Nafiz Imtiaz | Montclair University | USA

Researcher and applied data scientist specializing in Educational Data Mining (EDM), learning analytics, privacy-preserving early-support analytics, and fairness-aware machine learning. His research focuses ondeveloping open-source, educator-facing tools, including interoperable student-data systems, predictive screening frameworks for academic, engagement, and disability-related support, and transparent Early Warning Systems (EWS). Published researcher with a Best Paper Award at the IEEE CS BDC Symposium 2024 and peer-reviewed publications in education and business analytics. Master of Science in Business Analytics (GPA 3.91), Montclair State University.

Educational Background

Studied in :

• MS in Business Analytics, Montclair State University, Montclair, NY, USA.
• BBA in Management Information System (MIS), North South University, Dhaka, Bangladesh

Work Experience

Worked in Various Jobs :

Business Analyst

Bits and Binaries, Inc, Irving, TX - February 2026 – Present

Business Analyst

DCITM LLC, Richmond, VA - October 2025 – February 2026

Consultant Business Analyst

NetCom Learning Global, Manhattan, NY - February 2025 – October 2025

Project Research Assistant

BacBon Limited, Dhaka, Bangladesh - January 2019 – March 2022

Leadership Experiences

LAMP (Learn, Act & Make Program)

Tokyo, Japan February 2019

• Served as a LAMP young leader, working with other participants to solve socio-economic issues,

including market penetration and customer satisfaction challenges for entrepreneurs.

• Represented Bangladesh in presentations with social entrepreneurs and participated in volunteer

activities.

• Engaged with policy experts to learn and develop a forward-thinking mindset to come up with unique

solutions for different problems.

Academic Projects

Simulation-Based Adaptive Learning Analytics (ALA) Framework

Montclair State University, Feliciano School of Business, Montclair, NJ April 2025

• Developed and documented a simulation-based ALA framework using secondary datasets and

computational modelling of community-college enrolment scenarios in collaboration with Illinois

State University, Normal, IL

• Applied predictive modelling (logistic regression, random forest, gradient boosting) to forecast

student completion, retention, and workforce readiness.

• Simulation projections showed adaptive pathways could increase program completion by 18%, raise

retention by 15%, and improve workforce-readiness alignment by 12% compared to static curricula.

• Demonstrated that embedding adaptive analytics into existing LMS infrastructure enables early

identification of at-risk learners without costly curriculum redesign.

Conferences & Awards

IEE CS BDC Symposium 2024 (Best Paper Award)

Dhaka, Bangladesh November 2024

• Optimizing Stroke Prediction Models for Real-World Application: Tackling Data Imbalance Without

Synthetic Samples

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