Dr. Sheikh Shah Mohammad Motiur Rahman

The AI Philosophy

I am an interdisciplinary AI researcher and MLOps engineer focused on the development and deployment of Controllable Artificial Intelligence. My core philosophy is that building trustworthy AI systems requires both rigorous mathematical discovery and enterprise-grade software engineering.

Recognized with an MSCA Seal of Excellence and backed by a strong publication record, I translate theoretical research into highly performant, scalable technologies using Python, and PyTorch.

Engineering Case Studies

Enterprise SEM Denoising

The Problem: High-resolution microscopy generates massive noise hindering 3D reconstruction.

The Innovation: Engineered a two-stage adaptive deep learning framework, outperforming SOTA benchmarks.

Stack: PyTorch, Python, Deep Learning

Neuro-Symbolic Compilation

The Problem: Deep learning models fail silently on out-of-distribution data in critical settings.

The Innovation: Developing DeepSymbolica to discover causal, physical invariants through symbolic regression.

Stack: PyTorch 2.0, Controllable AI

Cross-Lab Neuro-Pipelines

The Problem: Fragmented lab data silos prevent scalable, collaborative AI analysis.

The Innovation: Architecting automated, CI/CD-driven ingestion and processing pipelines for unified datasets.

Stack: MLflow, FastAPI, Docker, AWS

Legacy System Modernization

The Problem: Monolithic, legacy MATLAB database systems create severe local compute bottlenecks when processing large-scale mathematical calculations.

The Innovation: Led the architectural transition to a modern client-server model, offloading high-throughput calculations to remote server-side processing.

Stack: System Architecture, MATLAB, Client-Server

Professional Summary

MLOps Engineer and AI Researcher with 7+ years of experience bridging deep learning innovation and full-lifecycle software development. Expertise on foundational machine learning and artificial intelligence (AI), with deep learning specializations in image processing, multi-task learning, trustworthy and controllable AI architectures, and the deployment of robust automated systems. Recognized with an MSCA Seal of Excellence and backed by a strong publication record in top-tier journals, I have a demonstrated ability to translate AI research into highly performant, scalable technologies. Currently specialized in architecting trustworthy, neuro-symbolic AI systems and deploying automated data pipelines using Python and PyTorch.

Skills & Competencies

AI & Machine Learning: Deep Learning, Controllable AI, Explainable AI, Image Processing, Neuro-symbolic AI

Programming: Python, JavaScript, PHP, MATLAB

MLOps & Orchestration: MLflow, Prefect, MLServer, Docker, CI/CD Automation

Validation & Monitoring: Great Expectations, Deepchecks, Evidently AI

Tools & Infrastructure: PyTorch, FastAPI, Streamlit, MongoDB, Git, AWS, DigitalOcean

Leadership: Agile/Scrum Methodologies, Cross-Functional Team Management, Project Lifecycle Execution

Technical Experience

Postdoctoral Fellow

Université de Montréal (Department of Neurosciences) | Montreal, QC, Canada

Jan 2025 - Present

Architect and deploy automated, Python-based data pipelines to process complex neuro-signals recorded across multiple labs into unified, analysis-ready datasets.
Design and implement a centralized data storage and management framework to scale collaborative, cross-lab Neuro-AI analyses.
Led the architectural transition of a legacy MATLAB database system to modern client-server architectures, implementing remote server-side processing for high-throughput calculations.

Ph.D. Researcher

University Bourgogne Franche-Comté (Femto-ST Institute) | Besançon, France

Oct 2021 - Sept 2024

Engineered an end-to-end deep learning pipeline for blind denoising of Scanning Electron Microscope (SEM) images, enabling rapid 3D reconstruction of microstructures.
Developed a novel two-stage adaptive noise estimation framework, significantly outperforming state-of-the-art benchmarks on electron microscopy datasets.
Published first-author methodologies in top-tier peer-reviewed journals including Ultramicroscopy and Engineering Applications of AI.

Lecturer (Senior Scale)

Daffodil International University | Dhaka, Bangladesh

Sep 2017 - Aug 2020

Instructed advanced undergraduate courses focusing on Software Engineering, Object-Oriented Development, and Numerical Analysis using Python.
Supervised multiple undergraduate theses with a strict focus on Machine Learning applications and cybersecurity frameworks.
Mentored junior technical talent by organizing student-led research initiatives, technical hackathons, and research methodology workshops.

Technical Team Lead & Consultant

Tekno Pole & AmTech Software Solutions | Dhaka, BD & Hyderabad, IN

Sep 2015 - Feb 2021

Managed cross-functional engineering teams of 5+ developers, implementing Agile/SCRUM workflows to deliver scalable mobile and web applications.
Provided high-level software architecture consultancy and led remote teams for long-term enterprise project maintenance.
Developed robust proofs-of-concept (PoC) and managed automated cloud deployments via DigitalOcean.

Education

Ph.D. in Computer Science (Deep Learning) - University Bourgogne Franche-Comté, France

Oct 2021 - Sep 2024

M.Sc. in Internet of Things (IoT) - University Bourgogne Franche-Comté, France

Sep 2020 - Aug 2021

M.Sc. in Computer Science & Engineering - Jahangirnagar University, Bangladesh

May 2017 - Apr 2018

B.Sc. in Software Engineering - Daffodil International University, Bangladesh

Jan 2012 - Dec 2015

Service & Leadership

Academic Reviewer & Committee Member (2020 - Present)

High-Volume Reviewer (50+ Papers): Trusted peer-reviewer for top-tier Elsevier and Springer journals, including Engineering Applications of Artificial Intelligence, Computers & Electrical Engineering, Expert Systems with Applications and IEEE Transactions on Image Processing.
Technical Program Committee: Evaluated submissions for major international conferences including IJCNN, ICMLC, and CoCoNet.

Technical Leadership & Affiliations (2014 - Present)

IEEE Senior Member: Active member of IEEE Computer Society & Signal Processing Society.
Ambassador: IEEEXtreme Programming Competition (Jul 2022 - Apr 2023).
Conference Organizer: Intl. Conference on Cyber Security and Computer Science (ICONCS 2020).
Champion: The National Hackathon, ICT Division - Bangladesh (2014).

Awards & Certifications

Awards

MSCA Seal of Excellence (2025): European Commission (Joint project with TU Dublin).
IVADO Postdoctoral Fellowship (2024): Université de Montréal.
EIPHI-BFC PhD Fellowship - Top Ranker (2021): Bourgogne Franche-Comté Region.
Award of Incoming Mobility Grant (Sep 2020): UBFC International Masters.

Certifications

MLOps Upskilling Program: Université de Montréal & IVADO (Feb 2026).
Azure AI Fundamentals: Microsoft (Apr 2024).
AI Fundamentals: DataCamp (May 2024).

Selected Publications

Rahman, S. S. M. M., M. Salomon, and S. Dembélé, "A novel adaptive noise model selection framework for blind denoising of scanning electron microscopy images," Engineering Applications of Artificial Intelligence, vol. 154, p. 110871, 2025.
Rahman, S. S. M. M., M. Salomon, and S. Dembélé, "Estimatenoisesem: A novel framework for deep learning based noise estimation of scanning electron microscopy images," Ultramicroscopy, p. 114192, 2025.
Rahman, S. S. M. M., Z. Chen, A. Lalande, et al., "Automatic classification of patients with myocardial infarction or myocarditis based only on clinical data: A quick response," Plos one, vol. 18, no. 5, 20285165, 2023.

* Full list of 30+ peer-reviewed publications available on Google Scholar.

Sheikh Shah Mohammad Motiur Rahman, Ph.D.

MLOps Engineer and AI Researcher