Md. Salauddin
Sarker
System Architect & AI Researcher
Researching
Building reliable, interpretable ML for real-world decisions — with a focus on Explainable AI (XAI) and Time-Series Forecasting.
Affiliation
Dept. of EdTE — UFTB, Bangladesh
ORCID
0009-0005-7907-7522-
GPA 3.84 / 4.00
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Output Published Research
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Domain Industrial Analytics
Open To
Thesis-Track Supervision
XAI · Time-Series
Two Times
Dean's Awardee
Academic Distinction
"The complexity of industrial data requires the precision of neural interpretation."
Scholarship
Aspiration.
Specializing in Trustworthy Data Science—architecting machine learning systems that maintain interpretability and robustness under real-world industrial constraints.
Explainable AI (XAI)
Developing faithful, actionable explanations for high-stakes decision makers using SHAP, LIME, and Grad-CAM.
Dynamic Forecasting
Evaluating neural resilience across non-stationary longitudinal data and multi-region normalizations.
Strategic AI Roadmap
Future Trajectories (2026-2030)
Knowledge-Grounded
LLMs
Integrating retrieval-augmented generation (RAG) with explainable reasoning to reduce hallucination in enterprise environments.
Recursive Agentic
Reasoning
Developing autonomous agents capable of self-correcting reasoning loops for complex multi-step industrial automation.
Quantization-Aware
Edge AI
Optimizing deep models for low-power edge deployment in agri-tech and healthcare without sacrificing interpretability.
Academic Pedigree
Educational Foundation
B.Sc. in Educational Technology & Engineering (EdTE)
University of Frontier Technology (UFTB), Bangladesh
CGPA: 3.84 out of 4.00
2019 — 2024
Specialized Coursework
Higher Secondary (Science)
Dhaka College
GPA: 5.00 out of 5.00
Secondary School Certificate
SHKSC
GPA: 5.00 out of 5.00
Research-Relevant Experience
Data Engineering & Operation Analytics
Software Developer / Data Systems
Q Cosmetics Ltd
January 2025 – Present
Operational Impact
- → Rolled out multi-module ERP workflows, reducing order cycle time by 35%.
- → Built Python–PostgreSQL ETL pipelines, improving audit consistency and data quality.
Analytics & BI
- → Built KPI dashboards (Metabase/Tableau), reducing monthly reporting from 3 hours to 15 minutes.
- → Structured master data and reconciliation logic for reliable downstream analytics.
Technical Matrix.
Multi-Disciplinary Stack & Scientific Toolkit
- Python
- SQL
- Java
- PHP
- Dart
- Linux / Shell
- PyTorch / TF
- SHAP / LIME
- Grad-CAM
- ARIMA / OLS
- Scikit-Learn
- Computer Vision
- PostgreSQL
- Odoo (ERP)
- ETL Pipelines
- Tableau
- Metabase
- Data Warehousing
Awards & Honors
Dean’s Award (2022, 2024)
University of Frontier Technology (UFTB)
Academic excellence for two non-consecutive cycles for top-tier GPA.
National STEAM Olympiad 2023
9th Place (National) • Campus Ambassador
Selected as campus leader to coordinate innovation drive; ranked top 10 nationally for technical pitch.
Academic Leadership
UFTB STEAM Club
Vice President
Facilitating technical mentorship for 200+ members and orchestrating high-fidelity engineering hackathons.
UFTB Robotics Club
Executive Member
Orchestrating large-scale robotics competitions and facilitating cross-discipline engineering hackathons.
UFTB Language Club
Japanese Language Secretary
Facilitating multi-lingual research communication and fostering global academic exchange.
Research Profile
Research
Publications.
Selected journal, conference, and manuscript outputs arranged for quick judgment on venue quality, indexing strength, and research fit.
Built to be understood in one supervisor scan.
Venue strength, indexing, and research fit are surfaced first. Methods and abstract stay secondary.
Best venue
Q1 journal in Artificial Intelligence
Artificial Intelligence Review under Springer Nature
Indexed reach
SCIE, Scopus, and IEEE Xplore exposure
Journal article, conference proceeding, and manuscript-stage work
Research fit
XAI, forecasting, edge AI, educational data mining
Structured for faculty review before reading abstracts
Top Indexed Venue
Q1 Journal
Artificial Intelligence Review under Springer Nature
Publication Mix
1 Journal + 1 IEEE Paper
2 additional manuscript-stage studies
Indexing Footprint
SCIE, Scopus, IEEE Xplore
Visible access path and venue class on every card
Professor Readout
Metrics First
Venue, ranking, and access appear before the paper details
Artificial Intelligence Review
Open-access review journal in Artificial Intelligence and cognitive science.
Leveraging explainable AI for sustainable agriculture: a comprehensive review of recent advances
Paper Type
Systematic review
Evidence Base
150+ papers synthesized
Supervisor Fit
Trustworthy AI and XAI supervision
Why It Stands Out
Novel 4-tier XAI taxonomy. Distilled from 150+ papers to evaluate local explanations such as LIME, SHAP, and Grad-CAM in precision agriculture systems.
Method Snapshot
Systematic mapping of 150+ papers; specialized 4-tier taxonomy for post-hoc local explanations in agri-systems.
Core XAI Tools
Evaluation of LIME, SHAP, and Grad-CAM for transparent decision-making in precision agriculture.
Artificial Intelligence (AI) has emerged as a transformative force across various industries, including agriculture, where it is being adopted to enhance productivity, efficiency, and sustainability. However, a significant challenge arises from the lack of transparency in AI models, which can undermine their dependability, particularly in critical fields like agriculture and medicine. This review explores recent advances in leveraging Explainable AI (XAI) to bridge the gap between model complexity and human trust.
IEEE 2nd International Conference on Computing, Applications and Systems
International conference proceedings paper with full-paper publication access.
Bridging Climates: Weather Forecasting Using OLS and ARIMA Models with a Multi-Country Dataset
Arafat Hossain, Md. Salauddin Sarker, Aditya Rajbongshi, Most. Rakiba Khanom Jisa, Maria Afrin Bindu, Kayes Mohammad Abdullah
Key Contribution
Conducted a 61-year multi-country clinical analysis of climate data, proving OLS outperforms ARIMA (RMSE 0.30) for baseline long-term predictions in Bangladesh.
Modeling Approach
OLS + ARIMA hybrid forecasting on a 61-year multi-country climate dataset (Bangladesh, Saudi Arabia, Japan, Russia).
Research Impact
OLS outperformed ARIMA for Bangladesh (RMSE 0.30, MAE 0.25); accurate long-term weather prediction via time-series analytics.
Industrial and deployment-focused AI forum submission
Specialized track manuscript positioned for industrial edge inference discussion.
EdgeVision: Quantization-Aware Inference for Industrial Edge
Key Contribution
Developed an 8-bit INT quantization approach for ARM edge nodes achieving sub-10ms inference latency targeting industrial vision.
Optimization Focus
8-bit INT quantization on ARM platforms; Sub-10ms inference for low-power nodes.
Research Position
Featured as High-Potential Research at the IDAA 2025 Scientific Forum.
Applied educational AI manuscript with explainability layer
Research-stage paper focused on retake prediction and interpretable student-risk modeling.
Bridging Educational Gaps: Predicting Retakes Among Bangladeshi Undergraduates with Machine Learning and Explainable AI
Nusrat Zahan Nila, Md. Ali Mahmud Pritom, Fatema Tuz Johora, Aditya Rajbongshi, Md. Salauddin Sarker, Md. Ashrafuzzaman
Key Contribution
Built an early course-retake prediction framework for Bangladeshi undergraduates using a soft voting classifier with SHAP and LIME, reaching 94.74% accuracy on a 474-student dataset.
Predictive Framework
474 real student records, extensive preprocessing, and baseline comparison across LR, GNB, RF, SVC, Perceptron, KNN, and a proposed soft voting ensemble.
XAI Insights
SHAP and LIME exposed the factors driving retake risk, including lack of preparation, family issues, poor time management, and related academic pressures.
Technical Validation.
Academic Certifications & Professional Recognition
Research Case Studies
Evidence of Technical Readiness & Scientific Method
Rice Leaf Disease Detection
Building an accurate and interpretable classifier for sustainable agri-diagnostics.
Need for reliable, transparent identification of leaf pathogens in field conditions.
Multi-class leaf image dataset; diverse environmental backgrounds.
Approach: Transfer learning (VGG16/ResNet50/EfficientNetB0) + attention mechanisms.
Explainability: Grad-CAM heatmaps for decision transparency and feature verification.
Results: 99.42% accuracy; sustained 42 FPS on mobile edge platforms.
RESEARCH IMPACT (MSc READY)
Demonstrated that spatial attention maps can pinpoint pathogenic regions in leaf images better than standard CNNs, crucial for trustworthy field diagnostic deployment.
Bridging Climates: Multi-Country Weather Forecasting
OLS vs ARIMA evaluation across 61 years of multi-country climatic data (Bangladesh, Saudi Arabia, Japan, Russia).
Forecasting weather variables accurately across geographically diverse regions.
61 years of historical weather data across multiple regions.
Approach: OLS + ARIMA hybridization; comprehensive error diagnostics and baseline comparisons across 4 countries.
Metrics: OLS outperformed ARIMA for Bangladesh (RMSE 0.30, MAE 0.25); demonstrated reliable long-term prediction via time-series analytics.
KEY INSIGHT (LEARNING)
Identified that non-stationarity requires careful time-indexed evaluation and domain-aware baselines rather than just high-complexity architectures.
Industrial Customer Churn Prediction
Bridging operational ERP data with predictive retention strategy.
Predicting organizational churn and identifying key risk drivers in commercial flows.
85% prediction accuracy with feature importance insights for retention.
Approach: Classification models (XGBoost/RandomForest) + SHAP feature importance analysis.
Outcome: Actionable retention strategy insights for operational management.
Academic Endorsements
Farhana Islam
Assistant Professor & Chairman
Dept. of Educational Technology & Engineering
farhana0001@uftb.ac.bd
University of Frontier Technology (UFTB), Bangladesh
Aditya Rajbongshi
Assistant Professor
Dept. of Educational Technology & Engineering
aditya0001@uftb.ac.bd
University of Frontier Technology (UFTB), Bangladesh
Curriculum Vitae
Academic & Professional Summary
Research Direction: Seeking fully funded MSc (Thesis) opportunities in XAI + Time-Series + Reliable ML.
Data Systems: Proven industry experience in ERP Workflows + ETL Pipelines + BI Dashboards.
Scientific Output: Multiple Published Papers across IEEE and Springer venues.
MSc
Candidacy 2026.
I am seeking a fully funded MSc with thesis (Fall 2026) to research Explainable AI, Time-Series Forecasting, and Trustworthy ML.
Get in Touch.
Collaboration & Inquiry
I am always open to discussing new opportunities in Explainable AI, Industrial Automation, and Odoo ERP. Whether you have a question or just want to say hi, I'll try my best to get back to you!
Email Address
md.salauddin.sarker.icte@gmail.comAcademic Referees
Available upon request for MSc applications.
Location
Dhaka, Bangladesh
WhatsApp / Phone
+880 1521-260707
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