Skip to content
Mufti Mahmud

Mufti Mahmud

Senior Lecturer

School of Science & Technology


Dr Mufti Mahmud is a (Senior) Lecturer of Computing and Technology at the School of Science and Technology. Dr Mahmud leads the MSc in Data Analytics for Business and the Data Analytics strand of the Online MBA courses. Dr Mahmud is a member of the NTU Distance Learning Governance, Operation and Steering committee as well as the International Mobility committee.

Dr Mahmud is also serving as the coordinator of the local organising committee chair for the IEEE World Congress on Computational Intelligence (IEEE WCCI) 2020 to be held in Glasgow, UK from 19 to 24 July 2020.

Dr Mahmud is responsible for the following modules:

  • Big Data and its Infrastructures (Postgraduate – on campus)
  • Big Data and its Infrastructures (Postgraduate – online delivery in collaboration with John Wiley & Sons Limited)
  • Practical Machine Learning Methods for Data Mining (Postgraduate – online delivery in collaboration with John Wiley & Sons Limited)
  • Data Analysis (Undergraduate)

Dr Mahmud is also involved in teaching the following modules:

  • Foundations of Computing & Technology (Undergraduate)
  • Information & Database Engineering (Undergraduate)
  • Practical Project Management & Professional Development (Undergraduate)
  • System Analysis & Design with Professional Development (Undergraduate)

Also, Dr Mahmud previously taught the following:

  • Neural Signal Processing, Scientific Computing using MATLAB at Postgraduate level;
  • Software Engineering, Software Design and Development, Object-Oriented Programming, Structured Computer Programming with C/C++, Professional Programming with C#.NET, Data Structures, Computing Systems, Computer Graphics, Computer Networking, Data & Tele Communication at Undergraduate level; and
  • Developing Windows Applications using Microsoft .NET Platform (C#), Developing Web Applications using Microsoft .NET Platform (ASP.NET & C#), and Web Database Programming using SQL Server 2000 at a professional level.

Dr Mahmud’s research vision is to contribute towards a secure, smart, healthy and better world to live in. In today's digitized world, converting the ever-expanding amount of raw data to smart data, and building predictive, secure and adaptive systems aiming personalized services are essential and challenging, which require cross-disciplinary and multi-stakeholder collaborations. Towards these goals, Dr Mahmud conducts problem-driven `Brain Informatics' research where he works with problem domain experts to find multidisciplinary solutions to real-world problems. Dr Mahmud’s research involves Computational-, Health- and Social- sciences, and uses Neuroscience, Healthcare, Applied Data Science, Computational Neuroscience, Big Data Analytics, Cyber Security, Machine Learning, Cloud Computing, and Software Engineering; and plans to develop secure computational tools to advance healthcare access in low-resource settings.

For more details, please visit personal profile.

Career overview

Prior to joining NTU Dr. Mahmud served as:

Research areas

Doctoral students

  • Marcos Ignacio Fabietti (main supervisor with Prof. A. Lotfi as co-supervisor), topic: Analysis of Healthcare Big Data using Machine Learning for Disease Monitoring and Management;
  • Oluwatamilore O. Orojo (main supervisor with Dr J. Teppar and Prof. M. McGinnity as co-supervisors), topic: Computational Architectures for Extracting Intelligence from Unstructured Data for Healthcare Applications;
  • Salisu W. Yahaya (Co-supervisor with Prof. A. Lotfi as main supervisor), topic: User-Centric Anomaly Detection in Activities of Daily Living.

Prospective students may obtain detailed information on opportunities to carry out postgraduate research towards an MPhil / PhD from the NTU Doctoral School.

Research areas

Dr Mahmud’s research expertise and interest revolve around the following:

  • Advanced Machine Learning for Biological Data Analysis: Recent research in Deep and Reinforcement Learning, and their combination promise to revolutionize Artificial Intelligence. And, multimodal data from various application domains (e.g., Omics, Bioimaging, Medical Imaging, and [Brain/ Body]-Machine Interfaces) are piling up which require novel data-intensive machine learning techniques. In this project, we have been applying DL based methods to analyse data coming from different biological sources.
  • Advanced Machine Learning for Crowd Analysis: This project aims in developing cutting-edge tools for multisource crowd data analysis to develop a real-time automated transportation system for smart city applications.
  • Distributed Biosignal Analysis Framework: With the rapidly growing brain data, scientists require automated and intelligent tools to infer meaningful conclusions from them which, for a normal desktop PC, is becoming increasingly difficult. This project aims to harness the powers of distributed and cloud computing for the job.
  • Early Detection of Brain Network Dysfunction in Alzheimer’s Disease (AD) & Mild Cognitive Impairment (MCI): AD & MCI are characterized by altered brain network activity which is currently detected at a mature stage. In this project, we aim to characterize a stable biomarker detectable at the disease onset to facilitate their early diagnosis and treatment. Sophisticated frequency-based analyses, of in vivo LFP from genetically modified mouse models at different pathological stages, are applied to detect network dysfunctions.
  • High-Resolution Brain-Chip Interfacing: This project aims to develop high-resolution CMOS based neuronal probes to acquire neuronal signals at (sub-)cellular resolution (7.5-100 micron) across different brain structures.
  • Neuroinformatics Tools for Extracellular Neural Signal Processing & Analysis: Automated analysis of multi-channel neural data has always been a huge challenge, especially when they are recorded in a low SNR setup. Under this umbrella project, we develop novel tools for automated analysis of multi-channel brain signals.
  • Neuroprosthetics & Rehabilitation Engineering: The aim of this project is to reduce rehabilitation costs and to provide independent mobility to the severely disabled through automatic and smart assistive devices controllable with bio-signals (e.g., EEG and EMG). These signals are mainly triggered by imagination, physical activities, and even just by looking at the way to follow. Also, fuzzy-based controllers are optimized to operate with minimal numbers of channels.
  • Secure Cyber-Physical Systems and Internet of Things: Rapid popularity of Internet of Things (IoT) and cloud computing permits neuroscientists to collect multilevel and multichannel healthcare data to better assess health conditions, diagnose diseases, and devise treatments. This project aims to ensure secure and reliable data communication between end-to-end devices supported by current IoT and cloud infrastructure through trust management as well as the development of novel secure IoHT frameworks.
  • Understanding Neural & Neuromuscular Behaviour through Modelling: To design appropriate therapy and realistic assistive devices for neurodegenerative disorders we need to understand in detail the neural transmission mechanisms. In this project, we develop detailed models of neural networks suitable for electroceutical therapy and neuromuscular junction mimicking the neurobiological phenomena.
  • Wireless Sensor Network for Healthcare Application: With the rise of service automation, access to digitized data, and growing network speed, this project aims to apply wireless sensor network to provide e-healthcare solutions.

External activity

Dr Mahmud’s external activities include discharging editorial responsibilities for prominent academic journals and organising important scientific conferences.

Dr Mahmud is:

Sponsors and collaborators

Since 2008, Dr Mahmud has been funded largely by the European Commission through the following projects:

  • Ramp (Euro 362k through FP7, ICT-2013.9.6 FET Proactive, 612058): A new biohybrid architecture of natural and artificial neurons endowed with plasticity properties was developed. Communication between artificial and natural worlds was established through new nano- and micro transducers to directly interface network of neurons in culture to an artificial CMOS-based counterpart.
  • NeuroAct (Euro 948k through FP7, PEOPLE-2011-IAPP Marie-Curie Action, 286403): Novel tools and platforms were developed to advance current understanding of the molecular and microcircuit bases for nervous system (dys)function and perform high-throughput in-vitro drug screening.
  • Realnet (Euro 643k through FP7, ICT-2009.8.8 FET Proactive, 270434): Specific imaging techniques to record from multiple neurons in the cerebellar network was developed. From the data, realistic real-time model of the cerebellum was obtained and connected to robotic systems under closed-loop conditions. Using "adaptable filter theory", sensorimotor control and cognitive systems were investigated.
  • CyberRat (Euro 372k through FP7, ICT-2007.8.3 Bio-ICT convergence, 216528): An innovative brain-chip interface, with CMOS chip featuring a large-scale matrix of stimulation and recording microelectrodes integrated at high-spatial-density (~1000 sites at <10-micron separation), was developed. I was in charge of processing and analysis of the data recorded from these chips.
  • Euro ~57k from UNIPD, IT for the `Development of novel software tools to study neuronal populations Activity recorded using high-resolution multi-site neuronal probes' (03/2015-02/2017).
  • Euro ~39k from UNIPD, IT for the `Use of capacitors and transistors for recording & stimulation of neuronal activity in the cortex and deep nuclei of rat brain' (01/2011-12/2012).
  • Euro ~67k from Fondazione CARIPARO for `Developing novel neuronal signal processing and analysis tools' (01/2008-12/2010).

Dr Mahmud’s external collaborators include:

  • Prof. Stefano Vassanelli from University of Padova, Italy on the development of novel high-resolution brain-chip interfacing and smart neuroengineering systems for distributed artificial intelligence
  • Prof. Cristina Fasolato from the University of Padova, Italy on characterization of a stable biomarker for early detection of Alzheimer’s disease.
  • Prof. Roland Thewes from the Technical University of Berlin, Germany on the development of microelectronic devices.
  • Prof. Amir Hussain from the University of Stirling, UK on the development of intelligent signal analysis tools for [e/m]Health applications.
  • Prof. Michele Giugliano from the University of Antwerp, Belgium on the development of novel cloud-based neuroinformatics tools.
  • Prof. M. Shamim Kaiser from IIT, Jahangirnagar University, Bangladesh on the development of low cost and wireless assistive/rehabilitation devices.


M. Asif-Ur-Rahman, F. Afsana, M. Mahmud*, M.S. Kaiser*, M.R. Ahmed, O. Kaiwartya, A. James-Taylor. (2018). Towards a Heterogeneous Mist, Fog, and Cloud based Framework for the Internet of Healthcare Things. IEEE Internet Things J. Doi: 10.1109/JIOT.2018.2876088 [* Co-senior authors, in press.]

A. Aliyu, A.H. Abdullah, N. Aslam, A. Altameem, R.Z. Radzi, R. Kharel, M. Mahmud, S. Prakash, U.M. Joda. (2018). Interference-aware Multipath Video Streaming in Vehicular Environments. IEEE Access, Vol. 6, pp. 47610-47626. Doi: 10.1109/ACCESS.2018.2854784

M. Mahmud*, M.S. Kaiser*, M.M. Rahman, M.A. Rahman, A.   Shabut, S. Al Mamun, A. Hussain. (2018). A Brain-Inspired Trust Management   Model to Assure Security in a Cloud based IoT Framework for Neuroscience Applications. Cogn. Comput., Vol. 10, No. 5, pp. 864–873 . Doi: 10.1007/s12559-018-9543-3 [*: equal contributors.]

M. Mahmud*, M.S. Kaiser*, A. Hussain, S. Vassanelli.   (2018). Applications of Deep Learning and Reinforcement Learning to   Biological Data. IEEE Trans.   Neural Netw. Learn. Syst., Vol. 29, No. 6, pp. 2063 - 2079. Doi: 10.1109/TNNLS.2018.2790388 [*: equal contributors.]

F. Afsana, M.A. Rahman, M.R. Ahmed, M. Mahmud*, M.S.   Kaiser*. (2018). An Energy Conserving Routing Scheme for Wireless Body Sensor Nanonetwork Communication. IEEE   Access, Vol. 6, pp. 9186-9200. Doi: 10.1109/ACCESS.2018.2789437 [*: Co-senior   authors.]

M.S. Kaiser, K. Lwin, M. Mahmud, D. Hajializadeh, T.   Chaipimonplin, A. Sarhan, M. A. Hossain. (2018). Advances in Crowd Analysis for Urban Applications through Urban Event Detection. IEEE Trans. Intell. Transp. Syst., Vol. 19, No. 10, pp. 3092 - 3112. Doi: 10.1109/TITS.2017.2771746

R. Fontana, M.   Agostini, E. Murana, M. Mahmud, M. Rubega, G. Sparacino, S. Vassanelli, C.   Fasolato. (2017). Early Hippocampal Hyperexcitability in PS2APP Mice: Role of Mutant PS2 and AP. Neurobiol Aging, Vol.   50, pp. 64-76. Doi: 10.1016/j.neurobiolaging.2016.10.027.

S. Vassanelli, M. Mahmud. (2016). Trends and Challenges in Neuroengineering: Towards ‘Intelligent’ Neuroprostheses through Brain-‘Brain Inspired Systems’ Communication. Front. Neurosci., Vol. 10, art.   no. 438. Doi: 10.3389/fnins.2016.00438.

M. Mahmud, S. Vassanelli. (2016). Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-art and Challenges. Front. Neurosci., Vol.10, art. no. 248. Doi10.3389/fnins.2016.00248.

M.S. Kaiser, Z. Chowdhury, S. Al Mamun, A. Hussain, M.   Mahmud. (2016). A Neuro-Fuzzy Control System Based on Feature Extraction of Surface Electromyogram Signal for Solar-Powered Wheelchair. Cogn. Comput., Vol. 8, No. 5, pp. 946-954. Doi: 10.1007/s12559-016-9398-4.

M. Mahmud, R. Pulizzi,   E. Vasilaki, M. Giugliano. (2014). QSpike Tools: a Generic Framework for Parallel Batch Preprocessing of Extracellular Neuronal Signals Recorded by Substrate Microelectrode Arrays. Front. Neuroinfor., Vol. 8, art. no. 26. Doi: 10.3389/fninf.2014.00026.

M. Mahmud, A. Bertoldo, et al. (2012). SigMate: A Matlab–Based Automated Tool for Extracellular Neuronal Signal Processing and Analysis. J. Neurosci. Methods, Vol.   207, No. 1, pp. 97-112. Doi: 10.1016/j.jneumeth.2012.03.009.

See all of Mufti Mahmud's publications...