Mohammad Alam
 Doctoral Researcher (AI, Machine Learning & Software Engineering) | Faculty of Technology

Department of Computer Science, Software Engineering

mohammad.z.alam@utu.fi



TyöhuoneAgora, 4th floor


ORCID-tunnistehttps://orcid.org/0009-0009-0130-339X





Asiantuntijuusalueet
Generative AI; Machine Learning (Deep and Reinforcement Learning); Natural Language Processing; SNNs; Neuromorphic Computing; RAG; LLMs; Prompt Engineering, Digital Twin.

Tutkimusyhteisö tai tutkimusaihe
LEAF-SNN: Energy-Efficient Spiking Neural Network Language Models for Scalable NLP | Sustainable AI, Neuromorphic Computing, and Bio-inspired Neural Models for Efficient Natural Language Processing.

Biografia

Mohammad Alam is a Doctoral Researcher in Computer Science, Artificial Intelligence, and Machine Learning at the University of Turku, Finland. He previously held appointments as PhD Researcher and Graduate Teaching Assistant at Brunel University London (January 2024–August 2024) before transitioning to continue his doctoral studies at Turku, Finland.

His current research involves international collaborations with the Faculty of Technology, Department of Computing and Data Science at Xiamen University Malaysia, and the Faculty of Engineering Technology, Department of Electrical and Electronic Engineering at Islamic University of Madinah. Alam holds degrees spanning computational sciences, business administration, and social sciences (BSc, MSc, MBA, MPA, BA), with additional specialized training in Data Science and Emerging Technologies. He currently serves as Lead Consultant at Space (BD) Tech Venture and Digital Finance, and has previously led consultancy and capstone projects for the Asian Development Bank (ADB) and UNDP, focusing on climate finance mechanisms and FinTech regulatory frameworks.

He brings 25 years of senior leadership experience from premier corporate banking and financial services institutions across Bangladesh, the United Arab Emirates, and Qatar, including service as Additional Managing Director. His domain expertise encompasses corporate finance structuring, quantitative risk assessment, green finance instruments, sukuk bond issuance, sustainable risk modeling, and decentralized financial framework design.

His research interests focus on FinTech innovation across on-premise, hybrid, and cloud-based banking systems, alongside energy economics and carbon finance. Alam is proficient in Python, R, C++, and Java for data science applications and financial modeling, enabling integration of academic research methodologies with industry-scale implementations.



Tutkimus

Alam's research focuses on Deep Learning and Reinforcement Learning, Natural Language Processing, Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Advanced Prompt Engineering, Spiking Neural Networks, Neuromorphic Computing, and Digital Twin technologies.

His consultancy expertise extends to AI applications in healthcare, digital finance, green bonds, risk modeling, and digital ethics. His scholarly contributions include peer-reviewed publications addressing artificial intelligence, machine learning, natural language processing, RAG systems, and spiking neural networks.



Opetus

Alam served as Graduate Teaching Assistant at Brunel University London (2024–2025), contributing to MSc and BSc modules in Ethics in Digital Systems and AI, Machine Learning, Deep Learning, Reinforcement Learning, and Information-Driven Entrepreneurship. He has also taught at Brit College of Engineering & Technology (BCET), UK, delivering modules in Data Mining and Machine Learning, and Big Data Theory and Practice.

Additionally, he brings over 16 years of adjunct faculty experience from leading higher education institutions in Bangladesh, including the University of Dhaka, BIBM, and East West University. His teaching portfolio spans BSc, BBA, and MBA programs, with responsibilities including curriculum development, student supervision, and delivery in both on-campus and online formats.



Julkaisut


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