ patient health records using advanced statistical methods to improve data reliability. Refined machine learning models for cancer data to improve predictive accuracy for patient outcomes.

Analyzed 200,000+ sales data points using machine learning algorithms to improve sales forecasting accuracy. Developed an AI-based application for detecting counterfeit products to enhance brand protection accuracy.

Developed and deployed a deep learning speech-to-text recognition system. Built NLP capabilities for translation, developed neural network models for image similarity search, and created statistical machine learning algorithms to provide actionable insights.

Developed NLP algorithms to extract key information from 100+ research reports to produce a comprehensive EV industry report. Analyzed IoT sensor sound data to improve machine status predictions, built models to predict business success rates, and supported AI-based quality control using image processing.

Taught operations research, mathematical finance, Python programming, and econometrics among other subjects. Supported instruction across diverse coursework using applied quantitative methods.

Taught finance, economics, and management courses and incorporated Industry 4.0 and business analytics into course content. Worked with faculty to align assessment methods for business statistics and marketing management.

Conducted finance and business research using IBM SPSS for statistical analysis. Supported faculty with data processing and interpretation to improve the accuracy and clarity of research outputs.

MSc in Actuarial Science with Business Analytics at Bayes Business School (2023–present). Modules include modelling & data analysis, machine learning, and applied NLP; project focuses on predicting pension commutation decisions using machine learning.

In-progress Certified Artificial Intelligence Professional (CAIP) credential from GAQM (UK).

Certified Machine Learning Expert (CMLE) credential with Global Tech Council (USA) for 2025–2026.

Certified Blockchain Expert (CBE) credential with the Blockchain Council (USA) for 2025–2026.

Grade: GPA 3.07 on a Japanese scale

Master of Engineering at Toyohashi University of Technology (2018–2020), with a research area in Financial Engineering. Thesis: investment in the Japanese aviation industry, covering uncertainty, competition, and learning.

Grade: CGPA 3.588 on a 4.0 scale (US scale)

MBA at Universiti Putra Malaysia (2014–2015) with a major in Finance. Project: effect of bond issuance on firm performance (empirical study on listed Malaysian companies).

Grade: CGPA 3.68 on a 4.0 scale

BBA (Hons.) at the University of Dhaka (2008–2012), majoring in Marketing. Scholarship received: Comilla Board (Talent-pool category).

Grade: GPA 5.0 on a 5.0 scale

Higher Secondary Certificate (HSC) at Noakhali Govt. College (2007), Business Studies group. Scholarship received: Comilla Board (Talent-pool category).

Grade: GPA 5.0 on a 5.0 scale

Secondary School Certificate (SSC) at Noakhali Zilla School (2005), Business Studies group. Scholarship received: Comilla Board (Talent-pool category).